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IJAAS

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Molecular techniques applied to investigations of abundance of the ammonia oxidizing bacteria and ammonia oxidizing archaea microorganisms in the environment Amjed Ginawi, Yunjun Yan

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March 2019

ISSN 2252-8814

International Journal of Advances in Applied Sciences (IJAAS) Vol. 8, No. 1, March 2019, pp. 1~7 ISSN: 2252-8814, DOI: 10.11591/ijaas.v8.i1.pp1-7

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Molecular techniques applied to investigations of abundance of the ammonia oxidizing bacteria and ammonia oxidizing archaea microorganisms in the environment Amjed Ginawi1, Yunjun Yan2 1,2

Key Lab of Molecular Biophysics of Ministry of Education, Huazhong University of Science and Technology, China 1Faculty of Marine Science and Fisheries, Red Sea University, Sudan

Article Info

ABSTRACT

Article history:

This review shows regards of the recently experienced concerning the environments of ammonia oxidizing bacteria (AOB), ammonia oxidizing archaea (AOA) microorganisms, and denitrifying microbes. The advancements of molecular biology techniques have encouraged staggeringly to the fast recent developments in the sector. Various methods for implementing so are discussed. The function of AOB, AOA, and denitrifying microorganism composition was investigated through a high throughput of the 16S rRNA amplicon sequencing protocol. There is potential to observe the specific species appearance of these microorganisms in each environment and get to the evaluated relative abundance of several kinds. There is information indicated which the structure of denitrifying and nitrifying group was monitored field to significant fluctuations and the complexes, together in space and in time. More effort is required to enhance and isolate those microorganisms that common of the progressions and to function them through the compound of molecular techniques, biochemical and physiological. However, the investigation with deoxyribonucleic acid (DNA), antibodies, and the polymerase chain reaction (PCR) was preferred mainly to report the composition of chemolithoautotrophic bacteria, surveys of their characteristics in environmental that needed quantification at the transcriptional level is presently not available.

Received Dec 20, 2018 Revised Feb 5, 2019 Accepted Feb 15, 2019 Keywords: Ammonia oxidizing archaea Ammonia oxidizing bacteria Chemolithoautotrophic bacteria Molecular techniques

Corresponding Author: Yunjun Yan, College of Life Science and Technology, Huazhong University of Science and Technology, 1037 Luoyu Rd, Hongshan District, Wuhan City, Hubei Province 430074, P. R, China. Email: yanyunjun@hust.edu.cn

INTRODUCTION Ammonia oxidation is the ratelimiting, and first steps of nitrification have been measured extensively because of its environmental importance in the large-scale nitrogen cycle and ecological implications [1]. Ammonia oxidizing and nitrite oxidizing microbes are a two-step process catalyzed. It is just the process of oxidative biological related to reduced and oxidized rivers of nitrogen in environmental [2]. This essential process in the nitrogen cycle of global was considered restricted to ammonia oxidizing bacteria (AOB) [3]. In diverse ecosystems, this processes counteract environmentally, it was applied to reduce nitrogen values in wastewater treatment plants. Various researches influences the essence of methods: autotrophic nitrification in microorganisms [4]; environmental of denitrification and nitrate/nitrite ammonification [5]; factors controlling denitrification [6]; aspects of denitrification in sediment and soil [7]; investigations for functional genes and phylogenetic of denitrification and nitrification [8]; molecular basis and cell biology of denitrification [9]; enzymology of ammonia oxidation [10]; enzymology Journal homepage: http://iaescore.com/online/index.php/IJAAS

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of the nitrogen cycle [11]; aquaculture and nitrifying bacteria [12]; dissimilatory nitrate reductases in microbe [13]; anaerobic ammonia oxidation [14]; inorganic nitrogen metabolism in bacteria [15]; and nitrogen cycling in aquatic ecosystems [16]. The function of microbial communities in freshwater environments was significant in determining the composition [17], presented the diversity of microbial communities and distribution [18]. The functions are an activated related to microorganism groups, e.g., nitrogen fixation; microorganisms are essential processed and in substance, turnover cycling the stream. As gene function target, the ammonia monooxygenase (amoA) is more functional than the 16S rRNA scheme, for analysis AOA and AOB population pattern [19]. The high concentrations of ammonia in the streams are toxic to fishes and other aquatic living organisms. The reduction of ammonia in the environment is one of the primary functions of (AOA and AOB) and attained by nitrification, AOA support nitrification in different habitats. However, the separate study of AOB or AOA functions in ammonia oxidation causing significant attention and attracted many researchers [20]. Ammonia oxidizing archaea and AOB involves the key enzyme the amoA that is constituted of three amoA, amoB, and the amoC genes encode subunits [21]. In situ ratio determinations in streams environments, even so, the indicated that nitrification was appeared almost universally, even in the many oligotrophic environments with very lowest concentrations of nitrates under the growth threshold of AOB [22, 23]. The environmental experiences were large variations in chemical conditions and several hydrological. Furthermore, The physiochemical characteristic may affect the population of microbes to processes such as: nitrogen cycling [24], NH4+ availability [25], dissolved oxygen (DO) concentrations [26], temperature [27], total nitrogen (TN), total phosphorus (TP) [28], light [29], salinity [30], oxidation-reduction potential (ORP) [31], and sulfide concentrations [32]. Some studies were showed that abundance AOA  AOB phylotypes and distinct AOA  AOB groups had been presented in various aquatic environments (freshwater- marine) both in local estuarine gradients and on a wide geographical scale [33, 34]. Moreover, a few is known about whether AOA  AOB microorganisms are essential nitrifies in estuarine and marine environments. This review is emphasized and highlighted the current achievements in the scope of chemolithoautotrophic bacteria detected through applied approaches of molecular biology, with focus on the natural habitats. There were techniques of engineering such as wastewater treatment plants, oxygen demand, water resources pollutants, and transport of pollutants. 2.

INVESTIGATIONS OF THE COMMUNITIES’ COMPOSITION The caused by their unique requests that are usually unidentified and decreased levels of growth, it is often not simplified to isolate a pure culture of AOB autotrophic microorganisms. The isolate combined with the typically small amount (few than 0.1%), complicated our understandable on the relative abundance and distribution of this microorganism in habitats environmental. However, molecular techniques were appeared to accessible Figure 1.

Figure 1. Molecular techniques model to functional diversity of microbes and characterize the structural in the environments Int. J. of Adv. in Appl. Sci. Vol. 8, No. 1, March 2019: 1 – 7

Int. J. of Adv. in Appl. Sci.

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2.1. Immunofluorescence technology The function of fluorescent polyclonal antibodies (FPA) that identify ammonia monooxygenase (AMO) were functional immunofluorescences to identify ammonia oxidizing microbes [35]. The FPA production depends on the pure cultures accessibility. The diversity of essential serological was shown within the isolates from the various sediment samples [36]. FPA was changed for selective calculating of many Nitrosomonas spp supported the affiliated and suspended microorganism for species-specific counting [37]. For years ago, Nitrosomonas spp were considered to be natural compounds of the microbes [37], and at the minimum the most obvious [38], the AOB in freshwater habitats, whereas Nitrosospira spp. were found to be dominated in sedimentary [39, 40]. Through microscopy of immunofluorescence with partial sequence 16S ribosomal RNA sequences analysis and DNA hybridization [41, 42]. Furthermore, the novel studies of other places have changed this show. 2.2. Phylogenetic technology The understanding that microorganisms of nitrifying (nitrite and ammonia oxidizers) appeared to the sole of possessing operated for 16S rRNA molecular methods were significantly facilitated analyses of communities [43, 44]. The understanding of these sequences enables several techniques for analyzing the group structures of nitrifying microorganisms. Many of the experiments have been made for the clade of the AOB within the genus β-Proteobacteria. 2.2.1. PCRbased technology Up to 1997, 30 oligonucleotides of the 16S rRNA gene sequences are known [45], with understanding specificity for AOB of the genus βProteobacteria were proposed for PCR or hybridizations with whole cells or DNA and RNA extraction. Almost all the understanding about the AOB diversity groups in natural habitats has been collected to apply oligonucleotides. The primer of the oligonucleotide for Nitrosococcus oceani was described [46, 47], however, have not been extensively used [48]. The PCR with primer sequences of known characteristics was applied to amplification 16S rRNA, AOB, and AOA groups from natural habitat. The use of direct nested PCR facilitates the detection of the family Eubacteriaceae [49] was achieved. In a few situations, modified PCR showed positive significance from samples where direct PCR unsuccessful, that was primarily ascribed to decreased AOB abundance [50]. When surveys of various environmental samples (Soil and water, nutrient-enriched or poor) it showed apparently that Nitrosospira-like AOB is nearly omnipresent [51]. Nitrosospira-like AOB showed to be the dominant AOB and the universally present worldwide. However, Nitrosomonas-like AOB might be identified from several environmental samples. N. eutropha/europaea has long been recognized that at lowest [52, 53], owing to their possibility to construct microbial extracellular polymers substances [41]. As a strategy of survivability, the exopolymers might be supported recovery after extreme dryness stress in sediments [54], survivability in famine situations [55]. The possibility of identification of members of the N. eutropha / europaea parentage increased with raising nutrient concentrations [56, 57]. Occasionally, although sequences of Nitrosomonas-like might not be determined in ecological DNA investigations, enrichment cultures obtained from same habitats detected Nitrosomonas spp [58, 59]. The considered of obvious that even within various types of sediment (profundal and littoral sediments) distinct communities correlated to either N. eutropha or N. europaea occurred [60]. Interestingly, during a continuously high number of ecological investigations sequences related to the same AOB clusters were obtained from many various sites (samples of aquatic habitats) with the diversity structure of their ‘total’ bacterial groups. The statistics were showed the representation of higher abundance within the microbial communities, which just some strains are in culture as described. In the investigation applying 16S rDNA amplicons to the descriptions AOB group in the root zone and the sediment of lakes in the Netherlands, not confirmations were observed which any specific classification/phylogenetic clusters were particular for these periodically low amounts of oxygen conditions [61]. The fertilized of soil huge elevated in nitrification concentration, however not in population size, was assessed by using PCR of competitive depended on 16S rRNA and amoA genes. This was proposed phenotypic modification within the AOB group [62]. It could be exciting to find if such changes might be monitored when the aim of the primary proteins mRNAs of ammonia oxidation (AMO, HAO). Over the past decades after the implementation of terminal restriction fragment length polymorphism (T-RFLP) [63, 64], temperature gradient gel electrophoresis (TGGE), and denaturing gradient gel electrophoresis (DGGE) [65] in environmental microbiology. These approaches are recent generally used in most microbiological laboratories globally as molecular tools to correlate between the diversity of microorganism communities and to observer population dynamics. Recent progress in these approaches has demonstrated their significance in environmental microbiology. Molecular techniques applied to investigations of abundance of the ammonia oxidizing … (Amjed Ginawi)

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2.2.2. Fluorescent in situ hybridization (FISH) Various oligonucleotides investigations to determined ammonia oxidizing group of the subclass βProteobacteria have been reported [66, 67], but there is barely a little FISH applications for evaluating of the abundance of nitrifying microorganism in ecosystems. According to the appearance search of the basic local alignment search tool (BLAST), together probes aligned sequences of 16S rRNA in ammonia oxidizing group and some unclassified clones. They might be used together because their characteristics are slightly various. The application of determined probes that identify diverse communities of ammonia oxidizing group could be possible only in environments with high ammonia oxidizing group abundances, for example, wastewater treatment plants. Until a little investigation for ammonia oxidizing group of the Proteobacteria has been reported, that could be particularly functional for the counting AOA and AOB groups in aquatic samples. Microarray technology has considered a high-throughput platform and valuable tool to estimate possible candidate proteins and genes [68, 69]. DNA microarrays are extensively applied for gene expression [70, 71]. 3.

CONCLUSION Molecular biology approaches have achieved the investigation of unidentified microorganisms and their function in various natural habitats more obtainable. Moreover, we are distanced to knowing the difficulty of microbial in every environmental. Nearly more than one hundred of the diverse genomes per gram occur in soils, and the numbers might be higher in habitats of aquatic likewise. Target DNA (functional genes or 16S rRNA sequences encoding) might be amplified by cloned, sequenced and PCR. The technology such as T-RFLP, TGGE, and DGGE might be facilitated to shown sample sets for the evaluation of regional and seasonal variations. Wherefore most obtained of sequences for functional genes will be novel. The status did not become dramatic in the 16S rRNA sequences situation. However, the functions of each environment will increase unique sequence data of the 16S rRNA databanks abundances. Recently molecular technology, like microarrays with various copies of all denitrification and nitrification genes, will potentially help to show of microorganisms for their specific metabolic facilitate in the nitrogen cycle. The knowledge is needed not concerning the bacterial structure, however, the concerning their activities. The excitement about the molecular biology approaches which allow us to investigate microorganisms directly in their environmental, we require extra attempts to develop and characterize molecular methods, those organisms that are still unidentified. ACKNOWLEDGEMENTS This work is financially supported by the National Natural Science Foundation of China (grant No. 31070089, 31170078, and J1103514), the National High Technology Research and Development Program of China (grant Nos. 2013AA065805 and 2014AA093510), the National Natural Science Foundation of Hubei Province (grant No. 2015CFA085), and the Fundamental Research Funds for HUST (grant Nos. 2014NY007, 2017KFYXJJ212, 2017KFXKJC010, 2017KFTSZZ001). Many thanks are indebted to the Analytical and Testing Center of Huazhong University of Science and Technology for the measurements of water quality. REFERENCES [1] [2] [3] [4] [5] [6] [7]

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[61] Kowalchuk, G.A., et al., "Community analysis of ammonia-oxidising bacteria, in relation to oxygen availability in soils and root-oxygenated sediments, using PCR, DGGE and oligonucleotide probe hybridisation," FEMS Microbiology Ecology, vol. 27(4), pp. 339-350, 1998. [62] Ebie, Y., et al., "Comparative analysis of genetic diversity and expression of amoA in wastewater treatment processes," Appl Microbiol Biotechnol, vol. 64(5), pp. 740-4, 2004. [63] Liu, W.T., et al., "Characterization of microbial diversity by determining terminal restriction fragment length polymorphisms of genes encoding 16S rRNA," Appl Environ Microbiol, vol. 63(11), pp. 4516-22, 1997. [64] Liesack, W. and P.F. Dunfield, "T-RFLP Analysis," in Environmental Microbiology: Methods and Protocols, J.M. Walker, J.F.T. Spencer, and A.L. Ragout de Spencer, Humana Press: Totowa, NJ, Editors. 2004, pp. 23-37. [65] Ferris, M.J., G. Muyzer, and D.M. Ward, "Denaturing gradient gel electrophoresis profiles of 16S rRNA-defined populations inhabiting a hot spring microbial mat community," Applied and environmental microbiology, vol. 62(2), pp. 340-346, 1996. [66] Molina, V., et al., "Ammonia-oxidizing beta-proteobacteria from the oxygen minimum zone off northern Chile," Appl Environ Microbiol, vol. 73(11), pp. 3547-55, 2007.

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[67] Magalhaes, C., et al., "Composition and activity of beta-Proteobacteria ammonia-oxidizing communities associated with intertidal rocky biofilms and sediments of the Douro River estuary, Portugal," J Appl Microbiol, vol. 103(4), pp. 1239-50, 2007. [68] Csako, G., "Present and future of rapid and/or high-throughput methods for nucleic acid testing," Clin Chim Acta, vol. 363(1-2), pp. 6-31, 2006. [69] Rensink, W.A. and C.R. Buell, "Microarray expression profiling resources for plant genomics," Trends Plant Sci, vol. 10(12), pp. 603-9, 2005. [70] Makretsov, N., et al., "A fluorescence in situ hybridization study of ETV6-NTRK3 fusion gene in secretory breast carcinoma," Genes Chromosomes Cancer, vol. 40(2), pp. 152-7, 2004. [71] Brown, L.A., et al., "Amplification of EMSY, a novel oncogene on 11q13, in high grade ovarian surface epithelial carcinomas," Gynecol Oncol, vol. 100(2), pp. 264-70, 2006.

Molecular techniques applied to investigations of abundance of the ammonia oxidizing … (Amjed Ginawi)

International Journal of Advances in Applied Sciences (IJAAS) Vol. 8, No. 1, March 2019, pp. 8~13 ISSN: 2252-8814, DOI: 10.11591/ijaas.v8.i1.pp8-13

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Machining time and number of machine for the production planning of wheel nut releaser with the demand of 100 units/day Nanang Qosim1, Mohammad Hartono2 1

Department of Mechanical Engineering, Faculty of Engineering, Universitas Indonesia, Indonesia 2 Department of Mechanical Engineering, Politeknik Negeri Malang, Indonesia

Article Info

ABSTRACT

Article history:

The impact of competition in the industry has driven people to improve the effectiveness of production process. One of the main factors supporting that effectiveness is the good planning of both the need and the number of machines. The aim of this study is to estimate the machining times to determine the number of machines to manufacture 100/day of the nut releaser tool for car wheel. The result shows that the need of the number of machines to manufacture each component of the nut releaser tool for car wheel are 7 units of power hacksaw, 27 units of lathe machine, 8 units of drilling machine, 12 units of milling machine, 2 units of EDM machine and 25 units of bench work.

Received Dec 17, 2018 Revised Feb 6, 2019 Accepted Feb 17, 2019 Keywords: Demand Machining time Number of machines Production planning

Corresponding Author: Nanang Qosim, Department of Mechanical Engineering, Faculty of Engineering, Universitas Indonesia, Kampus Baru UI, Depok 16424, Jawa Barat, Indonesia. Email: nanang2307@yahoo.com

INTRODUCTION To effect the effectiveness of the production process of the nut releaser tool for car wheel, there is required a planning of needs of machines based on the estimation or calculation of precise machining time. In the era of industrialization, the factory is no longer merely a simple manufacturing consolidation but is a set of integrated technology systems with the optimal arrangement and planning. The advanced technology and good product design will be meaningless due to the imprecise planning. Mistakes made in the planning of the need of machines will lead to fatal losses, as the production activities of an industry will run for a long time [1]. Machining Time, symbolized by (Tm), is the range of time taken by a machine to work on one unit of product [2]. The accurate estimation of machining time is essential for estimating the production costs, as well as assisting production process planning [3]. To produce the nut releaser tool for car wheel, the machines and facilities used are power hacksaw, lathes machine, milling machine, drilling machine, EDM machine and bench work, with the differences of machining time for each machine. The differences of machining time for each component of the nut releaser tool for car wheel, as shown in Figure 1, are influenced by the design and dimensions of each component. 2. MATERIALS AND METHODS 2.1. Technical drawing of each component The main data required in this study is the technical drawing for each component. Figure 1 is the technical drawing for each component of the nut releaser tool for car wheel. Journal homepage: http://iaescore.com/online/index.php/IJAAS

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Figure 1. Main components of the nut releaser tool for car wheel: (a) Top side; (b) Bottom side; (c) Connector; (d) Main gear; (e) Sub gear; (f) Main shaft; (g) Shock shaft 2.2. The calculation of machining time The calculation of machining time on power hacksaw is determined by the thickness of material to be sawed, t (mm); number of strokes, V (strokes/min) and depth of feeding, f (mm/strokes) with the expression as (1) [4]: Tm =

h Vxf

(1)

Other parameters for determining the machining time on power hacksaw may refer to Westermann Tables for The Metal Trade [5]. There are two main types of work on the lathe machine, ie facing and longitudinal cutting, with the expression of each as (2) and (3) [6]: Machining time and number of machine for the production planning of wheel … (Nanang Qosim)

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Tm =

df x i Sf x N

(2)

Tm =

lxi Sx N

(3)

where df (mm) is the length of surface to be removed; i (mm/rev) is the intensity of feeding; S is feed rate; Sf (mm/rev) is feed rate for facing process; N (rev/min) is the rotation of engine spindle; and l (mm) is the length of the surface to be removed plus the tolerance of 1. Other parameters for determining the machining time on the lathe machine can refer to Westermann Tables for The Metal Trade [5]. On the milling machine, the calculation of machining time is influenced by both the total path length, L (mm); and feed rate, s (mm/rev), with the expression as (4) [3]: Tm =

L s

(4)

Other parameters for determining machining time on the milling machine may refer to Westermann Tables for The Metal Trade [5]. The calculation of machining time on the drilling machine is determined by the length of the drilling tool, L (mm); the depth of the material to be drilled, l (mm); diameter of the drilling tool, d (mm); feed rate, S (mm/rev); and cutting speed, v (m/min) with the following expression [5]: Tm =

(5)

where, L = l + 0,3d

(6)

Other parameters for determining machining time on the drilling machines may refer to Westermann Tables for The Metal Trade [5]. 2.3. Standard time and Output Standard Based on the data of the machining time calculation, it can be determined the Standard time (ST) and Output Standard (OS). ST is a standard of time estimation taken by a machine to produce one component after added by the setting time, auxiliary time and allowance time. Formulated in an expression as (7) [7]: ST = Tm + Setting time + Allowance time + Auxiliary time

(7)

OS is the quantity of the product that can be produced by a machine within a day. On the nut releaser tool for car wheel factory, there are assumed three shifts of work within a day, 8 hours for a shift. The OS value itself can be determined if the value of ST has been determined by converting the unit as (8) [7]: OS = x x 3.

(8)

RESULTS AND DISCUSSION Based on the calculation of machining time of each component, there is obtained the data of calculation as presented in Table 1.

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Tabel 1. The data of machining time calculation Part A

F G

Machine Power hacksaw Lathe machine Drilling machine Bench work Power hacksaw Lathe machine Drilling machine Bench work Power hacksaw Lathe machine Drilling machine Bench work EDM Power hacksaw Lathe machine Drilling machine Milling machine Bench work Power Hacksaw Lathe machine Milling machine EDM Power Hacksaw Lathe machine Milling machine Power Hacksaw Lathe machine Milling machine Bench work

ST (min/unit) 8,98 28,52 12,92 37,5 8,98 28,52 14,62 37,5 3,2 12,06 8,31 11,5 1,88 3,87 17,89 8,02 18,41 8 4,53 20,57 46,02 6,99 2,4 16,22 4,62 2,32 16,37 4,56 7,4

OS (unit/day) 161,98 50,49 111,46 38,4 160,36 50,49 98,5 38,4 450 119,4 173,28 125,22 766,50 372,09 80,49 179,55 78,22 180 317,88 70 31,29 206 600 88,78 311,69 620,69 87,96 315,79 194,59

The next step is the calculation of material requirements for each component for each stage of the process, with the defects assumed as 0.5% on power hacksaw, 1% on lathe machine, 1% on drilling machine and 0.5% on bench work. The calculations are solved by (9) (Tompkins, White, Bozer, Frazelle, Tanchoco, & Trevino). Pn =

demand Pg n 100 = = 1  Pd n % 1  Pd n % 1  0,5%

(9)

The stages of this solution sequentially refer to the illustration as shown in Figure 2. Figure 2 is the flow diagram for an example of the material requirement calculation for top side component.

Figure 2. Flow diagram of the material requirement for top side

Machining time and number of machine for the production planning of wheel … (Nanang Qosim)

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ISSN: 2252-8814

Based on the calculation of Tm estimation for each component, there are obtained the OS values for each machine and production facility. Further, from the calculation of the need of material, there are obtained the value of material requirements for each process (P) on each machine and production facilities. Based on this data, the estimated calculation of the need for the number of production machines (N) for each component can be determined by (10) [8]. N1 = P1 OS1

(10)

So that, the results of all calculations of N for each component can be shown in Table 2. In Table 3, there is the total calculation of the need for the machine to produce all components of the nut releaser tool for car wheel with the demand of 100 units/day. Tabel 2. The calculation results of the need for the number of machine for each component Part

Machine

Top Side

Power hacksaw Lathe machine Drilling machine Bench work Power hacksaw Lathe machine Drilling machine Bench work Power hacksaw Lathe machine Drilling machine Bench work EDM Power hacksaw Lathe machine Drilling machine Milling machine Bench work Power Hacksaw Lathe machine Milling machine EDM Power Hacksaw Lathe machine Milling machine Power Hacksaw Lathe machine Milling machine Bench work

Bottom Side

Connector

Sub Gear

Main Gear

Main Shaft Shock Shaft

ST (min/unit)

OS (unit/day)

8,98 28,52 12,92 37,50 8,98 28,52 14,62 37,50 3,20 12,06 8,31 11,5 1,88 3,87 17,89 8,02 18,41 8,00 4,53 20,57 46,02 6,99 2,4 16,22 4,62 2,32 16,37 4,56 7,40

161,98 50,49 111,46 38,40 160,36 50,49 98,50 38,40 450 119,4 173,28 125,22 766,50 372,09 80,49 179,55 78,22 180 317,88 70,00 31,29 206 600 88,78 311,69 620,69 87,96 315,79 194,59

Defect of product (% Pd) 0,5 1 1 0,5 0,5 1 1 0,5 0,5 1 1 0,5 2 0,5 1 1 1,5 0,5 0,5 1 1,5 2 0,5 1 1,5 0,5 1 1,5 0,5

Product Prepared (P) (unit/day) 106 105 103 101 106 105 103 101 529 526 520 514 511 526 523 517 511 503 108 107 105 103 105 104 102 520 517 511 503

Number of machines (N) 0,65 2,08 0,92 2,63 0,65 2,08 1,04 2,63 1,18 4,41 3,00 4,10 1,50 1,41 6,40 2,80 6,53 2,79 0,34 1,53 3,36 0,50 0,18 1,17 0,33 0,84 5,88 1,62 2,58

Tabel 3. The total need of machine for all components Machine Power Hacksaw Lathe machine Drilling machine Milling machine EDM Bench work

A 0,65 2,08 0,92 2,63

B 0,65 2,08 1,04 2,63

C 1,18 4,41 3,00 1,50 4,10

Part Number D 1,41 6,4 2,80 6,53 2,79

E 0,34 1,53 3,36 0,50 -

F 0,18 1,17 0,33 -

G 0,84 5,88 1,62 2,58

Total machine 6,98 26,88 7,76 11,84 2,00 14,73

Grand Total 7 27 8 12 2 15

From the Table 2 and 3, it appears that lathe machine is the most needed type of machine in the manufacturing process of the nut releaser tool for car wheel with the demand of 100 units/day, with a total of 27 units. The amount is followed by bench work facilities (15 units) and milling machines (12 units) as the most needed machines. EDM which is a type of very expensive price-machine has the smallest need of 2 units. These results indicate that the planned estimates have a good efficiency as well as the high economic side. Int. J. of Adv. in Appl. Sci. Vol. 8, No. 1, March 2019: 8 – 13

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CONCLUSION In this study, it can be concluded that the number of machines and facilities needed to produce 100 units of the nut releaser tool for car wheel is 7 units of power hacksaw, 7 units of lathe machine, 8 units of drilling machines, 12 units of milling machines, 2 units of EDM machines and 25 units of bench work facilities. However, further study needs to make a more effective planning according to the modernization of machine and facilities. ACKNOWLEDGEMENTS The author would like to thank Department of Mechanical Engineering, Universitas Indonesia for valuable support to conduct this study. REFERENCES [1] [2] [3] [4] [5] [6] [7] [8]

E.-Y. Heo, D.-W. Kim, B.-H. Kim, and F. F. Chen, "Estimation of NC machining time using NC block distribution for sculptured surface machining," Robotics and Computer-Integrated Manufacturing, vol. 22, pp. 437-446, 2006. L. Skoczylas, K. Skoczylas, and D. Wydrzyński, "The standard milling time of flat surfaces without outline restrictions," Advances in Science and Technology Research Journal, vol. 10, 2016. R. T. Coelho, A. F. de Souza, A. R. Roger, A. M. Y. Rigatti, and A. A. de Lima Ribeiro, "Mechanistic approach to predict real machining time for milling free-form geometries applying high feed rate," The International Journal of Advanced Manufacturing Technology, vol. 46, pp. 1103-1111, 2010. H. A. Youssef and H. El-Hofy, Machining technology: machine tools and operations, CRC Press, 2008. H. Jutz, Westermann Tables For The Metal Trade, New Age International, 2006. J. R. Walker, Machining Fundamentals: From Basic to Advanced Techniques, Inc., USA: Goodheart-Wilcox Company, 2004. S. Wignjosoebroto, Factory Preposition and Material Translocation (in Bahasa), Surabaya: Penerbit Guna Widya, 1996. J. Tompkins, J. White, Y. Bozer, E. Frazelle, J. Tanchoco, and J. Trevino, Facilities Planning, Inc. 2nd edition. USA: John Wiley& Sons, 1996, pp. 36-47.

Machining time and number of machine for the production planning of wheel … (Nanang Qosim)

International Journal of Advances in Applied Sciences (IJAAS) Vol. 8, No. 1, March 2019, pp. 14~25 ISSN: 2252-8814, DOI: 10.11591/ijaas.v8.i1.pp14-25

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Car following and lane changing behavior using NGSIM and China data Md. Mijanoor Rahman, Mohd. Tahir Ismail, Majid Khan Majahar Ali School of Mathematical Sciences, Universiti Sains Malaysia, Malaysia

Article Info

ABSTRACT

Article history:

Road safety is imperative theme because increasing road fatalities deaths in world. Besides road fatalities, traffic jam is increasing, human is frustrated for uncomfortable journey. The roads safety and passengers comfortable of the roadway system are vastly depended on the Car following (CF) and Lane Changing (LC) features of drivers. CF and LC theory describe the driver behavior by following paths in a traffic stream. In this research, researchers have compared to US-101 Next-Generation-Simulation (NGSIM) data with Beijing forth ring road, China freeways real trajectory data by CF and LC models. The CF data has been calibrated with Genetic Algorithm (GA). Reproducing Kernel Hilbert Space (RKHS) is generated the LC beginning and finishing points. Findings revealed that the CF parameters as maximum acceleration, minimum deceleration, free speed, minimum headway and stopping distance percentages of Chinese data are 74.71%, 79.95%, 66.57%, 0.018% and 65.65% respectively of NGSIM data. After completing the comparison, researchers have been found out optimization safety and comfortable acceleration-deceleration and LC beginning-finishing points of driver behavior. Here this analysis generates the driver behavior at real traffic network on the express highways of specific two roads US-101 (NGSIM) data and Chinese freeways data. Since NGSIM data is well simulated so road traffic is more safety and comfortable for journey.

Received Nov 30, 2018 Revised Feb 7, 2019 Accepted Feb 18, 2019 Keywords: Calibration Car following Genetic algorithm Lane-changing Vehicle dynamics

Corresponding Author: Majid Khan Majahar Ali, School of Mathematical Sciences, Universiti Sains Malaysia, 11800 USM, Penang, Malaysia. Email: majidkhanmajaharali@usm.my

INTRODUCTION In 2017, China is global massive Automobile marker exceed USA with demand-supply and after 2020, 200 million passenger vehicles and loaded vehicles will be emerging in the traffic road network [1]. Then, not only people are increasing but also vehicles are increasing progressively. It has emanated the increased traffic exposure which has more road traffic crashes [2]. By road crashes, every year 1.25 million human lives decease, more than1.2 million road fatalities and above 50 million peoples are sickened whole world. But every country has not same propagation traffic fatalities. Road crash is rising geometrically in every country, although developing countries like as China ensues more traffic fatalities than developed countries like USA. Road crash has been taken seriously by developed countries. Only 10% road crash ensues in these types of countries. UN also proclaimed that road safety is now thoughtful disclosure entire universe [3]. In traffic network, CF and LC are very important mater. CF and LC model are being analyzed for traffic safety and comfortable journey [4, 5]. A complex traffic network behavior has been analyzed different types of traffic flow models as microscopic and macroscopic. Macroscopic model is compressible vehicles runny formed. Microscopic model is vehicle discrete position and running by dissymmetry situations [4], Microscopic simulation Journal homepage: http://iaescore.com/online/index.php/IJAAS

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produced a significant part in the exploration and plan of traffic network facilities. It delivers very flexible policy on which many traffic network situations can be deliberate in a measured operation without troublemaking real-world traffic [6, 7]. Generally, a traffic network simulation platform contains of numerous models which discourse different features of traffic network behavior [8-12]. Most of the microscopic simulation accept ordinary difference equations categorized by a rare parameter to explain driver Car Following Model (CFM). Apropos promise the simulation models glowing reproduce traffic scenario detected in preparation, diverse calibration methods are proposed to fittingly control these types of model parameters [9, 10, 12-14]. A rare parameter has somatic parallels in roughly microscopic CFMs like as preferred velocity conducted in Gipps CFM [9, 15]. Farther macroscopic structures of traffic network flow parameters like as critical density, free flow velocity and jam density are significant in numerous models and can be directly projected from circle indicator data [8]. Nevertheless, in lots of CFMs, supplementary parameters can’t be merely derived from macroscopic capacities [9, 16-18]. The calibration of CFMs by using observed vehicle road trajectory data enlarged interests recently, road trajectory data delivered more of driving behavior on the microscopic traffic flows [9-13, 19]. Accurately by data estimation, the model parameters are now very importance issue from real road trajectory data. Nobody could perform to perfectly fit the data with real trajectory CF data [6, 17]. Most efficient CFM, IDM was introduced and then it was used for given many significant rules at traffic network [6]. The IDM is a CF dynamical system by using many physical formulas of velocity, acceleration and deceleration. This IDM system give simulation data by using many parameters like as initial position, velocity, acceleration or deceleration of lag and front vehicle and gap and time headway between vehicles. Researchers have taken very widely usable optimization algorithm GA for calibration of CF simulation data to trajectory data. There have many optimization functions for calibration of simulation data to trajectory data like as sequential-quadratic-programming-Algorithm (SQPA), simultaneous-perturbationstochastic-approximation-algorithm (SPSAA), Nelder–Mead-algorithm (NMA) and some others [20]. To ﬁnd an optimal solution of parameter values was used calibration problem [15], the Nelder–Mead-algorithm [21, 22]. GA is the most efficient and deserved very nearly accurate value for the optimization other than many optimization function [23]. Paz et al. [18] found the best CF parameter from CORSIM software data by calibration with real trajectory data [18]. They used GA and SPSAA for microscopic car following calibration method. For CF parameters calibration, a stochastically global search GA is most broadly used system for unconstrained and constrained objective functions [24]. Li et al. [20] explained about GA method surely give CF calibration parameters [21]. In this research, authors have analyzed the IDM, CFM and calibrated the parameters by Sum of Square Error (SSE) objective function using widely functioning and very efficient GA algorithm [25]. Nilsson and Sjöberg [26] worked on binary lane choice system such as one or zero, current lane is zero and change lane is one and velocity profile such as collision time and control velocity for car driver. The authors analyzed the LC maneuver by only collision time LC behavior [26]. Hongtao et al. [27] proposed multiplayer Stackelberg game-theory based LC maneuver by using safety factor, gap acceptance, position, velocity and acceleration utilities of host car and surrounding car for human driver. Here, gap acceptance is depended on safety factor, position difference and velocity difference not acceleration. The researchers nicely represented the car lateral and longitudinal position into the LC time but didn’t calculated the optimization LC position of the car. A human driver could maintain the LC maneuver without any wireless connection among the drivers only using signal light. The optimization LC starting (time and position) and ending (time and position) and could be calculated in their article [27]. Marinho et al. [28] discovered search algorithm RKHS, non-parametric regression fitted system for analyzing real trajectory data sets [28]. C. Dong et al [29] proposed new LC model using reproducing kernel Hilbert space (RKHS) for optimizing the LC beginning and finishing point without collision. RKHS is very efficient non-parametric regression analysis is used on trajectory LC maneuver with optimum distance function [29]. The scientist worked on US 101 NGSIM real trajectory data for finding the beginning and finishing time. On LC time, the beginning and finishing time is very important for the driver because every driver want to avoid collision with other driver for safety and but worked on only real trajectory data not any specific model or maneuver. In this research, authors have analyzed the gap acceptance LCM and beginning and finishing points of LC maneuver using RKHS by NGSIM data and Chinese data. This work is bearing to develop a model of driver CF and LC in the expressed highway with on-ramp and off-ramp and urban congested traffic area.

Car following and lane changing behavior using NGSIM and China data (Md. Mijanoor Rahman)

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METHODOLOGY The detail methodology of CF with calibration and LCMs are analyzed in this section. IDM CFM generates the simulation data, this simulation data calibrates the IDM parameter by SSE objective function with GA algorithm. After that, in methodology is given the LC behavior of driver by using RKHS, nonparametric regression fitted model from NGSIM data. In this research, not only are the nearest ego and following vehicles in the existing and neighboring lanes considered but also the closer ego and following vehicles. The different car following and lane changing model has been developed by using MATLAB coding and extract trajectory road traffic data from Beijing forth ring road, China and US-101, NGSIM data. These models have been simulated about known parameters then calibrated according the trajectory data. These systems are discussed in the following. 2.1. CFM In traffic-flow modeling, the Intelligent-Driver-Model (IDM) is a microscopic traffic-flow model for simulation of urban and freeway traffic. This model has been discussed as follows [9, 30]: Distance between ego car 𝑛 and lag car (𝑛 − 1) is 𝑠 =𝑥 Where 𝑥 , 𝑥 respectively.

(1)

−𝑥 −𝑙 ,

denote the position of ego car 𝑛 and lag car (𝑛 − 1) and length lag car (𝑛 − 1)

𝑙

(2)

∆𝑥̇ = 𝑥̇ − 𝑥̇ Where 𝑥̇ and 𝑥̇ are the velocity of the 𝑛 − 𝑡ℎ car and (𝑛 − 1) − 𝑡ℎ respectively. The simplified version of IDM model is follows 𝑥̈ = 𝑎 1 −

̇ ̇

−

∗( ̇

,∆ ̇ )

(3)

Where the desired gap 𝑠 ∗ is defined by 𝑠 ∗ (𝑥̇ , ∆𝑥̇ ) = 𝑠 + 𝑚𝑎𝑥 𝑜, 𝑥̇ 𝑇 + Where 1 −

̇ ̇

̇ ∆ ̇

(4)

√

is the desired acceleration for ego vehicle and is

∗( ̇

,∆ ̇ )

is the braking deceleration

for front vehicle. The following parameters values of IDM CFM have been explained [21]: regular acceleration, 𝑎 = 1.4 𝑚/𝑠 , minimum deceleration, 𝑏 = 2.0 𝑚/𝑠 , desired speed, 𝑣 = 30 𝑚/𝑠, desired headway, 𝑇 = 1.5 𝑠, jam minimum distance, 𝑠 = 2 𝑚 and acceleration exponent, δ The CF simulation flowchart as follows in Figure 1.

Figure 1. CFM simulation flowchart Int. J. of Adv. in Appl. Sci. Vol. 8, No. 1, March 2019: 14 – 25

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2.1.1. Calibration process Calibration for CF give the better result for simulation 𝑛 number of car data with compared to the trajectory same number of car data. This comparison is measured of headway, gap, velocity and acceleration by optimization objective function [11]. Researchers have analyzed well known (SSE) function of velocity and position for calibration between simulations 𝑛 number of car data with compared to the trajectory same number of car data [21]. Ciuffo and Punzo [31] worked on parameter calibration of synthetic data with AIMSIM software data using some Goodness of Fit (GoF) as (SSE), Root Mean Square error (RMSE) and Normalized Root Mean Square error (RMSN) to objective function (OF). SSE is most suitable and widely used best GoF for car following calibration method than other GoF [23, 31]. Min = ∑

[𝑣 (𝑖𝑇|𝜃) − 𝑣 (𝑖𝑇)]

(5)

Where Ω is optimization value of solution space 𝜃, 𝑣 (𝑖𝑇|𝜃)and 𝑣 (𝑖𝑇) denote the simulated and empirical position or velocities of the 𝑛 − 𝑡ℎ CF at time 𝑡 = 𝑖𝑇, where = 2, 3, … , 𝑁 , respectively. 𝑣 (𝑖𝑇) is taken as known inputs (real trajectory data) in the optimization problem. Decision variable 𝜃 is not to easily applicable in the optimization function and 𝑣 (𝑖𝑇|𝜃) is depended to the (5). Here, the derivative variable of this optimization objective function is difﬁcult to achieve the value. For overcome this condition, Researchers have analyzed the Genetic Algorithm (GA) optimization procedure for finding the objective value. 2.1.2. Genetic algorithm (GA) In the research, GA chooses to save appropriate chromosomes and remove weak chromosomes from parent chromosome (PC) grade. Every simulation time, GA can update of its parent’s value then compare to the previous values and after validating for next parents. The optimization procedure GA works as follows: randomly takes some simulation values (chromosomes) that are parent chromosomes (PCs) and finds the feasible values by (5). Then it deletes some digit (chromosome) from highest some efficient value and interchange their digit for find two new values. And it compares to the previous values then it is new parents. Again, find the feasible value from (5). If desired optimization value gain, then it is stopped other than the system continues. By the above way, GA can check then update every value by change the chromosomes at every iteration for desired optimization values. The Genetic Algorithm (GA) flowchart as shown in Figure 2.

Figure 2. Genetic Algorithm (GA) Flowchart

Car following and lane changing behavior using NGSIM and China data (Md. Mijanoor Rahman)

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ISSN: 2252-8814

2.2. LC model Again, conditional on the drive, LC decision is characterized by two maters mandatory or discretionary. Usually, when the driver must need to change from current lane to desired lane, then this type of changing is called mandatory lane-changing. Other type of changing is called discretionary lane-changing, when driver want to improve the vehicle speed conditions or comfortable journey or over taking to lead vehicle or passing heavy vehicle or avoiding the traffic conditions near off-ramp or on-ramp or road condition is not good, it is not must needed to change the lane, then driver decide the discretionary LC [32, 33]. For mandatory LC, driver is more aggressive who sometimes avoid gap acceptance and forces the other drivers [34]. In this research, Researchers have analyzed these two types of LC for car. After taking the decision of drivers (discretionary and mandatory), Driver must calculate the gap at target lane to ego vehicle and lead vehicle. Then the ego vehicle goes by dynamical strategies like as position, velocity, acceleration and deceleration for avoid collision to the surrounding vehicles. Materially, the proposed procedure can also be realistic to heavy vehicles or car discretionary and mandatory LC vehicles by deﬁning them as ego vehicles [27]. In this section, researchers have discussed LCM by flowchart then analyzed the methodology of RKHS, non-parametric regression fitted model. This model is generated the LC beginning and finishing points by using NGSIM data, but researchers couldn’t explain the LC behavior of Chinese freeway data. The LC decision algorithm is discussed as follows by flowchart in Figure 3 [35].

Figure 3. LC behavior flowchart 2.2.1. LC method for behavior generator in RKHS A real trajectory is plainly designated by Gaussian-radial-basis-functions, by using RKHS of functional gradient to improve cost functional to static difficulties or direct high dimensional arm to voids [28]. Researchers monitor previous design, however by using functional-gradient the optimal solution is obtained, our reviewed method depends on RKHS with non-parametric-regression and previous dataset to evaluate continuous values as the start or end points. Reproducing-Kernel-Hilbert-Space H holds families of smooth-functions which are distinct by Mercer-Kernel. Again Mercer-Kernel is continuous mapping [29]. 𝐾 ∶ 𝑋 × 𝑋 → 𝐼𝑅, 𝑖. 𝑒. , 𝐾(𝑥, 𝑦) =< 𝑝, 𝑞 > 𝐻, 𝑤ℎ𝑒𝑟𝑒 𝑝 = 𝐾 , 𝑞 = 𝐾 , 𝑝, 𝑞 ∈ 𝐻

(6)

A linear-combination of this kernel, 𝑝(. ) = ∑ 𝛼 𝐾 (. ) with reproducing property 𝑝(𝑥) =< 𝑝, 𝐾 > is a function in H [29]. Another function 𝐹 ∶ Ψ → 𝐵 where 𝑋𝜀 Ψ and 𝑏𝜀𝐵 is behavior generator. The coordinate space Ψ is included surrounded group of cars position of real trajectory where 𝑋 ≝ {𝑥 } , 𝑥 𝜀𝐼𝑅 . T is length of previous position. The output vector is Ω = {𝑋} as practice set. The kernel is a Int. J. of Adv. in Appl. Sci. Vol. 8, No. 1, March 2019: 14 – 25

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matrix-valued-function Ψ × Ψ → 𝐼𝑅 × where D is dimension of the problem, where dimension is 2 in this research for finding the beginning and finishing points of the LC behavior. Where

𝐾(𝑋, 𝑈) =

𝐾(𝑋, 𝑈) … 𝐾(𝑋, 𝑈)

⋯ … ⋯

, ,

𝐾(𝑋, 𝑈) … 𝐾(𝑋, 𝑈)

(7) ,

The linear-combination of the kernel as following is inner-product-space [29]. 𝐺(𝑋) = ∑ 𝐾(𝑋 , 𝑋). 𝛼 , 𝛼 𝜀𝐼𝑅

(8)

2.2.2. Non-parametric-regression for beginning-finishing points of LC behavior The regularized empirical error is minimized by the below approximation function [29]. 𝑝̂ = arg min ∑ (𝑏 − 𝑝(𝑋 )) + 𝜇𝐿(𝑝)

(9)

Where 𝑋 and 𝑏 are practice input and output of behavior and 𝐿(𝑝) is the penalty terms. The solution of the coefficient term is solved by the following equation. (10)

𝛼 = (𝐾(𝑋, 𝑈) + 𝜇𝑁𝐼) 𝑏 From the above equation, the optimization behavior generator is following equation. 𝑏 = 𝐾 ∗ (𝐾 + 𝜇𝐼) 𝑏

(11)

Where𝑏 ≝ {𝑏 } , collection of practice data set and 𝐾 ∗ , new kernel from previous equations. The distance formula of multi-quadratic kernel. 𝐾(𝑋 , 𝑋 ) =

‖

Where c > 0

(12)

Where 𝑋 , 𝑋 𝜀Ψ are matrices, The Hilbert—Schmit norm give the kernel value by, ‖(𝑋 − 𝑋 ) = 𝑊‖ =

𝑡𝑟(𝑊 . 𝑊)

(13)

A trace function is represented by tr(.), ‖𝑊‖ = ‖𝑊‖ =

𝜇

(𝑊 . 𝑊)

(14)

A spectral norm and Frobenenius norm are consider a singular value of matrix. 𝑡𝑟(𝑊 . 𝑊) =

∑𝜗

(15)

Matrix W has a singular value is 𝜗 𝜇

(𝑊 . 𝑊) = max 𝜗

(16)

A main difference of matrices is calculated by the singular values which takes from real trajectories. 2.2.3. LC algorithm By applying RKHS, non-parametric regression fitted line of begging and finishing points from real trajectory data set is given as follows in Figure 4 [29].

Car following and lane changing behavior using NGSIM and China data (Md. Mijanoor Rahman)

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ISSN: 2252-8814

Figure 4. LC dynamics flowchart 3. RESULT AND DISCUSSION 3.1. Data collection and procedure The NGSIM data are collected from US-101 highway with on-ramp and off-ramp by high proficiency video camera from 50 meters high and the length of the lane is 500 meters at different time and by simulation software, this data is created on the excel sheet including car position, velocity, acceleration, gap to front car, time headway to front car, lane changes scenario and so on which taken from popular website (www.ngsim.fhwa.dot.gov). Again, Guo, et al. [36], collected the Chinese data from Beijing forth ring road, west bridge, west bound, urban expressways with on-ramp (downstream) and off-ramp (upstream) at 9.05 am to 9.35 am in November 03, 2008 sunny day and section length 100 meters [36]. This data has car-id, time, position, speed, time-headway, gap and acceleration. For compare this data with US-101 data, researchers have created the data like US-101 such as only two times positions of data are created to one hundred times position data for every column by using position-speed-acceleration equations. After that, since time is different than US-101, researchers have assumed same time that is the two types of data are at same time. Again, for LC analysis, data is required with longitudinal position and lateral position, but in Chinese data has no these types of column, for these, researchers couldn’t able to compare the LC scenario with US-101 data. Only LC analysis have been discussed for US-101 data. 3.2. CFM The following parameters values are given by the GA calibration of IDM CFM. Five types parameter as maximum acceleration, minimum deceleration, freeway car speed, minimum time headway and stopping distance for US-101 data and Chinese data is many far different time headway, more different at Int. J. of Adv. in Appl. Sci. Vol. 8, No. 1, March 2019: 14 – 25

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minimum deceleration and maximum acceleration and near is free speed and stopping distance from the Table 1. The rang of the parameters as follows and assumed parameters are maximum acceleration, 𝑎 = 1.4 𝑚/𝑠 , minimum deceleration, 𝑏 = 2.0 𝑚/𝑠 , desired speed, 𝑣 = 30 𝑚/𝑠, desired headway, 𝑇 = 1.5 𝑠, jam minimum distance, 𝑠 = 2 𝑚 [21]. The CF parameters as maximum acceleration, minimum deceleration, free speed, minimum headway and stopping distance percentages of Chinese data are 74.71%, 79.95%, 66.57%, 0.018% and 65.65% respectively of NGSIM data. Here, the minimum headway is near zero of Chinese data and acceleration and deceleration are near to maximum. So, stopping distance of Chinese data is far than NGSIM data. From these discussion, US-101 data is safer and more comfortable than Chinese data. Table 1. Parameter values for US-101 data and Chinese data IDM parameter range US-101 Chinese data Percentage of Chinese data

Max. Accel. [1,3] 1.001959 2.96130 74.71%

Min. Decel. [1,4] 1.002857 3.99999 79.95%

Free speed [10, 30] 15.06155 29.9999 66.57%

Min. headway [0, 3] 1.224402 0.00023 0.018%

Stopping dist. [1, 10] 5.231103 9.99825 65.65%

3.2.1. CFM figure explanation Figure 5 and Figure 6 represent driver CF behavior position of US-101 data and Chinese data respectively that group of cars are going so fast but NGSIM car suddenly slow. Figure 7 and Figure 8 represent the observation value of real trajectory and calibration value by GA of gap and speed difference on time. For NGSIM data, calibration data is moving very nearly with observation data for gap and speed difference more than Chinese data. From 700 𝑠 to 730 𝑠 the gap of Chinese data is more than 100 𝑚 and 700 𝑠 to 710 𝑠, the speed difference is more than 30 𝑚/𝑠. From Figure 9 and Figure 10, the objective function SSE values very early decrease of NGSIM data than Chinese data. And the Chinese data objective values of GA are starting from 2.7 × 10 to 1.9 × 10 but the NGSIM data objective values are starting from near 850 to near 300 that are extremely difference. Figure 11 and Figure 12 are the fitness value of these data by GA are represented that NGSIM data fitness value is low than Chinese data. At GA, best fitness values and mean fitness values of NGSIM data is more near than Chinese data. From the above discussion, NGSIM data is nicely simulated than Chinese data is claimed by authors. 3.3. Experimental result of LCM In the NGSIM real trajectory data has eighteen columns including local X and local Y which represent the longitudinal and lateral position respectively and every car has lane id, velocity, acceleration, gap, time headway and time are very necessary for LC behavior analysis. Although in the Chinese data has only six columns as car id, car velocity, acceleration, longitudinal position, frame id, gap and time headway and it has not including local X and local Y that is very necessary for LC behavior analysis. Every group of car as front car, ego car, rear car, front car at target lane, rear car at target lane at left side and right side in NGSIM data have car id, car velocity, acceleration, longitudinal position, frame id, gap and time headway with recorded data within 10 seconds. Front car follows the surrounding car’s dynamics for understanding LC maneuver. For start the lane-changing, front car applies the maneuver. Sections of real trajectories data from all group of cars before the front car starts rotating towards near target lanes are reserved input data as X. For LC behavior, parameter value α has been obtained from given value 𝑏 by above equation. After knowing parameter value α has been applied to another equation for finding new response kernel K ∗ is measured using the data set. And finally, the estimation value 𝑏 is obtained. Figure 13 is shown LC start beginning and finishing points, dots are indicating LC time of car and black line is indicating the before and after LC time which is maneuver for driver by given above formulation, Figure 14 is represented the longitudinal and lateral car position maneuver of ego car for different group, where first, second and fourth group cars are blue, red and orange color respectively are changing from middle lane to right lane (first line) and only purple color car of third group is changing from third lane to middle lane. Figure 15 has been shown the LC position of a group of car, since time is not included in the graph, so the position of lateral and longitudinal is interchanging. If time is included in the graph, then it will be three-dimensional graph.

Car following and lane changing behavior using NGSIM and China data (Md. Mijanoor Rahman)

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ISSN: 2252-8814

Figure 5. Car position for US-101

Figure 6. Car position for Chinese freeway

Observation Calibrated IDM

10 5 0 690

700

710

720

730

740

750

760

770

Time (s) 5 Observation Calibrated IDM

-5 690

700

710

720

730

740

750

760

770

Time (s)

Figure 7. Calibration with observation by GA for US-101

Figure8. Calibration with observation by GA for Chinese freeway

Figure 9. Objective function value of GA for US-101 data

Figure 10. Objective function value of GA for Chinese data

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Figure 11. Fitness values of GA US-101

Figure 12. Fitness values of GA for Chinese data

Figure 13. LC beginning and finishing points of ego car

Figure 14. LC position of ego car of different group

Figure 15. LC position of front car and front car of same group 4.

CONCLUSION The research elementary focus is to compare driver behavior evaluation contextual of different state of US-101 and Chinese freeway road by using many efficient IDM of CF and LC car dynamics. This CF simulation model is calibrated by well-known GA using SSE objective function. The US-101 driver behavior Car following and lane changing behavior using NGSIM and China data (Md. Mijanoor Rahman)

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ISSN: 2252-8814

real trajectory data is well simulated then the Chinese freeway data because NGSIM CF calibration data and simulation data is more likely such as input CF parameter values and output values is very near than Chinese data. The ego car is following the front car according to IDM parameter values as position, gap, headway, velocity, comfortable Acceleration and deceleration if necessary, otherwise driver is changing lane for mandatory or discretionary LC behavior by calculating the safe gap and beginning-finishing points of this time. Based on aforementioned models, driver must understand their behavior and can act for driving car at congested or freeway traffic network. Then, these models have given the result to the driver about their driving system that is, when the driver follow the car and when the driver change the lane about the maneuver for going fast and comfortable. When driver can understand the lane changing time and acceptable gap then car collisions or other crashes are not happened. This research has very helpful for acting to the road safety by analyzing the car-following and lane changing decision of the drivers. After analyzing, this work has been given a strong suggestion and rule to the road user. If driver can understand their behavior about road traffic system by CF and LC, then traffic safety and road crashes will be decreased. Then driver can go very fast avoid accident and the traffic flow has been increased. By analyzed the different scenario, traffic network will bestow the generalized CF or LC behavior which will be the life safe and comfortable journey of human drivers. By properly extracted data for future work, CF calibration parameters and LC behavior can give the rule for road safety, comfortable and time-consuming journey. REFERENCES [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] [20] [21] [22] [23]

S., Ma, et al., "Road traffic injury in China: a review of national data sources", Traffic injury prevention, vol. 13(sup1), pp.57-63, 2012. H. Hao, et al., "Natural gas as vehicle fuel in China: A review." Renewable and sustainable energy reviews, vol. 62, pp. 521-533, 2016. D. Nepelski and D. P. Giuditta, "International technology sourcing between a developing country and the rest of the world. A case study of China," Technovation, vol. 35, pp. 12-21, 2015. Y. Li and D.Sun, "Microscopic car-following model for the traffic flow: the state of the art" Journal of Control Theory and Applications, vol. 10(2), pp. 133-143, 2012. J. Zheng, et al., "Predicting driver’s Lane changing decisions using a neural network model," 2013 IEEE Simulation Modelling Practice and Theory, vol. 42, pp. 73-83, 253-1258, 2014. H. N. Koutsopoulos and H. Farah, "Latent class model for car following behavior", Transp. Res. Part B: Methodol., vol. 49, pp. 563–578, 2012. J. (Ed.) Barceló, "Fundamentals of Traffic Simulation," Springer, 2010. D. Chen, et al., "A behavioral car following model that captures traffic oscillations. Transp," Res. Part B: Methodol., vol. 46(6), pp. 744–761, 2012. Kesting, M. Treiber, "Calibrating car-following models by using trajectory data methodological study," Transp. Res. Rec., vol. 2088, pp.148–156, 2008. V. Punzo, et al., "Can results of car following model calibration based on trajectory data be trusted?," Transp. Res. Rec., vol. 2315, pp. 11–24, 2012 V. Punzo and F. Simonelli, "Analysis and comparison of microscopic traffic flow models with real traffic microscopic data", Transp. Res. Rec., vol 1934, pp.53–63, 2005. S. Ossen and S.P. Hoogendoorn, "Reliability of parameter values estimated using trajectory observations", Transp. Res. Rec., vol. 2124, pp. 36–44, 2009. B. Ciuffo, et al., "Thirty years of Gipps’ car-following model," Transp. Res. Rec., vol. 2315, pp.89–99, 2012. E. Chung, A. G. (Eds.) Dumont, Transportation Simulation: Beyond Traditional Approaches. CRC Press, 2009. E. Brockfeld, et al., "Calibration and validation of microscopic models of traffic flow," Transportation Research Record: Journal of the Transportation Research Board, vol. 1934, pp. 179-187, 2005. S. Ossen and S.P. Hoogendoorn, "Validity of trajectory-based calibration approach of car following models in presence of measurement errors", Transp.Res. Rec, vol. 2088, pp. 117–125, 2008. P. Wagner, "Analyzing fluctuation in car following," Transp. Res. Part B: Methodol., vol. 46, pp. 1384-1392, 2012. Paz, et al., "Calibration of traffic flow models using a memetic algorithm, "Transportation Research Part C: Emerging Technologies, vol. 55, pp. 432-443, 2015. V. Punzo and A. Tripodi, "Steady-state solutions and multiclass calibration of Gipps microscopic traffic flow model", Transp. Res. Rec., vol. 1999, pp. 104–114, 2007. L. Li, et al., "A global optimization algorithm for trajectory data-based car following model calibration," Transportation Research Part C: Emerging Technologies, vol. 68, pp. 311-332, 2016. J.A. Nelder and R. Mead, "A simplex method for function minimization," Comput. J., vol. 7, pp. 308–313, 1965. P. G. Gipps, "A behavioural car following model for computer simulation," Transp. Res. B: Methodol., vol. 15, pp. 105–111, 1981. H. Rakha and Y. Gao, "Calibration of steady-state car following models using macroscopic loop detector data. In: Proceedings of 75 Years of the Fundamental Diagram for Traffic Flow Theory", Greenshields Symposium, pp. 178–198, 2011.

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[24] B. Ciuffo and V. Punzo, "No Free Lunch theorems applied to the calibration of trafﬁc simulation models," IEEE Trans. Intell. Transp. Syst., vol. 15(2), 5, pp. 53–562, 2014. [25] J.C. Lagarias, et al., "Convergence properties of the Nelder–Mead simplex method in low dimensions", SIAM J. Optim, vol. 9(1), pp. 112–147, 1998. [26] J. Nilsson and J. Sjöberg, "Strategic decision making for automated driving on two-lane, one way roads using model predictive control," in Intelligent Vehicles Symposium (IV), 2013 IEEE, pp. 1253-1258, 2013. [27] Y. Hongtao, et al., "A human-like game theory-based controller for automatic lane changing," Transportation Research Part C: Emerging Technologies, vol. 88, pp. 140-158, 2018. [28] Z. Marinho, et al., "Functional gradient motion planning in Reproducing Kernel Hilbert Spaces", arXiv preprint arXiv:1601.03648, 2016. [29] C. Dong, Y, Zhang, and J. M. Dolan, "Lane-change social behavior generator for autonomous driving car by nonparametric regression in Reproducing Kernel Hilbert Space," in Intelligent Robots and Systems (IROS), 2017 IEEE/RSJ International Conference, pp. 4489-4494, 2017. [30] M. Rahman, et al., "Evaluation of Driver car following Behavior Models for Cooperative Adaptive Cruise Control Systems," Transportation Research Record: Journal of the Transportation Research Board, vol. 2622, pp.84-95, 2017. [31] B. Ciuffo and V. Punzo, "Verification of traffic micro-simulation model calibration procedures: Analysis of goodness-of-fit measures," in 89th Annual Meeting of the Transportation Research Board, Washington, DC, 2010. [32] P. G. Gipps, "A model for the structure of lane changing decisions," Transportation Research Part B: Methodological 20, vol. 5, pp. 403–414, 1986. [33] T. Toledo, et al., "LCM with explicit target lane choice," Transportation Research Record: Journal of the Transportation Research Board, vol. 1934, pp.157-165, 2005. [34] P. Zhao, et al., "Analysis of mandatory and discretionary lane change behaviors for heavy trucks," arXiv preprint arXiv:1707.09411, 2017. [35] X. Li and J.Q. Sun, “Studies of vehicle lane changing dynamics and its effect on traffic efficiency, safety and environmental impact," Physical A: Statistical Mechanics and its Applications, vol. 467, pp.41-58, 2017. [36] M. Guo, et al., "Empirical study of LC behavior on three Chinese freeways," PloS one, vol. 13(1), e0191466, 2018.

BIOGRAPHIES OF AUTHORS Md. Mijanoor Rahman, current Ph.D student at school of mathematical science in Universiti Sains Malaysia (USM). Now he is working on Traffic flow network by using car following with some calibration and lane changing model. Currently he is an Assistant professor at mathematics department in Mawlana Bhashani Science and Technology, Tangail, Bangladesh. He has total Eight years teaching experience at different university. He completed his master’s degree and bachelor’s degree at mathematics department in Chittagong University, Bangladesh. He has previous eight publications in different journals. The authority of Mawlana Bhashani Science and Technology have given the Study leave with pay salary for doing his PhD with research. Dr. Mohd Tahir Ismail is Associate Professor and researcher in the School of Mathematical Sciences, Universiti Sains Malaysia (USM). The research area is on financial time series. Particularly he is keen in the modeling of structural breaks and analyses the economics issues. As of now, he has published more than 100 publications in reviewed journals and proceedings (some of them are listed in ISI, Scopus, Zentralblatt and other indices). His research grand is the extension of indicator saturation approach in GARCH Model to detect structural break and outliers: Empirical evidence in Malaysia Shariah-compliant Indices, FRGS, 2017-2019. Many trainings & conferences have been participated as International Seminar on Mathematics in Industry (ISMI2017), International Conference on Forecasting Economic and Financial Systems, Bodies & Professional is Malaysian Mathematical Sciences Society (PERSAMA), Malaysia Institute of Statistics (ISM). Dr Majid Khan joined the School of Mathematical Sciences, USM, as a lecturer (Operational Research) in May 2017. He is a researcher and appointed fellow working in the field of seaweed cultivation, solar drying systems, processing, modelling and simulation. His research uses application of Iot, big data and simulation methods to improve model predictions of moisture losses during drying in control and uncontrolled environment. He is also interested in modelling the problems in engineering and other biological systems such as tissue culture and aquamarine. He uses the techniques from statistical theory, approach and existing application tools to develop mathematical model and finally to transform the model in industry application and to answer a range of inspired questions.

Car following and lane changing behavior using NGSIM and China data (Md. Mijanoor Rahman)

International Journal of Advances in Applied Sciences (IJAAS) Vol. 8, No. 1, March 2019, pp. 26~33 ISSN: 2252-8814, DOI: 10.11591/ijaas.v8.i1.pp26-33

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Statistical analysis of night radiance RH using VIIRS day/night band satellite time series data Jyoti U. Devkota Department of Natural Sciences (Statistics), Kathmandu University, Nepal

Article Info

ABSTRACT

Article history:

Amount of night lights in an area is a proxy indicator of electricity consumption. This is interlinked to indicators of economic growth such as socio-economic activities, urban population size, physical capital, incidence of poverty. These night lights are generated by renewable and non renewable energy source. In this paper the behavior of night radiance RH data was minutely analyzed over a period of 28 hour; Visible Infrared Imaging Radiometer Suite Day/Night Band (VIIRS DNB) satellite earth observation data were used. These 28 hours and 8936 observations time series data is from 2 September 2018 to 4 September 2018. The behavior of night radiance RH data over 122 time intervals was analyzed using box plots. It was seen that the arithmetic mean of RH data is more sensitive than the arithmetic mean of first order difference of RH data. The first order difference of night radiance RH was regressed on night radiance over 110 intervals of time. The box plot of slope and intercept of this linear regression showed the behavior of these regression parameters over 110 intervals of time. It is seen that the data are more scattered with respect to slope than with respect to intercept.

Received Nov 21, 2018 Revised Jan 20, 2019 Accepted Jan 31, 2019 Keywords: Boxplot Linear regression Statistical analysis VIIRS satellite DNB data

Corresponding Author: Jyoti U. Devkota, Department of Mathematics, School of Science, Kathmandu University, P.O. Box 9303, Kathmandu, Nepal, Tel: 009779841539845. Email: drjdevkota@ku.edu.np, devkotajb@gmail.com

INTRODUCTION Night time light provides lighting in darkness. This night radiance is also an indicator of socio economic activities conducted in the night. As most of socioeconomic activities at night require light, the intensity of night time lights and the area covered by it correlate with socioeconomic activities and economic development. The brightness of indoor and outdoor night time light can be seen from the space due to satellite imagery. In areas lacking with data on economic accounting, night time satellite data can give us an estimate of economic growth. All vital inputs needed for survival and economic development of a society are provided by energy. Energy used in night time activity plays a very important role in this dynamics. Renewable and non renewable energies are sources to night time lights. These night lights illuminate bridges, streets, ship fleets, buildings etc. The data on night lights can be obtained from earth observation data of satellites. Researchers have found a positive correlation between night time light data and several socio-economic variables [1]. The free and inexpensive satellite time series data on night lights can thus be used for in-depth statistical analysis with vast interdisciplinary applications. Changes in population density can also be analysed using data on night time lights. Power outages in urban areas can also be spotted through these data. These power blackouts could be due to disasters and natural calamities. Increase in night time radiance implies population growth, economic growth and improved living standards.

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The research questions of this study are: 1) How does day and night band radiance RH behave over a real time interval of 28 hours? 2) How is it related to its first order difference? 3) What are the underlying trends and patterns in these two variables? The main contributions of this study are as follows: 1) Statistical analysis of behaviour of day and night band radiance real time RH data for a period of 28 hours; 2) Statistical analysis of behaviour of first order difference of this RH data; 3) Regression of first order difference of RH on RH data over 110 time intervals and analysis of behaviour of 110 values of slopes and Intercepts obtained of this 28 hour data. In this paper a detailed statistical analysis of dynamics of change in Night Radiance Data RH is done. Real time radiance data is monitored from 2 September 2018, 21:16 hours to 4 September 2018, 1:56 hours. This data is day and night band radiance satellite data. This is a detailed analysis of satellite data monitored for 28 hours. The results obtained here help us to identify underlying trends and patterns in the behaviour of this data. Energy consumption dynamics from renewable and non renewable sources used in night light illumination can be explained with these behaviours. The results obtained here can be used in analysis of long term time series day and night band radiance data. This paper is based on data products from the Visible Infrared Imaging Radiometer Suite (VIIRS) night time sensors. They are also called Day/Night Band, or DNB. Elvidge, Zhinzhin, Hsu and Baugh discussed satellite pyrometry at night and also gave background information about these VIIRS data [2]. Koel and Simon modelled crude birth rate and maternal mortality ratio of India using night time satellite images [3]. Mann, Melaas and Mallick used VIIRS/DNB to measure electricity supply reliability in Maharastra India [4]. Yu et al. claim that VIIRS data is a useful tool for evaluating poverty at county level in China [5]. Sharma et al. combined Moderate Resolution Imaging Spectroradiometer (MODIS)-based multispectral data with the Visible Infrared Imager Radiometer Suite (VIIRS)-based nighttime light (NTL) data for robust extraction and mapping of urban built-up areas [6]. For example, Dou, Liu, He and Hue used VIIRS data for urban land extraction [7]. This paper is arranged in the following manner. This section is followed by Methodology section titled Research Method highlighting statistical tools and the data used. This is then followed by sections on Results and Discussion and Conclusion. 2. RESEARCH METHOD 2.1. Data There are 8936 observations on time series of night radiance RH data measured by variable Radiative Output with unit W/m2. On different times ranging from 21:16 hours on 2 September 2018 to 1:56 hours on 4 Sptember 2018, there are 122 intervals of values taken by factor time. This satellite data is a product of National Aeronautics and Space Administration (NASA). They provide real-time imagery of our night time world. These imagery products are from the Visible Infrared Imaging Radiometer Suite (VIIRS) night time sensors. They are also called Day/Night Band, or DNB. This paper uses VIIRS DNB radiance data. DNB imagery has wide range of applications. Their primary purpose is to support short term weather predictions and disaster response community [8]. A number of sources contribute to the DNB signal, including city lights, lightning, fishing fleet navigation lights, gas flares, lava flows, and even auroras. When partial to full illumination from the moon is available, reflection of this lunar illumination off of ice, snow, and other highly reflective surfaces enable the study of ocean and terrestrial features. VIIRS is a scanning radiometer onboard the Suomi National Polar Partnership (SNPP) Satellite. The VIIRS collects visible and infrared imagery and radiometric measurements of land, atmosphere and oceans. It is sensitive to 22 wavelength bands, including a DNB with 750-m resolution. The DNB is sensitive to visible and near – infrared wavelengths ranging from daylight down to low levels of night time radiance. The ability of the DNB to detect the low levels of visible light present at night makes it well suited to studying night lights [4, 9]. VIIRS sensors have higher resolution and detect low light better than older Defence Meteorological Satellite Program Operational Linescan System (DMSP-OLS) system [10, 11]. 2.2. Analysis of 28 hours near real time data 2.2.1. Box plots Box plots provide visual summary of data in terms of quartiles and variance. Thus it is more powerful way of data representation than a tabular representation. By the use of boxplots important information can be communicated and absorbed in a glance. It uses the median, the approximate quartiles, and the lowest and highest data points to convey the level, spread, and symmetry of a distribution of data values [12]. Boxplots can also visually represent interrelationships between several variables.

Statistical analysis of night radiance RH using VIIRS day/night band … (Jyoti U. Devkota)

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ISSN: 2252-8814

2.2.2. Simple linear regression The dynamics of change in response variable y is studied and predicted using single regressor variable x. We assume that each observation y can be described by the model [13]. 𝒚 = 𝜷𝟎 + 𝜷𝟏 𝒙 + 𝜺 Here 𝜷𝟎 𝐢𝐬 𝐭𝐡𝐞 intercept. 𝜷𝟏 is the slope. Suppose that we have n pairs of observations say (𝒚𝟏 , 𝒙𝟏 ), (𝒚𝟐 , 𝒙𝟐 )…………. (𝒚𝒏 , 𝒙𝒏 ). Then these data may be used to estimate 𝜷𝟎 and 𝜷𝟏 . Here y is the first order difference of night radiance data and x is the night radiance data. 3.

RESULTS AND DISCUSSION The pattern of change in RH night radiance is minutely analysed. This analysis is not correlated with any other indicator of economic growth but studied here for its own benefit. Normally the study of satellite data is related to a development indicator. For example Letu, Nakajima and Nishio estimated the co2 emission by power plants using DMSP-OLS of VIIRS data [14]. Rybnikova and Portnov claim that VIIRS data gives more accurate results than DMSP –OLS data when finding correlation between incidence of breast cancer and artificial lights at night [15]. Similarly Shi et al. claim that VIIRS data are better in forecasting total freight traffic for China than DMSP-OLS data [16]. Satellite data can be used to monitor various parameters related to earth’s environment. Near real time monitoring of ecosystem using satellite remote sensing was proposed by Verbesselt, Zeileis and Herold [17]. These are 8936 observations on Time Series. On different times ranging from 21:16 hours on 2 September 2018 to 1:56 hours on 4 Sptember 2018, there are 122 values taken by the factor time. The behavior of Mean, Median, First Quartile (Q1) and Third Quartile (Q3) over 122 values of factor time is refelected in Figure 1. As seen from this figure and from the size of the boxplot, the variance of the average value over the 122 values of time is the higher than the variance of the median and Q1. The behavior of values in 65% confidence interval of the mean over 122 values of time is reflected in Figure 2. This figure also shows that the variance of the median values is much lower than the variance of the mean values. The aritnmetic mean is very sensitive to extreme observations in contrast to median and quartiles. So the size of the boxplot for sample mean over different values of time is larger than Medain, Q1 and Q3. The behavior of Mean, Median, First Quartile (Q1) and Third Quartile (Q3) of first order difference of Rh values over 122 values of time is shown in Figure 3. The behavior of these sample statistics over arange of 65% confidence interval is portrayed in Figure 4. The size of the boxplot corresponding to mean and median in Figure 3 and Figure 4 shows that for the first order difference there are very few extereme observations. Hence the size of boxplot is same in Figure 3 and Figure 4. It is seen that the average values of first order difference of night radiance time series data are very closely spread than the average value of night radiance data. The behavior of spread of the night radiance data over the range of 122 values of time factor is refelected in Figure 5. The pattern in the spread of first order difference is exhibited in Figure 6. The size of the boxplot corresponding to standard deviation SD is same with respect night radiance data and first order difference of night radiance data. This is standard deviation calculated over 122 values of time. The visual display of slope and intercept over 110 values of time is exhibited in Figure 7. This boxplot shows that while regression first order difference of night radiance data on the night radiance data for different values of time, the slope has more variance than intercept. This implies that the intrinsic value of first order difference is concentrated around 0. But the rate of change in first order difference for a unit change in RH has a larger spread. The probability density of slope and intercept is exhibited in Figure 8 and Figure 9. The probability density of mean radiance over different values of time factor is exhibited in Figure 10. The probability density of mean of first order difference night radiance data is exhibited in Figure 11. The autocorrelation and partial autocorrelation of the night radiance time series data is given in Table 1. All the values obtained by correlating 8936 values are highly significant.

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Figure 1. Mean, median, first quartile and third quartile of night radiance over 122 values of time

Figure 2. Mean, median, first quartile and third quartile of night radiance over range of 65% confidence interval

Figure 3. Mean, median, first quartile and third quartile of first order difference night radiance data over 122 values of time

Statistical analysis of night radiance RH using VIIRS day/night band … (Jyoti U. Devkota)

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Figure 4. Mean, median, first quartile and third quartile of first order difference night radiance data over range of 65% confidence interval

Figure 5. The spread of night radiance rh data in terms of standard deviation and range

Spread of the Data

Range

First Order Difference of Night Radiance RH Time Series Data

100

150

200

250

300

RH in W units

Figure 6. The spread of first order difference of night radiance rh data in terms of standard deviation and range

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Figure 7. The slope and intercept of linear regressions over 122 values of time

Figure 8. The probability density of slope over 110 values of time

Figure 9. The probability density of intercept over 110 values of time Statistical analysis of night radiance RH using VIIRS day/night band … (Jyoti U. Devkota)

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0.15 0.00

0.05

0.10

Density

0.20

0.25

0.30

Probability Density of Mean

N = 122 Bandwidth = 0.4561

Figure 10. The probability density of mean radiance over 122 values of time

4 0

Density

Probability Density of Mean of First Difference

-20

N = 122 Bandwidth = 0.01927

Figure 11. The probability density of mean of first difference radiance over 122 values of time Table 1. The autocorrelation and partial auto correlation of Night Radiance RH data Lag Autocorrelation Partial Autocorrelation

1 0.234 0.234

2 0.173 0.125

3 0.186 0.130

4 0.109 0.03

5 0.087 0.022

6 0.092 0.037

7 0.055 0.033

CONCLUSION The results obtained in this paper are not means to an end but are complete on its own right. In this paper behaviour of VIIRS day and night band radiance RH is minutely explored. This is based on 28 hour time series data from 2 September 2018 to 4 September 2018. It is seen that the arithmetic mean of night radiance has more spread than the median and the quartiles. This is due to the fact that arithmetic mean is more sensitive to extreme observations. But the mean of first order difference is less sensitive to extreme observations. The size of the box plot of arithmetic means and medians for first order difference are same. Int. J. of Adv. in Appl. Sci. Vol. 8, No. 1, March 2019: 26 – 33

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This shows that it better to handle first order difference of RH data than the RH data itself. These values have been obtained over 122 values of time. First order difference of night radiance time series data is regressed over the time series night radiance data. The intercept of this simple linear regression conducted on 110 values of time shows the inherent value taken by the first order difference. The slope of this simple linear regression shows the unit change in first order difference of RH over a unit change in the night radiance RH time series data. From the box plot of slope and intercept, it is seen that the spread of intercept is much less than that of slope. The probability density of mean of first order difference of RH is symmetric. The autocorrelation of 8936 time series data is highly significant. The partial autocorrelation is also highly significant. The probability distribution of slope and intercept gives an idea on the application of random effects models. These models will be explored in future research. But this detailed statistical analysis of such data can have vast interdisciplinary applications, especially for countries with limited and scarce data. The effect of night lights is a proxy to electricity consumption and it permeates into diverse fields ranging from population, disaster management to economics. The intensity of night lights in an area can be taken as an indicator of socioeconomic activities conducted in that area. Such lights are generated by renewable and non renewable energy sources and they illuminate areas inhabited by human population. Thus night radiance satellite data can be used in analysis of energy consumption dynamics of such areas. The intensity of night lights is highly correlated to the socioeconomic activity conducted in that area. The statistical analysis of night radiance data is related with the statistical analysis of energy consumption. REFERENCES [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17]

World Bank Group, Night time lights revisited. Policy Research Working Paper Report 7496, 2015. C. Elvidge, M. Zhinzhin, F. Hsu, and K. Baugh. “VIIRS Nightfire: Satellite Pyrometry at Night,” Remote Sens, DOI: 10.3390/rs5094423, vol. 5, pp. 4423-4449, 2013. K. Roychowdhury and S. Jones, “Nexus of Health and Development: Modelling Crude Birth Rate and Maternal Mortality Ratio Using Night time Satellite Images,” ISPRS Int. J. Geo-Inf, vol. 3, pp. 693-712, 2014. M. Mann, E. Melas, and A. Malik, “Using VIIRS Day/Night Band to Measure Electricity Supply Reliability Preliminary Results from Maharashtra,” India, Remote Sens, vol. 8, pp. 7-11, 2016. B. Yu, K. Shi, Y. Hu, C. Huang, Z. Chen, and J, “Wu. Poverty Evaluation Using NPP-VIIRS Nighttime Light Composite Data at the County Level in China, “IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 8(3), pp. 1217-1229, 2015. R. C. Sharma, R. Tateishi, K. Hara, S. Gharechelou and K. Iizuka, “Global mapping of urban built-up areas of year 2014 by combining MODIS multispectral data with VIIRS nighttime light data,” International Journal of Digital Earth, vol. 9(10), pp. 1004-1020, 2016. Y. Dou, Z. Liu, C. He and H. Yue, “Urban Land Extraction Using VIIRS Night time Light Data: An Evaluation of Three Popular Methods,” Remote Sens, vol. 9, pp. 175, 2017. [Online]. Available: https://earthdata.nasa.gov/viirs-dnb. C. Elvidge, M. Zhinzhin, F. Hsu, and K. Baugh, “What is so great about nighttime VIIRS data for the detection and characterization of combustion sources,” Proceedings of Asia Pacific Network, vol. 35, pp. 33-48, 2013. W. Guo, D. Lu, Y. Wu, and J. Zhang, “Mapping Impervious Surface Distribution with Integration of SNPP VIIRSDNB and MODIS NDVI data,” Remote Sens, vol. 7(9), pp. 12459-12477, 2015. C. D. Elvidge, K. Baugh, M. Zhizhin, F. C. Hsu and T. Ghosh, “VIIRS night-time lights,” International Journal of Remote Sensing, pp. 5860-5879, 2017. D. F. Willaimson, R. A. Parker and J. F. Kendrick, “The Box Plot: A Simple Visual Method to Interpret Data,” Annals of Internal Medicine, vol 110(11), pp. 916-921, 1989. W. Hinnes, D.C. Montgomery, D. M. Goldsman and C. M. Borrer. Probability and Statistics in Engineering, IVth edition. John Wiley and Sons, Inc, pp. 409-436, 2003. H. Letu, T.Y. Nakajima and F. Nishio, “Regional-Scale Estimation of Electric Power and Power Plant CO2Emissions Using Defense Meteorological Satellite Program Operational Linescan System Nighttime Satellite Data,” Environ. Sci. Technol. Lett, vol. 1(5), pp. 259–265, 2014. N. A. Rybnikova and B. A. Portnov, “Outdoor light and breast cancer incidence: a comparative analysis of DMSP and VIIRS-DNB satellite data,” International Journal of Remote Sensing, pp. 5952-5961, 2017. K. Shi, B. Yu, Y. Hu, C. Huang, Y. Chen, Y. Huang, Z. Chen and J. Wu, “Modelling and mapping total freight traffic in China using NPP-VIIRS nighttime light composite data,” GIScience and Remote Sensing, vol. 52(3), pp 274-289, 2015. J. Verbesselt, A. Zeileis and M. Herold, “Near real-time disturbance detection using satellite image time series,” Remote Sensing of Environment, vol. 123, pp. 98-108, 2012.

Statistical analysis of night radiance RH using VIIRS day/night band … (Jyoti U. Devkota)

International Journal of Advances in Applied Sciences (IJAAS) Vol. 8, No. 1, March 2019, pp. 34~43 ISSN: 2252-8814, DOI: 10.11591/ijaas.v8.i1.pp34-43

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A categorical data analysis of impact of biogas on women of rural households – with examples from Nepal Jyoti U. Devkota Department of Natural Sciences, Mathematical Science Group, School of Science, Kathmandu University, Nepal

Article Info

ABSTRACT

Article history:

This paper is based on primary data collected from 400 households of biogas consumers. A detailed structured questionnaire was developed and pretested. Here response to each question was given as a multiple choice option resulting in more than 450 categorical data. These variables studied various aspects of households using biogas. The results focused on women of these households. Interrelationships between several other variables including the role of a woman in various decisions related to the plant were analyzed. Logistic regression of decision making of woman on several variables including asset index of the family was done. This asset index was calculated from the principle component analysis of socio-economic variables. The family dynamics in the choice of biogas as renewable energy source was quantified using odds ratio and regression coefficients. The interdependence between variables was tested using Chi square test of Independence of Attributes. A novel data based approach of generation and analysis of categorical data is demonstrated here. The suitability of generation of categorical data in the absence of accurate measurement instruments is highlighted. This method is also suitable for countries without a strong backbone of good quality official records, and provides a good backup data for official statistics.

Received Nov 18, 2018 Revised Jan 20, 2019 Accepted Jan 31, 2019 Keywords: Biogas Logistic regression Odds ratio Principal components analysis Renewable energy Women empowerment

INTRODUCTION Energy and women are very closely linked to one another. Finding fuel or energy for cooking is a woman’s responsibility. Among economically weaker sections of countries like Nepal, energy is like water as it plays a key role in cooking. Here, women are supposed to find fuel on daily basis. As firewood is primarily used for cooking in rural areas of Nepal, a lot of time and energy is spent in the collection of firewood. Agriculture based economy calls for labor intensive work. Women's contribution as a labor force is mainly focused on farm and domestic activities. Subsistence agriculture based Nepalese economy has an involvement of about 50% females, 44% males and 6% children between 10 - 14 years [1]. In all areas of Nepal the work burden for women is higher than for men with the highest work burden found amongst women in the mountains. A significant proportion of time of women here is spent in collecting firewood and fodder, indicating the depleted resource base in the mountain areas. According to census 2011, 19.72% of household reported the ownership of land or house or both in the name of female family members [2]. In urban areas, 26.77% of households show female ownership of fixed assets while the percentage stands at 18.02 in rural areas.

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According to 2011 Census, female literacy rate in the five years and above age group was reported to be 53.0% in rural area and 75.3 % in urban area [2]. The literacy rate in Nepal is 75.2% for males and 57.4% for females. Further the number of women with SLC (school leaving certificate) and above constitutes only 18% of the total female population of six years of age and above. The comparable figure for man in the same age group was 22%. In urban areas 40% percent of the men and 35% of the women had this level of education. Biogas plant, a sustainable energy system has great potential in Nepal. Total production potential from this source in the year 2008/09 is about 43 million GJ which is about 11% of the total energy demand of the same year. Potential of biogas availability in the country is about 81% of the total biogas production. The availability factor of the biogas production in the Terai, Hills and Mountain region is 100%, 75% and 50% respectively. The country still has potential for 2.9 million biogas plant. This estimate is based on cow dung feed. If other sources are also incorporated this estimate will increase [3]. According to World Bank the per capita energy consumption of Nepal in 2009 was 88kWh. Power outages of up to 12 hours per day from the national grid reflect the sorry state of energy sector. The gross domestic product (GDP) of Nepal in 2009 was 12.85 billion US dollars [3]. Energy plays a key role in the economy. Women are major workforce of rural agrarian economies. Hence the cause and effect of biogas on women needs to be quantified. There are intangible effects of renewable energy use and these effects have to be studied in evidence based manner. Statistical methods have a potential of being used in diverse fields. For example, Logistic regression, discriminant analysis and neural networks were used in predicting the presence of a species of Himalayan bird by Manel et al. [4]. Logistic regression analysis of two retrospective series of out-of-hospital cardiac arrests was performed by Valenzuela et al. [5]. Hebert et al. [6] applied principal components analysis (PCA) to reduce the number of predictor variables with minimal loss of information. A survey using a structured questionnaire to collect data on socio-economic characteristics and malaria beliefs and practices among more than 400 net-owning and nonnet-owning households was conducted by Howard et al.[7]. They were divided into socio-economic quartiles. Vyas et al. [8] have applied PCA to Brazil and Ethiopia demographic health survey data and also reviewed various issues related to choice of variables and data preparation. Filmer et al. [9] studied wealth by constructing a linear index from asset ownership indicators, using principal components analysis to derive weights. Different methods to be adopted to improve the running of PCA on discrete data were suggested by Kolenikov et al. [10]. Polonsky et al. [11] elaborated on household wealth scores based on ownership of assets generated using principal components analysis. Logistic and poisson regression analyses were performed to identify the determinants of health facility utilization and equity of access across socio-economic strata. Mwageni et al. [12] used asset-based wealth index based on PCA to determine the relationship between household socio-economic characteristics and inequalities of access to health interventions, and to health outcomes in rural Tanzania. Devkota and Singh modeled the mortality and fertility data of developing countries especially from Nepal by using deterministic and mixed effect models [13, 14]. Like many counties in South Asia and Africa, Nepal suffers from limited and scarce official records. Devkota discussed several problems associated with the continual collection of vital statistics especially in countries with limited and scarce data [15]. Devkota explains the importance of minimum error data and its digitization by giving examples from Biogas Consumer Profile database [16]. The undefinable but very important effect of renewable energy on improving lives of women is analysed here with the help of several statistical methods. Very crucial effects that cannot be measured in quantities are analysed. This approach is in contrast to use of evidence based methods in economic and other exact sciences. For example Court V. discussed that mainstream economics still does not recognize the crucial role that energy plays in the economic process [17]. He reiterated the importance of reformulation of a clear and in-depth state of knowledge provided by a thermo-evolutionary perspective of the economic system. King C. stated important implications for economic modeling [18]. He stated that firstly fundamental shifts in resources costs relate to economic structure and economic growth. Secondly that the number of sectors used to represent economic transactions influences the systemic metrics themselves. Parkinson et al. developed a multi-criteria analysis approach enabling the specification of diverse decision-making preferences [19]. This approach was illustrated with a case study of the water-stressed nation of Saudi Arabia. Here using this approach, preferences combining aspiration and reservation levels in terms of cost, water sustainability and electricity sector CO2emissions were modeled. In energy research and in other interdisciplinary fields statistical methods can be applied. Logan L. et al. [20], created an adaptable, novel methodology that utilizes plume mixing and probability distribution to analyze temperature and flow data for both a power plant’s discharge and the adjoining river. To assess risk, a probability risk space that quantifies the probability of exceeding a given temperature was also developed [20]. Adio et al. [21] studied the effect of magnetic activated carbon on on absorption of chromium and thalium using statistics [21]. A categorical data analysis of impact of biogas … (Jyoti U. Devkota)

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The remainder of this paper is arranged in following manner. Section 2 is called Research Methods. Here the significance of Principal Components Analysis and Logistic regression are explained. Theoretical background to these techniques is provided. This is followed by Result and Analysis Section 3. This s ection discusses about the data, where dataset that motivates this study is described. Here, steps followed during the design as well as implementation of the survey are elaborated. The design and construction of Biogas consumers profile database is explained. It is followed by Section 4 called conclusion. 2.

RESEARCH METHOD Multivariate analysis is one of the techniques that analyses the interdependence between multiple variables governing a phenomenon. As mentioned by Hair et al. [22], multivariate analysis methods are not only related to analytic aspect of research but also to the design and approach to data collection for decision making and problem solving. In the case of multivariate data where several variables might be interrelated and the true information of the data might be disturbed due to this multi-collinearity. 2.1. Principal component analysis It is one of the methods of Multivariate Analysis. Principal components analysis retains the variability of the original data by transforming the correlated variables into fewer orthogonal variables. In this statistical approach, the interrelationship among a large number of variables can be analyzed. The techniques focuses on condensing information contained in a large number of original variables into a smaller set of variables (factors) with minimum loss of information. This data summarization helps identify the underlying dimension or factor, estimates of factors and contribution of each variable to the factors (termed loadings). Un rotated factor matrix comprising of factor loadings is used when the main objective of research is in best linear combination of variables where the a particular combination of original variables account for more of variance in the data as a whole than any other linear combination. The theoretical background of Principal Components Analysis is the following. Suppose we have a set of N variables, a1j* to aNj*, representing the ownership of N assets by each household j. Further, let us standardize each variable by its mean and standard deviation; for example, ∗

𝑎 =

∗ ∗

, where

𝑎∗ is the mean of 𝑎∗ across households and 𝑠 ∗ is its standard deviation. These

selected variables are expressed as linear combination of a set of underlying components for each household j:

𝑎∗ = 𝑣

∗ 𝐴 +𝑣

∗𝐴

+ ⋯ +𝑣

𝑎∗ = 𝑣

∗ 𝐴 +𝑣

∗𝐴

+ ⋯ +𝑣

∗𝐴 ∗𝐴

(1)

Where, 𝑗 = 1, … . 𝐽 A’s are the components and v’s are the coefficient on each component for each variable. The "scoring factors" from the model are recovered by inverting the system implied (1), and yield a set of estimates for each of the N principal components:

𝐴

=𝑓 ∗𝑎

+𝑓 ∗𝑎

𝐴

=𝑓

+𝑓

∗𝑎

+ ⋯+ 𝑓

∗𝑎

+ ⋯+ 𝑓

∗𝑎 . ∗𝑎

(2)

j = 1...J. The first principal component, expressed in terms of the original (un normalized) variables, is therefore an index for each household based on the expression ∗

𝐴

=𝑓 (

∗

∗ ∗

)+... +𝑓

(

∗ ∗

)

With respect to this study there are 47 asset variables and 400 households. So N = 47 and J = 400

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2.2. Multinomial logistic regression Similarly logistic regression helps predict the probability of occurrence of a non metric dependent variable where the independent variables may be metric, non metric or both. It is another method used in multivariate analysis. It is used to predict categorical placement in or the probability of category membership on a dependent variable based on multiple independent variables. It has multiple categories’ of independent and multiple categories of dependent variables. Binary logistic regression is a special case of multinomial logistic regression. It allows for only two categories of the dependent or outcome variable but multiple categories of independent variables. Multinomial logistic regression uses maximum likelihood estimation to evaluate the probability of categorical membership. Let J denote the number of categories of Y. Here Y is a multinomial response variable. Let {π1, π2, πj} denote the response probabilities, satisfying the condition that their sum is equal to 1. Logit models for multinomial response pair each category with a baseline category [23]. When the last category (J) is the baseline, the baseline-category logits are log (πi/πj), j = 1J-1 The main difference between Binary Logistic Regression and Multinomial Logistic Regression is that for Binary logistic regression the dependent variable takes two values so, i = 1, 2 . But for Multinomial Logistic Regression i = 1, 2, I Given that the response falls in the category j or J, this is the log odds that the response is j. The baseline category logit model with predictor x is log (πj/πj) = αj + βjx, j = 1, J-1 The model has J-1 equations with separate parameters for each. The effects vary according to the category paired with the baseline. When J = 2, this model simplifies to single linear equation for log (π 1/π2) = logit(π1), resulting in ordinary logistic regression for binary responses. There is an odds ratio associated with each predictor. It is denoted by Exp(B). It is more than 1 in cases where predictors increase the logit, Exp(B) is equal to 1 in cases where predictor don’t have any influence on the logit and Exp(B) is less than 1 in cases where predictors decrease the logit. 3. RESULTS AND ANALYSIS 3.1. Data A detailed survey of 400 households of biogas consumers was conducted. The questionnaire comprised of 59 questions with response provided as a multiple choice option. This resulted in a categorical data that could be analyzed on an ordinal scale. Due to large sample size of 400 households these ordinal data can be treated as continuous. This is by the application of Central Limit Theorem. There were 467 variables related to the response to these 59 questions. The information was stored in a consumer profile data base of biogas users [16]. There were 2272 individuals living in these 400 households. Information was collected and stored on age distribution, land holdings, livestock, and fuel wood expenses before and after the plants, their assets and decision making power. So with before and after structure of the questions, information could be obtained about the households that haven’t installed biogas. 3.2. Results Woman’s role in decision making is closely related to the socioeconomic status of the household. So the questions assessing socioeconomic status held special importance here. But questions related to socioeconomic status are of sensitive nature. If such questions are asked directly, the chances of getting correct answer are very low. Thus questions indirectly assessing the income group and related to material holdings were asked. These are called proxy asset indicator and the response of the interviewee can verified by observation. The variables related to the ownership of assets like land holding, private ownership of water sources for drinking, bathing, irrigating, possession of cars, tractor, bicycle, radio, location of toilet, amount of loan incurred are considered as proxy indicators of the economic prosperity. Further, biogas consumers in Nepal and also in this study are primarily farmers located in rural settings of Nepal. In such settings most of the economic transactions are carried out outside the market. Thus, direct questions on income earned fails to reflect the socio-economic scenario of the respondents. Structured questions on proxy asset indicators were designed in the following manner. The four groups of the questions comprised namely of the first group with 8 questions relating to asset ownership which comprised of land, house, electronic equipments such as computer, television, radio, mobiles, telephones and refrigerator, means of transportation. There were 10 indicator variables in this category. Characteristics of house dwellings and toilet were the second group and comprised of 6 questions and 10 indicator variables. Here questions on materials used in the construction of house were asked. Open latrine, latrine far away from the house or close to the house etc were queries related to the type of latrine. Water source and needs was the third group with 6 questions and 20 indicator variables. Detailed question on the use of water such as for bathing or cooking etc were asked. Different sources of water such as private well, open well, community water supply etc were asked. Fourth and the last group was A categorical data analysis of impact of biogas … (Jyoti U. Devkota)

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the amount of land owned and comprised of 5 indicator variables. Then the descriptive statistics of the variables including mean, standard deviation and correlation were calculated. This gave an idea on the average value of the variable per household and its spread across the household. Since most of the asset variables data are 0 or 1 as they were in response to yes/no questions. Here absence was labeled as 0 and presence was labeled as 1. Thus the binary data can be labeled ordinal scale. Here the mean of most common asset variable is close to 1 and the standard deviation is very low for example in response to the yes/no question of Do you have a latrine?, the mean is 0.97 and the standard deviation is 0.17. Whereas in response to the yes/no question Do you have a well built latrine?, the mean is 0.93 and standard deviation is 0.26. This suggests that although latrine is almost universal among the consumers well built latrine is somewhat less and not so common. The data were mainly ordinal and ratio in nature. The large sample size of 400 households supports the assumption of normality by the application of Central Limit Theorem. The decision making power of a woman in the household is dependent on its socioeconomic standards which is quantified using principle components analysis [22]. The interdependence of decision making of woman on the socio economic background, educational qualification, involvement of work of other family members is predicted using logistic regression. Here the results of principle components analysis calculated in terms of asset index are incorporated as independent variable. Out of 467 variables, 47 variables were identified as proxy asset indicators. These variables are classified under different headings mainly classified in six categories namely ownership of fixed assets, type of house, type of toilet, source of water, ownership of land, loan. Principle components analysis of these 47 asset variables extracted 10 components. They explained 60% of the total variance [22]. Using linear combination of three main principal components the 400 households are divided into three groups. This is on the basis of asset index. This is a linear combination of factor loadings and normalized asset ownership variables. Here rich comprised of top 20% of society, middle income group of middle 40% and the lowest 40% are economically most deprived. The robustness of this classification is tested by the data on those assets which are conventionally owned by people who are more economically well off. Thus the dimensionality of the data comprising of 47 interrelated asset variables is reduced to 10 orthogonal variables. It is also seen that ownership of own water source is the main factor differentiating economically well off households with poor households. The overview of various statistical methodologies used here is explained in Figure 1.

Figure 1. Flow chart explaining the research methodology of this paper In this figure the various steps summarized above are explained with the help of flow diagram. The involvement of women not only in decision making but also in the work associated with the plant is illustrated from Figure 2 – Figure 6. Initiation of plant construction in more than 50% of households (227) was done by husband also portrayed by Figure 2. In 87 households which is 21.8% of the total, initiation of plant construction was done by wife. This is two times more that of wife. It was followed by initiation by son Int. J. of Adv. in Appl. Sci. Vol. 8, No. 1, March 2019: 34 – 43

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in 20, daughter in 5, and other male members in 19, other female members in 11 and by collective decisions in 31 households. This shows the crucial role of male members in the initiation of plant construction. As shown in Figure 3 that about 81.8% of female were actively involved in the construction and operation of the biogas plant. As reflected in Figure 4, payment for the biogas was made by the husband in 341 households that is 85.3% followed by other male members 41(10.3%). The number of households with wives making payment was found to be only in 16 household that is 4%. It showed, generally male members are the ones making financial decisions on a source of renewable energy. The involvement of the women was found to be less than male by almost 21%. Most of the households had their biogas plants registered under husband's name, which had the frequency of 254 household (63.5%), as reflected in Figure 5. This was three times more than that of the number household that has it registered in wives names(22.8%) followed by other male members 43 (10.8%). In more than 50% of household, biogas was registered under the name of the husband. Plants registered under male members exceed that of female by more than two times. Equal participation by both genders in the decision making is shown in Figure 6. The gender differential with respect to age and level of education among 400 households of biogas consumers are shown in Table 1. The difference in the educational background between male and female is dependent on the socio-economic status. It can be seen that male members are more actively involved in education. This is in comparison to their female counterparts. The socio-economic standard of biogas consumers with respect to ownership of material assets in comparison to the census 2011 data of rural households is shown in Table 2. A comparison between the material assets owned by 400 households of biogas consumers is compared in results obtained from census 2011. The ownership of refrigerator is only 3.3% of the households in rural Nepal [1], whereas it is 4% of households among biogas consumers. Televisions are owned by 81.25% of the households of biogas consumers inhabiting in rural areas which is much higher than 30.7% of household inhabiting in rural Nepal. The data of asset ownership of biogas consumers is based on the survey of biogas consumers done as part of this research. The biogas consumers are economically well off in comparison to the households inhabiting in rural Nepal. Gender wise comparison of among the members of 400 households with respect to education is given in Table 3. As seen from Table 3, the gender wise difference for example in the age group 6 – 16 in the level of education is not significant. The proportion of male members in these 400 households going to school is 0.8034. The proportion of female members in 400 households is 0.8239. It is seen from Table 3 that at younger ages the difference with respect to education is not significant. But at older ages this difference is significant. For example, the gender wise differentials among students passing class 10 board examinations in age group 26-50 years and 50-75 years are highly significant. The dependence between socio-economic status and role of wife in a family in deciding for a biogas plant is given in Table 4. It is seen that in 358 households, husband or wife played a key role deciding for a biogas plant. As seen from Table 5; 55, 81 and 38 wives in poor, middle and rich income groups played a key role in deciding about the plant. The dependence between socio-economic status and role of wife in keeping profits gained by using biogas plant is given in Table 5. It is seen that in 340 households, husband or wife played a key role deciding for a biogas plant. As seen from Table 5; 21, 36 and 7 wives in poor, middle and rich income groups play a key role in keeping profits from the plant. Similarly, Table 4 and Table 5 are related to woman empowerment. The role of woman in that particular consumer household in deciding for the construction of plant and her involvement in the decision making and other activities of the household, is tested using data given in Table 4 and Table 5. The dependence of decision making power of a woman in household on the socio-economic classification is then tested with the help of chi-square test of independence of attributes. There is dependence between the income groups and decision for the construction of biogas plant by wife with p = 0.02. There is dependence between the income groups and profit made from the construction of biogas plant kept by wife with p value = 0.02. The decision making power of a woman regarding a source of renewable energy is mathematically quantified. Logistic regression is used in this assessment and quantification of dynamics of change is done by odds ratio and regression coefficients. Answers to several questions were given as independent variables. They were namely who initiated the construction of the plant, who was involved in construction of plant, who made payment for the construction, under whose name is it registered, and who decided for the plant. Options such as husband, wife, son, daughter, other male member, other female member were provided. This classification of income group denoted by pcases is output of principle components analysis [4]. It is used as independent variable is used in this logistic regression. The results of this binary logistic regression are provided in Table 6. The probability of a wife deciding in favor of biogas is predicted on the basis of income group. Decision making power of husband or wife is regressed on economic status using logistic regression. So the economic status is an ordinal, multinomial independent variable. Decision of husband or wife is a yes/no binary dependent variable. Probability of a wife deciding for a plant was regressed on all the A categorical data analysis of impact of biogas … (Jyoti U. Devkota)

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variables. Backward elimination of the independent variables was done. Here income classification represented by pcases is an ordinal data with 3, 2 and 1 representing the rich, middle income and poor respectively. A variable named total animal generates ratio data. Distance of more than 500m covered is denoted by the variable name mnore500. It is for the collection of firewood after the construction of plant. Redfuelm1 is a binary yes/no data for reduced fuel expenses by male members. Similarly redfuelf1 is response by female members. Redfuelmf1 is a binary yes/no data for male and female. District is the nominal data for the districts where 3 stands for Bhaktapur district, 2 stands for Simara and 1 stands for Sarlahi. All these variables represent post plant construction phase. As seen from Table 6 the odds of wife deciding for the construction of biogas plant decreases by half from rich income group to poor income group (p value = 0.014) whereas the odds of wife deciding for the construction of biogas is same for the middle income group. Here the last category is the reference category. The decision for a plant by a wife or a husband is dependent on the socio-economic status with p value = 0.005. This implies that middle income group and high income group women are equally likely to decide for the construction of biogas plant in comparison to low income group. In lower income group the chances that a woman decides for a plant decreased by 50%. Here among 358 selected households 139 belong to low income group, 147 to middle income group and 72 to rich income group. Similarly for those households which still cover more than 500m the odd in favour of a wife deciding for a plant is 2.7 times more than that of the husband with p value 0.001. The chances of husband deciding for a plant are higher when the total number of cattle (total animals) increases (p value 0.007). Thus households with cattle have higher probability of husband having a greater say in the decision making process.Similarly the odds of a wife deciding for a plant are six times and three times in Simara and Sarlahi respectively in comparison to Bhaktapur. If a male responds in affirmative to reduced expenses the odds of a wife deciding for the plant increases by 2.6 times. The accuracy of the model is 63.1%.

Figure 2. Initiation for biogas plant construction

Figure 3. Female involvement in biogas plant construction

Figure 4. Female involvement in payment for biogas plant construction

Figure 5. Female involvement in registration biogas plant construction

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Figure 6. Female involvement in decision making biogas plant construction Table 1. Gender wise comparison of education in 400 households of biogas consumers Age

Neither read nor write Male Female 0-5 49 30 6-16 8 10 17-25 6 10 26-50 83 174 50-75 164 178 75+ 10 19 Source: Sample survey 400 households

Education School Going Passed Class 10 Board Exams Male Female Male Female 27 21 204 191 35 18 34 45 92 62 92 93 100 58 32 8 8 2 3 0 0 0

Above Class 10 Male Female 24 17 128 92 81 37 6 0 0 0

Table 2. Material assets owned by biogas consumers with reference to data of 2011 census Material Assets Motorcycle Cell Phone Rural Households-Census 2011 6.28 60 (in percentage) Biogas consumers 13.5 87 (in percentage) Source: Census data2011 and Sample survey 400 households

Radio 50

Refrigerator 3.3

Cycle 33

Television 30.7

Computer 3.4

4.0

81.25

3.5

Table 3. Gender wise comparison with respect to level of education among biogas consumers at 5% level of significance Education School going 6-16years Class 10 board exams passed 6-16 years Above class 10, 6-16 years School going 17-25years Class 10 board exams passed 17-25 years Above class 10, 17-25 years School going 26-50 years Class 10 board exams passed 26-50 years Above class 10, 26-50 years School going 50-75 years Class 10 board exams passed 50-75 years Above class 10, 50-75 years Illiterate 17-25 years Illiterate 26-50 years Illiterate 50-75 years Illiterate 75+ years Source: Sample survey 400 households

Proportion Male 0.8034 0.156 0.104 0.151 0.413 0.576 0.298 0.312 0.214 0.159 0.04 0.03 0.035 0.271 0.777 0.714

Proportion Female 0.8239 0.1006 0.904 0.21 0.33 0.481 0.263 0.167 0.093 0.086 0.011 0 0.064 0.529 0.92 1

Z test for proportions -0.4788 1.5018181 0.3042 -1.3803 1.5506 1.7217 0.9516 4.1664 4.127 2.1872 1.84 2.486 -0.6082 -5.265 -3.9015 -2.427

Conclusion Difference not significant Difference not significant Difference not significant Difference not significant Difference not significant Difference not significant Difference not significant Difference is significant Difference is significant Difference is significant Difference not significant Difference is significant Difference not significant Difference is significant Difference is significant Difference is significant

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Table 4. Socio-economic status versus decision making of biogas plant Decision Making Income Group Husband Wife Poor 84 55 Middle Income 66 81 Rich 34 38 Total 184 174 Source: Sample survey 400 households

Table 5. Socio-economic status versus profit made biogas plant Decision Making Income Group Husband Wife Poor 114 21 Middle Income 105 36 Rich 57 7 Total 184 174 Source: Sample survey 400 households

Total 139 147 72 358

Total 135 141 64 340

Table 6. Results of fitting logistic regression Independent Variables pcases1 pcases1(1) pcases(2) Total Animals Amore500 District District (1) District (2) redfuem1 redfuef1 redfuef Constant

-0.902 -0.105 -0.161 0.978

0.366 0.342 0.059 0.291

1.295 1.814 0.972 1.000 0.719 -1.378

0.664 0.611 0.360 0.555 0.403 0.761

WALD 10.635 6.075 0.095 7.335 11.277 9.964 3.802 8.805 7.305 3.254 3.182 3.28

df 20 1 1 1 1 2 1 1 1 1 1 1

Sig 0.005 0.014 0.758 0.007 0.001 0.007 0.051 0.003 0.007 0.071 0.074 0.07

Exp(B)

95% Confidence Interval for B Lower Upper

0.406 0.900 0.851 2.66

0.198 0.460 0.758 1.503

0.831 1.760 0.957 4.708

3.651 6.137 2.644 2.719 2.053 0.252

0.993 1.851 1.306 0.917 0.931

13.417 20.344 5.351 8.065 4.524

CONCLUSION This paper studies the cause and effect of renewable energy in general and biogas in particular in uplifting the status of women. The intangible effect of use of energy in uplifting the lives of women in rural agrarian economy is measured with the help of several statistical methods. The entire work done can be summarized in the following manner. First survey of 400 households of biogas consumers was conducted; it was followed by database construction. Then the trends and patterns in this multivariate data were explored and analyzed by exploratory data analysis. This was followed by principal components analysis of 47 assets variables. Then in this paper, this result of asset variables were used in quantifying the decision making power of women through logistic regression. The interrelationship between socio-economic status and the empowerment of a woman in terms of decision making and profit keeping is measured. First the dependence between these attributes is explored with the help of chi-square test of independence of attributes. Then probability of a wife deciding for a plant is regressed on several variables including socio-economic status measured by asset index, number of animal. The impact is quantified with odds ratio. The use of biogas saves the time of women by mean value of 65 minutes daily. The modal value is 1-3 hours per day. This time was spent on collection of firewood and in the kitchen. This time saved is used by the women in teaching children at home and in other income generating activities. Firewood based kitchen have adverse impact on health. A switch over to smoke free kitchen by using biogas is good for the health of women. The women reaped financial benefits by engaging in income generating activities like livestock rearing and working in the farm. There were fewer cases of diseases related to respiratory tract due to the use of smoke free kitchen. The use of biogas uplifts the status of women by giving them time for income generation. ACKNOWLEDGEMENTS This work is funded by NORAD, SINTEF under Renewable Nepal Project grant number RENP-1006-PID-379 and RNB -15-PID-01. NORAD is Norwegian development agency and SINTEF is a Norwegian energy institute. REFERENCES [1] CBS, Nepal. Women in Nepal some statistical facts, Central Bureau of Statistics, Kathmandu, Nepal, 2004. [2] CBS, Nepal. National Report on National Population and Housing Census 2011, Vol 1. Central Bureau of Statistics, Kathmandu, Nepal, 2012, [3] WECS, Nepal. Energy Sector Synopsis Report 2010. Water and Energy Commission Secretariat, Kathmandu, Nepal, 2010.

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[4] Manel S, Dias J M, et al., “Comparing discriminant analysis, neural networks and logistic regression for predicting species distributions: a case study with a Himalayan river bird,” Ecological modelling, vol. 120(2-3), pp. 337-347, 1999. [5] Valenzuela T D, Roe D. J, et al., “Estimating Effectiveness of Cardiac Arrest Interventions: A Logistic Regression Survival Model,” Circulation, vol. 96, pp. 3308-3313, 1997. [6] Hebert, J. R., Hurley, T. G. et al., “Nutritional and Socioeconomic Factors in Relation to Prostate Cancer Mortality: A Cross-National Study,” Journal of National Cancer Institute, vol. 90(21), pp. 1637-1647, 1998. [7] Howard N., Chandramohan D, et al., “Socio-economic factors associated with the purchasing of insecticide-treated nets in Afghanistan and their implications for social marketing,” Tropical Medicine and International Health, vol. 8(12), pp. 1043-1050, 2003. [8] Vyas S. and Kumaranayake, L., “Constructing Socio-Economic status indices: how to use principal component analysis,” Health Policy and Planning, vol. 21, pp. 459-468, 2006. [9] Filmer D. and Pritchett L., “Estimating wealth effect without expenditure data or tears: An application to educational enrollments in states of India,” Demography, vol. 38, pp. 115-132, 2001. [10] Kolenikov, S. and Angeles, G., “Socioeconomic status measurement with discrete proxy variables: is principal components analysis a reliable answer?” Review of Income and Wealth, vol. 55(1), pp. 128-165, 2009. [11] Polonsky, J, Balabonova, D, et al., “Equity in community health insurance schemes: evidence and lessons from Armenia,” Health and Policy Planning, vol. 24, pp. 209-216, 2009. [12] Mwageni, E, Masanja, H, et al., “Initial studies on health inequalities in the Rufiji River Basin, Coastal Tanzania: Evidence from a Demographic Surveillance System. Tanzania Ministry of Health,” TEHIP Discussion Paper No. 3, 2002. [Online]. Available: http://idl-bnc.idrc.ca/dspace/bitstream/10625/40988/1/129039. [Accessed on 25 March 2013]. [13] Devkota J. U. and Singh R. S., “Mathematical modeling of mortality for countries with limited and defective data,” Journal of Applied Statistical Sciences, vol. 19(1), pp. 24-36, 2011. [14] Devkota J. U. and Singh R. S., “Deterministic and probabilistic models with applications to modeling fertility data,” Journal of Applied Statistical Sciences, vol. 18(2), pp. 161-176, 2010. [15] Devkota J. U., Mortality and Fertility Models for Countries with Limited Data - Results Based on Demographic Data of Nepal, India and Germany. Lambert Academic Publishing, Saarbruecken, Germany, 2012. [16] Devkota J. U, Hada B, Prajapati C, Singh S.The importance of research data digitization and its statistical analysiswith example of biogas consumers of Nepal. International Journal for Environmental Science and Development, vol 3(2), pp. 103-108, 2012. [17] Court, V., “Energy Capture, Technological Change, and Economic Growth: An Evolutionary Perspective,” Biophysical Economics Resource Quality, pp. 3-12, 2018. [18] King, C. W., “Information Theory to Assess Relations between Energy and Structure of the U.S. Economy over Time,” Biophysical Economics Resource Quality, pp. 1-10, 2016. [19] Parkinson S, et al., “A multi-criteria model analysis framework for assessing integrated water-energy system transformation pathways,” Applied Energy, vol. 210, pp. 477- 486, 2018. [20] Logan, L, et al., “Probabilistic assessment of aquatic species risk from thermoelectric power plant effluent: Incorporating biology into the energy-water nexus,” Applied Energy, vol. 210, pp. 434-450, 2018. [21] Adio S.O, Asif M, et al., “Poly (amidoxime) modified magnetic activated carbon for chromium and thallium adsorption,” Statistical analysis and regeneration.Process Safety and Environmental Protection, vol. 121, pp. 254-262, 2019. [22] Hair J. F. and Anderson R. E., Multivariate Data Analysis. Singapore Pearson, 1998. [23] Devkota J. U., Singh S., Prajapati C., and Hada B., “Principle components analysis of the socioeconomic conditions of biogas users - with examples from Nepal,” International Journal of Renewable Energy Research, vol. 4(3), pp 655–664, 2014.

BIOGRAPHIES OF AUTHORS Jyoti U. Devkota, PhD is a Professor of Mathematical Statistics in Kathmandu University, Nepal. Her research interests are interdisciplinary applications of Statistics to problems from Energy and Environment. She focuses on development of new methodologies and application of existing methodologies to data generation, correction, prediction and analysis. She obtained her PhD degree from Department of Computer Sciences and Mathematics, University of Osnabrueck, Germany, supported by DAAD fellowship. She completed her Masters and Bachelors degree in Mathematical Statistics from Lady Shriram College, University of Delhi, New Delhi, India.

A categorical data analysis of impact of biogas … (Jyoti U. Devkota)

International Journal of Advances in Applied Sciences (IJAAS) Vol. 8, No. 1, March 2019, pp. 44~53 ISSN: 2252-8814, DOI: 10.11591/ijaas.v8.i1.pp44-53

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KED-AES algorithm: combined key encryption decryption and advance encryption standard algorithm Edwin R. Arboleda, Carla Eunice R. Fenomeno, Joshua Z. Jimenez Department of Computer and Electronics Engineering, College of Engineering and Information Technology, Cavite State University, Philippines

Article Info

ABSTRACT

Article history:

Two existing cryptosystems are being combined and proposed. It is the enhanced combination of KED (Key Encryption Decryption), a cryptosystem that uses modulo 69 and the AES (Advance Encryption Standard) cryptography. The strength of the KED is that the keys are being used by the sender and the receiving end. The AES is easy to implement and has good defense against various attacks such as hacking.

Received Nov 21, 2018 Revised Jan 16, 2019 Accepted Feb 1, 2019 Keywords: Advance encryption standard Cryptography Key encryption decryption

Corresponding Author: Edwin R. Arboleda, Department of Computer and Electronics Engineering, College of Engineering and Information Technology, Cavite State University, Indang, Cavite, Philippines. Email: edwin.r.arboleda@cvsu.edu.ph

INTRODUCTION Cryptography is the science of encrypting and decrypting information. It is the art of securing data or information by transforming it into another unreadable format wherein a message is hidden from anyone who will read it and only the intended receiver will be able to convert it and reveal the hidden message. The main goal of cryptography is to secure the data from illegal access [1, 2]. Based on [3, 4], it stated that Cryptography is the scientific establishment on which one forms secure frameworks. It contemplates methods for safely putting away, transmitting, and handling data. Understanding what cryptographic primitives can do, and how they can be made together, is important to construct secure frameworks, however not enough. A few extra contemplations go into the outline of secure frameworks, and they are secured in different Berkeley graduate courses on security [2]. Cryptography is about correspondence within the sight of an enemy. It includes numerous issues (encryption, verification, key appropriation to give some examples). The field of current cryptography gives a hypothetical establishment considering which we may comprehend what precisely these issues are, the manner by which to assess conventions that indicate to explain them, and how to construct conventions in whose security we can have. We present the essential issues by talking about the issue of encryption [5]. According to [6], Cryptography is the place security designing meets arithmetic. It gives us the apparatuses that underlie most advanced security conventions. It is presumably the key empowering innovation for securing appropriated frameworks, yet it is shockingly difficult to do right. "Conventions," cryptography has frequently been utilized to secure the wrong things or used to ensure them in the wrong way. We'll see bounty more cases when we begin looking in detail at genuine applications [7].

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Cryptography is the exploration of writing in mystery code and is an antiquated craftsmanship; the initially reported utilization of cryptography in composing goes back to around 1900 B.C. at the point when an Egyptian copyist utilized non-standard symbolic representations as a part of an engraving [8]. A few specialists contend that cryptography showed up suddenly at some point in the wake of composing was developed, with applications running from strategic letters to war-time fight arranges. It is nothing unexpected, then, that new types of cryptography came not long after the broad improvement of PC correspondences [9]. In information and broadcast communications, cryptography is fundamental when imparting over any untrusted medium, which incorporates pretty much any system, especially the internet [8]. There are two basic types of encryption, symmetric algorithm and asymmetric algorithm. According to [10] symmetric algorithms also called “secret key” uses the same key for both encryption and decryption and asymmetric algorithms or “public key” uses different keys for encryption and decryption [11]. A symmetric cryptosystem (or private key cryptosystem) utilizes one and only key for both encryption and unscrambling of the information. The key utilized for encryption and unscrambling is called the private key and just individuals who are approved for the encryption/unscrambling would know it. In a symmetric cryptosystem, the encoded message is sent over without any open keys joined to it while asymmetric or the public key there are two distinctive keys utilized for the encryption and decoding of information [8]. The key utilized for encryption is kept open thus as called open key, and the unscrambling key is kept mystery and called private key. The keys are created in a manner that it is difficult to get the private key from people in general key. The transmitter and the beneficiary both have two keys in an Asymmetric framework. Be that as it may, the private key is kept private and not sent over with the message to the beneficiary, in spite of the fact that the general population key is this is further explained by [11]. The advantages and disadvantages of symmetric and asymmetric cryptosystem was stated by [12], A symmetric cryptosystem is speedier, encoded information be exchanged on the connection regardless of the possibility that there is a probability that the information will be caught. Since there is no key transmitted with the information, the odds of information being decoded are invalid, it utilizes secret word validation to demonstrate the beneficiary's character and a framework just which has key can unscramble a message are the benefits of symmetric cryptosystem while in Asymmetric cryptosystem [13], cryptography there is no requirement for trading keys, along these lines wiping out the key conveyance issue [14], the essential preferred standpoint of open key cryptography is expanded security: the private keys absolutely never should be transmitted or uncovered to anybody, and in conclusion it can give advanced marks that can be denied [15]. The disadvantages of symmetric cryptosystem have an issue of key transportation. The private key is to be transmitted to the getting framework before the real message is to be transmitted [16, 17]. Each method for electronic correspondence channels. In this way, the main secure method for trading keys would trade them by and by and can't give advanced mark that can't be revoked. While in asymmetric cryptosystem an impediment of utilizing open key cryptography for encryption is speed: there are well known mystery key encryption techniques which are altogether speedier than any right now accessible open key encryption strategy [18]. This standard indicates the Rijndael calculation [19, 20], a symmetric square figure that can prepare information squares of 128 bits, utilizing figure keys with lengths of 128, 192, and 256 bits [21]. Rijndael was intended to handle extra piece sizes and key lengths, nonetheless they are not embraced in this standard. All through the rest of this standard, the calculation determined thus will be alluded to as "the AES algorithm." The algorithm might be utilized with the three different key lengths, mention above, and in this way, these different "flavors" might be alluded to as "AES-128", "AES-192", and"AES-256 [4]". Warjri [22] proposed a new symmetric key algorithm called as KED (Key Encryption Decryption) and a new key generation method. Using modulo 69 and inverse modulo69, the proposed algorithm was used for encryption and decryption process, in which the same key is used both for encryption and decryption. 2.

RESEARCH METHOD The authors proposed a hybrid of KED that uses modulo 69, CHAOS-based cryptosystem, and AES algorithm. The key generation used in this hybrid uses that of KED and all the algorithm are used in encryption and decryption process [18]. The s-box and inverse s-box tables will be used in encryption and decryption respectively; they will be part of the process itself and not in the sent message. The Encryption process is shown in Figure 1 and The Decryption process is shown in Figure 2.

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ISSN: 2252-8814

Figure 1. Encryption process

Figure 2. Decryption process [23] Key generation algorithm KED Key Generation: a. Generating ‘k2’, firstly user enters a key. The length of the key is stored in ‘kl’. Hence k2 is generated as follows: b. c. d.

Where, ‘i’ is the position of each character in the key. ‘kl’ is the length of the key. ‘val’ is the integer value that has been assign to each character as shown in Figure 3. For ‘k1’, select any natural number say ‘k1’ where k1≠0 and must be relatively prime to ‘m’ (i.e., ‘k1’ should not have factors in common with ‘m’). Find inverse modulo69 of ‘k1’ and store it in ‘n1’

Chaos Key Generation: a. Convert k1 and k2 to its 8-bit binary equivalent. b. Get the gray code of the binary form of k1 and k2, denote as J1 and J2. Encryption KED:

Figure 3. Synthetic values of alphabets [23] Int. J. of Adv. in Appl. Sci. Vol. 8, No. 1, March 2019: 44 – 53

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a. Firstly, substitute or assign integer value for plain text. b. Multiply Synthetic value with first key i.e., k1. c. Now add the result from step 2 above with second key i.e., k2. d. Then calculate with modulo69. CHAOS: a. Convert mi to 8-bit binary equivalent. b. XOR the 8-bit binary with the corresponding k. c. Get the 1’s complement, denoted as w. AES: a. Convert the binary to hexadecimal b. Apply the S-box to each value as shown in Figure 4, denoted as W1. c. The first digit will be the row x, and second digit will be the column y. d. Convert to binary the row x and column y. e. Convert to decimal the 8-bit binary for the cipher text.

Figure 4. AES S-Box [23] Decryption AES: a. Convert to binary to hexadecimal. b. Apply the inverse S-box as shown in Figure 5, denoted as W2. c. Covert to binary.

Figure 5. AES Inverse S-Box [23] KED-AES algorithm: combined key encryption decryption and advance … (Edwin R. Arboleda)

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ISSN: 2252-8814

CHAOS: a. Get the 1’s complement, denoted as ‘S’. b. XOR the 8-bit binary with the corresponding k. c. Convert the 8-bit binary to its decimal equivalent, denoted as mi. KED: a. Assign integer value for cipher text as given in Figure 3. b. Subtract ‘k2’ from above integer value. c. Multiply above result with inverse modulo69 of ‘k1’ i.e., ‘n1’. d. Finally calculate with modulo69. 3.

RESULTS AND ANALYSIS Message: COZY LOOBY GIVES SHARP SQUID WHO ASK FOR JOB PEN. The Encryption and Decryption are shown in Table 1-6. Encryption: Table 1. KED encryption Plaintext (PT) C O Z Y SPACE L O O B Y SPACE G I V E S SPACE S H A R P SPACE S Q U I D SPACE W H 0 SPACE A S K SPACE F O R SPACE J O B SPACE P E N .

Integer Value (V1) 3 15 26 25 37 12 15 15 2 25 37 7 9 22 5 19 37 19 8 1 18 16 37 19 17 21 9 4 37 23 8 15 37 1 19 11 37 6 15 18 37 10 15 2 37 16 5 14 51

V1*K1 (C1) 15 75 130 125 185 60 75 75 10 125 185 35 45 110 25 95 185 95 40 5 90 80 185 95 85 105 45 20 185 115 40 75 185 5 95 55 185 30 75 90 185 50 75 10 185 80 25 70 225

C1+K2 (C2) 21 81 136 131 191 66 81 81 16 131 191 41 51 116 31 101 191 101 46 11 96 86 191 101 91 111 51 26 191 121 46 81 191 11 101 61 191 36 81 96 191 56 81 16 191 86 31 76 261

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C2 mod69 21 12 67 62 53 66 12 12 16 62 53 41 51 47 31 32 53 32 46 11 27 17 53 32 22 42 51 26 53 52 46 12 53 11 32 61 53 36 12 27 53 56 12 16 53 17 31 7 54

Synthetic Value U L | ] : { L L P ] : $ . * 4 5 : 5 ) K 0 Q : 5 V % . Z : / ( U : K 5 \ : 9 U 0 : = U P : Q 4 G ;

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Table 2. CHAOS encryption ASCII Value 85 76 124 93 58 123 76 76 80 93 58 36 46 42 52 53 58 53 41 75 48 81 58 53 66 37 46 90 58 47 41 85 58 75 53 92 58 57 85 48 58 61 85 80 58 81 52 71 59

Binary 0101 0101 0100 1100 0111 1100 0101 1101 0011 1010 0111 1010 0100 1100 0100 1100 0101 0000 0101 1101 0011 1010 0010 0100 0010 0100 0010 1010 0011 0100 0011 0101 0011 1010 0011 0101 0010 1001 0100 1011 0011 0000 0101 0001 0011 1010 0011 0101 0100 0010 0010 0101 0010 0100 0101 1010 0011 1010 0010 1111 0010 1001 0101 0101 0011 1010 0100 1011 0011 0101 0101 1100 0011 1010 0011 1001 0101 0101 0011 0000 0011 1010 0011 1101 0101 0101 0101 0000 0011 1010 0101 0001 0011 0100 0100 0111 0011 1011

Gray coded (K1 & K2) 0000 0111 0000 0101 0000 0111 0000 0101 0000 0111 0000 0101 0000 0111 0000 0101 0000 0111 0000 0101 0000 0111 0000 0101 0000 0111 0000 0101 0000 0111 0000 0101 0000 0111 0000 0101 0000 0111 0000 0101 0000 0111 0000 0101 0000 0111 0000 0101 0000 0111 0000 0101 0000 0111 0000 0101 0000 0111 0000 0101 0000 0111 0000 0101 0000 0111 0000 0101 0000 0111 0000 0101 0000 0111 0000 0101 0000 0111 0000 0101 0000 0111 0000 0101 0000 0111 0000 0101 0000 0111 0000 0101 0000 0111 0000 0101 0000 0111

XOR 0101 0010 0100 1001 0111 1011 0101 1000 0011 1101 0111 1110 0100 1011 0100 1001 0101 0111 0101 1000 0011 1101 0010 0001 0010 0011 0010 1111 0011 0011 0011 0000 0011 1101 0011 0000 0010 1110 0100 1110 0011 0111 0101 0100 0011 1101 0011 0000 0100 0101 0010 0000 0010 0011 0101 1111 0011 1101 0010 1010 0010 1110 0101 0000 0011 1101 0100 1110 0011 0010 0101 1011 0011 1101 0011 1100 0101 0010 0011 0101 0011 1101 0011 1000 0101 0010 0101 0101 0011 1101 0101 0100 0011 0011 0100 0010 0011 1100

1’s Complement 1010 1101 1011 0110 1000 0100 1010 0111 1100 0010 1000 0001 1011 0100 1011 0110 1010 1000 1010 0111 1100 0010 1101 1110 1101 1100 1101 0000 1100 1100 1100 1111 1100 0010 1100 1111 1101 0001 1011 0001 1100 1000 1010 1000 1100 0010 1100 1111 1011 1010 1101 1111 1101 1100 1010 0000 1100 0010 1101 0101 1101 0001 1010 1111 1100 0010 1011 0001 1100 1101 1010 0100 1100 0010 1100 0011 1010 1101 1100 1010 1100 0010 1100 0111 1010 1101 1010 1010 1100 0010 1010 1011 1100 1100 1011 1101 1100 0011

Table 3. AES encryption Hexadecimal AD B6 84 A7 C2 81 B4 B6 A8 A7 C2 DE DC D0 CC CF C2

AES S-Box (W1) 95 4e 5f 5c 25 0c 8d 4e c2 5c 2e 1d 86 70 4b 8a 25

Binary 1001 0101 0100 1110 0101 1111 0101 1100 0010 0101 0000 1100 1000 1101 0100 1110 1100 0010 0101 1100 0010 1110 0001 1101 1000 0110 0111 0000 0100 1011 1000 1010 0010 0101

Cipher text 149 78 95 92 37 12 141 78 194 92 46 29 134 112 75 138 37

KED-AES algorithm: combined key encryption decryption and advance … (Edwin R. Arboleda)

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ISSN: 2252-8814 Table 3. AES encryption (continued) Hexadecimal CF D1 B1 C8 A8 C2 CF BA DF DC A0 C2 D5 D1 AF C2 B1 CD A4 C2 C3 AD CA C2 C7 AD AA C2 AB CC BD C3

AES S-Box (W1) 8a 3e c8 e8 c2 25 8a f4 9e 86 e0 25 03 3e 79 25 c8 db 49 25 2e 95 74 25 c6 95 ac 25 62 4b 7a 2e

Binary 1000 1010 0011 1110 1100 1000 1110 1000 1100 0010 0010 0101 1000 1010 1111 0100 1001 1110 1000 0110 1110 0000 0010 0101 0000 0011 0011 1110 0111 1001 0010 0101 1100 1000 1101 1011 0100 1001 0010 0101 0010 1110 1001 0101 0111 0100 0010 0101 1100 0110 1001 0101 1010 1100 0010 0101 0110 0010 0100 1011 0111 1010 0010 1110

Cipher text 138 62 200 232 194 37 138 244 158 134 224 37 3 62 121 37 200 219 73 37 46 149 116 37 198 149 172 37 98 75 122 46

Decryption: Table 4. AES decryption Decimal 149 78 95 92 37 12 141 78 194 92 46 29 134 112 75 138 37 138 62 200 232 194 37 138 244 158 134 224 37 3 62

Binary 1001 0101 0100 1110 0101 1111 0101 1100 0010 0101 0000 1100 1000 1101 0100 1110 1100 0010 0101 1100 0010 1110 0001 1101 1000 0110 0111 0000 0100 1011 1000 1010 0010 0101 1000 1010 0011 1110 1100 1000 1110 1000 1100 0010 0010 0101 1000 1010 1111 0100 1001 1110 1000 0110 1110 0000 0010 0101 0000 0011 0011 1110

Hexadecimal 95 4e 5f 5c 25 0c 8d 4e c2 5c 2e 1d 86 70 4b 8a 25 8a 3e c8 e8 c2 25 8a f4 9e 86 e0 25 03 3e

Inverse S-Box (W2) AD B6 84 A7 C2 81 B4 B6 A8 A7 C2 DE DC D0 CC CF C2 CF D1 B1 C8 A8 C2 CF BA DF DC A0 C2 D5 D1

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Binary 1010 1101 1011 0110 1000 0100 1010 0111 1100 0010 1000 0001 1011 0100 1011 0110 1010 1000 1010 0111 1100 0010 1101 1110 1101 1100 1101 0000 1100 1100 1100 1111 1100 0010 1100 1111 1101 0001 1011 0001 1100 1000 1010 1000 1100 0010 1100 1111 1011 1010 1101 1111 1101 1100 1010 0000 1100 0010 1101 0101 1101 0001

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Table 4. AES decryption (continued) Decimal 121 37 200 219 73 37 46 149 116 37 198 149 172 37 98 75 122 46

Binary 0111 1001 0010 0101 1100 1000 1101 1011 0100 1001 0010 0101 0010 1110 1001 0101 0111 0100 0010 0101 1100 0110 1001 0101 1010 1100 0010 0101 0110 0010 0100 1011 0111 1010 0010 1110

Hexadecimal 79 25 c8 db 49 25 2e 95 74 25 c6 95 ac 25 62 4b 7a 2e

Inverse S-Box (W2) AF C2 B1 CD A4 C2 C3 AD CA C2 C7 AD AA C2 AB CC BD C3

Binary 1010 1111 1100 0010 1011 0001 1100 1101 1010 0100 1100 0010 1100 0011 1010 1101 1100 1010 1100 0010 1100 0111 1010 1101 1010 1010 1100 0010 1010 1011 1100 1100 1011 1101 1100 0011

Table 5. CHAOS decryption 1’s Complement (S) 0101 0010 0100 1001 0111 1011 0101 1000 0011 1101 0111 1110 0100 1011 0100 1001 0101 0111 0101 1000 0011 1101 0010 0001 0010 0011 0010 1111 0011 0011 0011 0000 0011 1101 0011 0000 0010 1110 0100 1110 0011 0111 0101 0100 0011 1101 0011 0000 0100 0101 0010 0000 0010 0011 0101 1111 0011 1101 0010 1010 0010 1110 0101 0000 0011 1101 0100 1110 0011 0010 0101 1011 0011 1101 0011 1100 0101 0010 0011 0101 0011 1101 0011 1000 0101 0010 0101 0101 0011 1101 0101 0100 0011 0011 0100 0010 0011 1100

J1 & J2 0000 0111 0000 0101 0000 0111 0000 0101 0000 0111 0000 0101 0000 0111 0000 0101 0000 0111 0000 0101 0000 0111 0000 0101 0000 0111 0000 0101 0000 0111 0000 0101 0000 0111 0000 0101 0000 0111 0000 0101 0000 0111 0000 0101 0000 0111 0000 0101 0000 0111 0000 0101 0000 0111 0000 0101 0000 0111 0000 0101 0000 0111 0000 0101 0000 0111 0000 0101 0000 0111 0000 0101 0000 0111 0000 0101 0000 0111 0000 0101 0000 0111 0000 0101 0000 0111 0000 0101 0000 0111 0000 0101 0000 0111 0000 0101 0000 0111

S XOR J1/J2 (S1) 0101 0101 0100 1100 0111 1100 0101 1101 0011 1010 0111 1010 0100 1100 0100 1100 0101 0000 0101 1101 0011 1010 0010 0100 0010 0100 0010 1010 0011 0100 0011 0101 0011 1010 0011 0101 0010 1001 0100 1011 0011 0000 0101 0001 0011 1010 0011 0101 0100 0010 0010 0101 0010 0100 0101 1010 0011 1010 0010 1111 0010 1001 0101 0101 0011 1010 0100 1011 0011 0101 0101 1100 0011 1010 0011 1001 0101 0101 0011 0000 0011 1010 0011 1101 0101 0101 0101 0000 0011 1010 0101 0001 0011 0100 0100 0111 0011 1011

Decimal 85 76 124 93 58 123 76 76 80 93 58 36 46 42 52 53 58 53 41 75 48 81 58 53 66 37 46 90 58 47 41 85 58 75 53 92 58 57 85 48 58 61 85 80 58 81 52 71 59

ASCII Code U L | ] : { L L P ] : $ . * 4 5 : 5 ) K 0 Q : 5 V % . Z : / ( U : K 5 \ : 9 U 0 : = U P : Q 4 G ;

KED-AES algorithm: combined key encryption decryption and advance … (Edwin R. Arboleda)

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ISSN: 2252-8814 Table 6. KED decryption Integer Value (V2) 21 12 67 62 53 66 12 12 16 62 53 41 51 47 31 32 53 32 46 11 27 17 53 32 22 42 51 26 53 52 46 12 53 11 32 61 53 36 12 27 53 56 12 16 53 17 31 7 54

V2-K2 (P1) 15 6 61 56 47 60 6 6 10 56 47 35 45 41 25 26 47 26 40 5 21 11 47 26 16 36 45 20 47 46 40 6 47 5 26 55 47 30 6 21 47 50 6 10 47 11 25 1 48

P1*n1 (P2) 210 84 854 784 658 840 84 84 140 784 658 490 630 574 350 364 658 364 560 70 294 154 658 364 224 504 630 280 658 644 560 84 658 70 364 770 658 420 84 294 658 700 84 140 658 154 350 14 672

P2 mod69 3 15 26 25 37 12 15 15 2 25 37 7 9 22 5 19 37 19 8 1 18 16 37 19 17 21 9 4 37 23 8 15 37 1 19 11 37 6 15 18 37 10 15 2 37 16 5 14 51

Plaintext (PT) C O Z Y SPACE L O O B Y SPACE G I V E S SPACE S H A R P SPACE S Q U I D SPACE W H O SPACE A S K SPACE F O R SPACE J O B SPACE P E N .

CONCLUSION The proposed cryptosystem was able to combine the KED using modulo 69, CHAOS-based and AES cryptosystem. It is the enhanced system of KED by adding the chaotic-based cryptosystem and the AES S-box and inverse S-box in both encryption and decryption. The proposed algorithm is then implemented by using a message that comprise the English alphabet. The key is generated using the proposed method. Then the message is encrypted and decrypted successfully. REFERENCE [1] S. Hebert, “A Brief History of Cryptography”, an article [Online]. Available: http://cybercrimes.net/aindex.html. [2] A. Sanada, Y. Nogami, K. Iokibe, and A. Khandaker, “Security Analysis of Raspberry Pi Against Side-Channel Attack with RSA Cryptography,” pp. 287–288, 2017. [3] Trevisan, L., "Cryptography. Lecture Notes from CS276," Spring 2009, vol. 154, 2009. [4] L. K. Galla, V. S. Koganti, and N. Nuthalapati, “Implementation of RSA,” in 2016 International Conference on Control Instrumentation Communication and Computational Technologies, ICCICCT 2016, pp. 81–87, 2017.

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[5] D. Llamocca, D. Ph, D. Debnath, D. Ph, L. Rong, and D. Ph, “Can Crypto Chip To Secure Data Transmitted Through Can Fd Bus by Using Aes-128 & Sha-1 With A Symmetric Key by Tri P. Doan," A dissertation submitted in partial fulfillment of the requirement for the degree of Doctor of Philosophy Rochester, Michigan Doct, 2017. [6] Goldwasser, S., and Bellare, M., "Lecture Notes on Cryptography," 2008. [7] S. Oukili and S. Bri, “High throughput FPGA Implementation of Data Encryption Standard with time variable subkeys,” Int. J. Electr. Comput. Eng., vol. 6, no. 1, p. 298, 2016. [8] S. Dey, J. Nath, and A. Nath, “An Integrated Symmetric Key Cryptographic Method - Amalgamation of TTJSA Algorithm, Advanced Caesar Cipher Algorithm, Bit Rotation and Reversal Method: SJA Algorithm,” Int. J. Mod. Educ. Comput. Sci., vol. 4, no. 5, pp. 1–9, 2012. [9] P. K. Panda, “A Hybrid Security Algorithm for RSA Cryptosystem,” 2017. [10] Caesar, G. J. and Kennedy, J.F. (n.d.)., "Cryptography. Security Engineering: A Guide to Building Dependable Distributed Systems," vol. 42, 2008. [11] D. John and L. Martin, “Binary rsa encryption algorithm,” pp. 178–181, 2016. [12] L. Kocarev, J.M. Amigo, and J. Szczepanski, “The Chaos-based Cryptography: an overview”, IEEE Circuits and Systems Magazine, vol. 1(3), pp. 6-21, 2001. [13] Y. Chen, K. Li, X. Fei, Z. Quan, and K. Li, “Implementation and Optimization of AES Algorithm on the Sunway Taihu Light,” 2016. [14] K. Garson, Policy-Based Encryption and its Application in a Hospital System by. 2009. [15] Q. Zhang and A. Qunding, “Digital image encryption based on Advanced Encryption Standard(AES) algorithm,” 5th Int. Conf. Instrum. Meas. Comput. Commun. Control. IMCCC 2015, pp. 1218–1221, 2015. [16] J.-F. W. J.-F. Wang, S.-W. C. S.-W. Chang, and P.-C. L. P.-C. Lin, “A novel round function architecture for AES encryption/decryption utilizing look-up table,” IEEE 37th Annu. 2003 Int. Carnahan Conf. onSecurity Technol. 2003 Proc., pp. 132–136, 2003. [17] P. Katkade and G. M. Phade, “Application of AES algorithm for data security in serial communication,” Proc. Int. Conf. Inven. Comput. Technol. ICICT 2016, vol. 2016, 2016. [18] O. A. Dawood, A. M. S. Rahma, and A. M. J. Abdul Hossen, “New Symmetric Cipher Fast Algorithm of Revertible Operations’ Queen (FAROQ) Cipher,” Int. J. Comput. Netw. Inf. Secur., vol. 9, no. 4, pp. 29–36, 2017. [19] J, Daemen and V. Rijmen, “AES Proposal: Rijndael, AES Algorithm Submission,” September 3, 1999. [20] J. Daemen and V. Rijmen, “The block cipher Rijndael, Smart Card research and Applications, LNCS 1820,” Springer-Verlag, pp. 288-296. [21] A. K. Sharma and H. Sharma, “New Approach to Des with Enhanced Key Management and Encryption/Decryption System,” vol. 32, pp. 1–31, 2012. [22] J. Warjri, KED- “A Symmetric Key Algorithm for Secured Information Exchange Using Modulo 69,” I.J Computer Network and Information Security, vol. 10, pp 37-43, 2013. [23] J. L. Lai, K. H. Liao, Y. Te Lai, and R. J. Chen, “Design CAROM module used in AES structure for Sub-Byte and Inv-Sub-Byte transformation,” Proc. - 2013 Int. Symp. Biometrics Secur. Technol. ISBAST 2013, no. 1, pp. 198–202, 2013.

KED-AES algorithm: combined key encryption decryption and advance … (Edwin R. Arboleda)

International Journal of Advances in Applied Sciences (IJAAS) Vol. 8, No. 1, March 2019, pp. 54~63 ISSN: 2252-8814, DOI: 10.11591/ijaas.v8.i1.pp54-63

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Design PV system for a small GEO satellite and studying the effect of using different types of propulsion A. Lotfy, W.Anis, Joseph V. M. Halim Faculty of Engineering, Ain Shams University, Egypt

Article Info

ABSTRACT

Article history:

This paper presents an optimum design of the solar Photo-Voltaic (PV) power system for small Geostationary Earth Orbit (GEO) satellites using triple junction solar cells and advanced Lithium Ion batteries. The paper applies the proposed system on various propulsion technologies; full chemical, full electrical and hybrid propulsions. This research work studies the capability to fulfil efficiently all the satellite power requirements during both the launching and the on-station phases while reducing the high cost challenge. Since the propulsion type is a key factor for the satellite cost, an economic analysis is demonstrated and investigated in two different strategies. The first scenario fixes the satellite weight and offers the revenue due to the increase in the satellite payload. However, the second scenario evaluates the saving profits due to the reduction in the satellite weight using the same number of satellite transponders. The analytical comparison among the different propulsion techniques shows the superior advantages of using the full electrical satellites.

Received Nov 29, 2018 Revised Jan 31, 2019 Accepted Feb 19, 2019 Keywords: Economic Electric propulsion GEO satellite Lithium ion batteries Multijunction solar cell

Corresponding Author: A. Lotfy, Faculty of Engineering, Ain Shams University, Nour Mosque 38 Abbasia Next El-Mohamady, Al Waili, Cairo, Egypt. Email: alotfy718@gmail.com

INTRODUCTION Small Geostationary Earth Orbit (GEO) satellites are proposed to accomplish efficiently the geostationary missions with a launch mass of 2 to 3.5 tons and a lifetime of 10 to 15 years. The satellite power system consists of two main parts; the solar array and the storage batteries. The Photovoltaic (PV) solar arrays are the primary source of converting the solar energy to an effective electrical power and the arrays configuration depend on the spacecraft stabilization technique, the type of the orbit and the satellite power requirements [1]. The storage batteries are charged using the generated power of the solar arrays during the daylight and they are used to satisfy all the satellite power requirements during the eclipse durations. In this paper, the proposed solar system uses the triple junction (3J) solar cells, which have highest efficiency and lightest weight. Also, it exploits the benefits of a certain type of the Lithium Ion batteries (Li-ion VES180) due to its low mass and high storage capacity. Basically, there are three types of propulsion in the satellite market. The first and most common type is the full chemical propulsion (CP), which uses the chemical reactions to produce a flow of fast-moving hot gas. So, the satellite can reach the GEO in few days since the liquid apogee and perigee engines can provide strong pushes using these chemical reactions. Also, the chemical thrusters can perform all the station keeping maneuvers and most of the attitude control operations using this chemical fuel during the satellite lifetime. Therefore, the satellite lifetime is determined by the amount of fuel that can be stored in the propellant tanks. However, all the operations of the satellite subsystems, especially the payload and Telemetry/Command/Ranging (TCR) subsystems, depend on the solar power. The second type is the full Journal homepage: http://iaescore.com/online/index.php/IJAAS

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electrical propulsion (EP), which depends only on the generated power from the solar arrays either during the transfer orbits phase or to perform the station keeping maneuvers, the attitude control corrections and fulfil the subsystems operations. The electric propulsion is limited by the amount of the available power that can be generated by the solar panels and stored in the batteries. This causes that the launching and Geostationary Transfer Orbits (GTO) phase can be extended up to six months to reach the GEO. However, it offers several advantages over the chemical type. First, the economic benefits due to reducing the launching mass, or the opportunity to increase the service payload mass in addition to increasing the operational lifetime [1]. Second, the operational benefits since the electric-propulsion engines and thrusters are more efficient than chemical ones, in the sense that they require much less propellant to produce the same overall effect. Moreover, the electric thrusters have the ability to regulate the force applied to the spacecraft in very accurate manner and consequently, control the spacecraft’s position and orientation along its orbit with an incomparable precision. The third propulsion type is the hybrid, which can be considered as a mix of the previous two types [2]. For this type, the kicks of the transfer orbits are performed using the chemical fuel. However, the station keeping maneuvers, the attitude control operations and the subsystems power requirements depend on the electrical power generated from the solar arrays. In this paper, an optimum design for the satellite PV system will be applied on GEO satellites and studied for different types of propulsion, including full chemical, hybrid and full electrical. Practically, the design satisfies all the satellite power requirements during the satellite lifetime and its methodology can be summarized in the following steps: First, defining the satellite orbital period and the eclipse duration, used in the presented design; identifying the power demand of the satellite operations, that copes with the satellite lifetime. This includes the power requirements to perform the on-station keeping maneuvers and the attitude control corrections in case of using either hybrid or full electrical propulsions. Also, the required thrust power to perform the GTO kicks, till reaching the GEO, in case of using full electrical propulsion. This is in addition to satisfying the power requirements of the various satellite subsystems; estimating the required area and mass of the satellite solar array, using the selected solar cell type. This is in addition to calculating the corresponding total number of assembled solar cells in the solar array; the last is calculating the optimum size, mass and the required capacity of the selected battery bank. This paper is organized as follows: Section 2 presents the design of the solar array of the proposed power system and applies it for various types of propulsion. The details of the battery sizing, mass and storage capacity are discussed in section 3 for all the propulsion types. In sections 4, the economic analysis is presented in two strategies to compare between the propulsion technologies; the first demonstrates the revenue due to the increase in the satellite payload for the same satellite weight and the second shows the saving profits due to the reduction in the satellite weight using the same number of transponders. Finally, the conclusion is drawn in section 5. 2.

THE SOLAR ARRAY DESIGN Initially, a full chemical small geostationary satellite is used as a study case. The satellite has a lifetime of 12 years and a total mass of 3028 kg whereas the fuel mass is 1693 kg. Also, the orbital period is 23.56 hours and the maximum eclipse duration is 72 minutes. The parameters of the proposed power system are demonstrated in the following data: a. The essential average power is assumed to be 5.859 kW either during daylight or eclipse, 𝑃 = 𝑃 = 5.859 kW, where 𝑃 and 𝑃 denote the spacecraft power requirements during eclipse and daylight, respectively. b. A power tracking mechanism is applied by using a Solar Array Drive Mechanism (SADM) to have a peak generated power during daylight. c. The efficiency of the satellite power subsystem will be roughly 𝑋 = 0.8 and 𝑋 = 0.9 where 𝑋 is the efficiency of the spacecraft power subsystem to supply the power from the array to the loads in daylight. However, 𝑋 is the efficiency of the spacecraft power subsystem to supply the power from the array to the batteries for charging during daylight and from the batteries to the loads during eclipse [3]. d. The degradation per year for the multi-junction solar cells, like the triple junction (3J) cells, is 0.5%. Also, the nominal value of the Inherent degradation (Id) is 0.77 [4]. The total extent of the generated power, that can be achieved by the solar array during the daylight, is determined by summing the power requirements of the spacecraft during the daylight and the needed power to charge the batteries to accomplish the eclipse power demands. Then, the solar array generated power 𝑃 can be expressed as: 𝑃 =

= 7.67 kW

(1)

Design PV system for a small GEO satellite and studying the effect of using … (A. Lotfy)

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ISSN: 2252-8814

where 𝑇 and 𝑇 are the eclipse and the daylight durations for the satellite in its orbit, respectively. It should be noted that 𝑇 equals 72 minutes whereas 𝑇 is equal to 1341.6 minutes. In this paper, the solar array design will be described the following steps: a. Estimating the output power per unit area of the solar array The output power per unit area 𝑃 can be calculated by multiplying the efficiency of the PV material (ƞ) by the solar radiation intensity (𝐺 ). Since the power efficiency of the 3J solar cells is 30% and 𝐺 equals to 1367w/𝑚 [5], 𝑃 can be presented as: 𝑃 = (𝐺 ) ×ƞ = 410.1 Watt/𝑚

(2)

b. Calculating the required output power at the beginning of life (BOL) of the satellite 𝑃

= 𝑃 𝐼 cos 𝜃 = 315.78 Watt

(3)

where 𝜃 is the incidence angle between the vector normal to the solar array and the direction of the sun line. In the presented design, 𝜃 equals zero since the satellite uses a power tracking mechanism. c. The power degradation factor (𝐿 ) through the satellite lifetime is given as 𝐿 = (1 − degradation per year)

= 0.94

(4)

d. Estimating the output power at the satellite end of life (EOL) PEOL=PBOL Ld = 296.83 Watts

(5)

e. Determining the required area (Asa) and mass (Msa) of the solar array 𝐴

= 25.8 𝑚

(6)

The specific performance of the planar array is taken to be 70 W/Kg [6]. Consequently, the required mass of the solar array can be evaluated as: 𝑀

𝑃

= 109.57 𝑘𝑔

(7)

f. Calculating the required number (Nsa) of the assembled solar cells in the solar array The data of the Azur Space for the 3J solar cells is used in the presented design, where the Bus voltage is 𝑉 = 100 𝑣 𝑑𝑐 and the cell power is 𝑃 = 1.24 Watt. Therefore, the total number of the assembled triple junctions (3J) cells in the solar array can be obtained as: Nsa=

= 6185cells

(8)

In the following discussion, a comparison between the types of propulsion (full chemical, hybrid and full electrical) is performed in power budget, required solar array area and mass points of view. a. All the previous calculations are performed in case of using full chemical propulsion, where the generated power of the solar arrays is only used to fulfil the power requirements of the satellite subsystems. However, all the maneuvers and attitude control corrections in addition to the transfer orbits kicks are performed using the chemical fuel. b. Either for full chemical or hybrid satellites, the required amount of the chemical fuel to transfer the satellite during the GTO till reaching the GEO is 1230 kg. c. For both full electrical and hybrid satellites, the daily North-South and East-West station keeping manoeuvres, in addition to the attitude control corrections every 1-2 days, are performed using the solar arrays power [7]. During the designed satellite lifetime (10-15 years in this paper), the thrusters require about 80-100 mN for the orbital correction operations [8] and about 40-80 mN for the attitude control corrections [1]. To satisfy the previous thrusters force for the orbital and attitude control, the power demand of the satellite will increase about 1.8-2.7 kW more than the full chemical propulsion type [9].

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d. Depending on the previous methodology, the satellite using hybrid propulsion needs about 1.8-2.7 kW extra average power more than using chemical propulsion. This is in order to perform the required station keeping and attitude control corrections during a satellite lifetime of 10-15 years. Using the same formulas of the full chemical satellite, the required generated power (𝑃 ) from the solar array for the hybrid satellite can be calculated as 10.03-11.21 kW. Consequently, the required area and weight of the solar array will be increased. The calculated results show that the solar array area (𝐴 ) will increase to 33.837.76 𝑚 , the solar array mass (𝑀 ) will be 143.29-160.14 kg and the total number of the assembled cells in the solar array (Nsa) will be 8088-9040 cells. e. Using full electrical propulsion, the satellite needs approximately 320-540 mN for its GTO kicks till reaching the GEO and about 80-160 mN for the orbit topping [1]. By adding the previously mentioned requirements of the thrusters to satisfy the orbital and attitude control corrections, with a considerable safety margin, the satellite needs about 800 mN-1N during the designed lifetime. This means that there is around 16-20 kW extra average power is required more than the full chemical propulsion [10]. Consequently, the solar array for the electrical satellite should generate power (𝑃 ) of 28.63-33.87 kW. Therefore, the calculated solar array area (𝐴 ) is 90.45-114.09 𝑚 , the solar array mass (𝑀 ) is 312.27483.86 kg and the total number of the assembled cells (Nsa) is 23088-27314 cells. Figure 1 shows solar array required area versus satellite lifetime using full chemical propulsion. Figure 2 shows solar array required area versus satellite lifetime using hybrid propulsion. Figure 3 shows solar array required area versus satellite lifetime using full electrical propulsion.

Figure 1. Solar array required area versus satellite lifetime using full chemical propulsion

Figure 2. Solar array required area versus satellite lifetime using hybrid propulsion Design PV system for a small GEO satellite and studying the effect of using … (A. Lotfy)

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ISSN: 2252-8814

Figure 3. Solar array required area versus satellite lifetime using full electrical propulsion Figures 1, 2 and 3 illustrate the relation between the required area of the designed solar array versus the satellite lifetime using full chemical, hybrid and full electrical propulsions, respectively. As mentioned before, the solar array in the full chemical satellite generates the power for only the subsystems requirements whereas the chemical fuel is used in all other stages, including transfer orbits, maneuvering and attitude control operations. Also, hybrid satellite uses the solar array to generate power for satellite subsystems, attitude control and maneuvering requirements while the chemical fuel is used only for the transfer orbits. Moreover, the full electrical satellite uses the solar array generated power for all missions, including GTO, maneuvering, attitude control and subsystems power requirements. As a result, the figures show that the largest required area of the solar array will be in case of using full electrical propulsion. Also, the required solar array area for the hybrid satellite is larger than that in case of the full chemical satellite. In addition, there is a linear proportional relation between the designed lifetime of the satellite and the required solar array area since as the satellite lifetime increases, the manufactured solar array area should be increased, respectively. 3.

DESIGN OF THE LI-ION BATTERY During the non-shadowing periods, the satellite charges the batteries using the generated power of the solar arrays. The battery storage capacity is used to provide the satellite subsystems with their required power during the eclipse periods. This is in addition to supporting the required station keeping maneuvers and the attitude control operations in the eclipse periods, if needed, in case of using either full electrical or hybrid propulsions. Initially, the battery sizing for the full chemical satellite can be calculated using the following procedure: a. Selecting the type of the batteries Batteries technology can offer many types to the satellite manufacturing, such as Nickel Cadmium (NiCad), Nickel Hydrogen, Lithium Ion (Li-ion), and Sodium Sulfur. Li-ion batteries have the efficient qualification for the space technology and consequently, this type of batteries are currently the common storage devices for various satellite applications. In this paper, an advanced type of Li-ion batteries, called Li-ion VES180, is selected for the presented design due to its low mass and high storage capacity. For the Liion VES180 batteries, the cell voltage is 𝑉 = 3.6 Vdc and the energy density 𝐸 is 150Wh/Kg [11]. The Depth of Discharge (DOD) is assumed to be 80 % and the satellite bus voltage is 𝑉 = 120 Vdc [4]. b. Determining the number of cells (NB) needed in the satellite Li-ion VES180 batteries: 𝑁 =

=33.33≅33cells

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Therefore, the applied satellite bus voltage 𝑉 can be expressed as: 𝑉 =𝑁 × 𝑉

(10)

= 118.8 𝑉𝑑𝑐

c. The total capacity (𝐶) of the satellite batteries is given by 𝐶=

(

)(

)

(

)(

)

= 96.84 𝐴ℎ

(11)

d. The required storage capacity (𝐶 ) of the batteries can be calculated as (12)

𝐶 = 𝐶 × 𝑉 = 11.5 𝐾𝑊ℎ

e. Estimating the total mass of the satellite batteries (MBatt), required during eclipse The last step is to estimate the total mass of the batteries, required to satisfy the spacecraft power requirements during eclipse. This can be calculated as shown in the following equation [12]. 𝑀

(13)

= 76.67 𝐾𝑔

The previous calculations are executed for the full chemical satellite and in this section, a comparison between the different propulsion techniques will be performed using the same previous steps. As mentioned before, the required generated power (𝑃 ) from the solar array for the hybrid satellite is 10.0311.21 kW whereas for the full electrical satellite, it is 28.63-33.87 kW. Consequently, the calculated results show that the required storage capacity and the mass of the batteries for hybrid propulsion satellite will increase to be 15.04-16.81 kWh and 100.27-112.07 kg, respectively. However, for full electrical satellite, the required capacity and the mass of the batteries are 42.95-50.8 kWh and 286.33-338.67 kg, respectively. 4.

ECONOMIC ANALYSIS For a commercial vision, the propulsion is the predominant factor in the spacecraft mass and this will affect the launching cost. In the satellite market, the main goal is to reduce the weight of the satellite while maintaining the possibility of increasing the satellite payload. Table 1. Comparison between the three propulsion types for a GEO satellite of 10-15 years lifetime Type of propulsion Full chemical Hybrid Full electrical

Fuel mass (kg) 1693 1230 -

Weight of solar array (kg) 109.57 143.29-160.14 312.27-483.86

Weight of Batteries (kg) 76.67 100.27-112.07 286.33-338.67

Weight of solar system (kg) 186.24 243.56-272.21 598.6-822.53

Reduction in Satellite weight (%) 22-24% 50-60%

Table 1. demonstrates a comparison between the propulsion techniques for a GEO satellite of 10-15 years lifetime. As shown, the hybrid satellite offers about 22-24% reduction in the satellite weight. However, the full electrical satellite can offer about 50-60% reduction in the satellite weight since it saves the huge mass of the fuel and the propellant tanks in spite of it needs an extra solar system weight, comparing to the full chemical satellite. Therefore, the usage of the full electrical propulsion technology in the satellite market proposes superior economic benefits as follows: 4.1. Increase the satellite payload for the same satellite weight In the economic present value analysis, there are two methods of payment; uniform and geometric gradient payments. This paper will follow the uniform approach, where all the instalments of payment are equal. 𝑋 =𝐴∗

(

)

(14)

Denote (X) as the initial payment, (A) is the instalment amount of payment, (i) is the annual interest rate and (Y) is the number of annual instalments, which is considered to be equal to the satellite lifetime [13].

Design PV system for a small GEO satellite and studying the effect of using … (A. Lotfy)

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The (14) is only effective when the following conditions are fulfilled; the first instalment is paid after one year from the initial payment (X), the amount of the instalments are equal and the durations between the periodic instalments are also equal. In this paper, the satellite is used for communication purposes. Thus, the satellite payload consists of a number of transponders, each contains a number of channels and each channel offers a revenue every year during the whole satellite lifetime. The following discussion will emphasize the achieved revenue of the full electrical satellite over the full chemical one. Assume that 𝑤 is the satellite saved weight due to using the electrical propulsion with respect to the chemical propulsion and 𝑤 is the transponder weight. Thus, ( ) expresses the excess number of transponders that can be used in the full electrical satellite, for the same total weight of the full chemical satellite. Therefore, the number of excess channels in the full electrical satellite will be K ,where (K) is the number of channels per transponder. If the annual renting payment per channel is (R), the annual profit (AP), due to the saved weight, will be expressed as: AP= K

(15)

In case of renting all the channels of the satellite, the extra cost of the full electrical satellite with respect to the full chemical satellite for the designed lifetime is given by: 𝑃

= 𝑃 +𝑃

(16)

where (𝑃 ) is the revenue loss due to the delayed usage of all channels, which is attributable to the GTO duration (n) till reaching the GEO. Also, (𝑃 ) is the extra cost due to excess solar system, which includes the extra solar array and batteries cells. From (14) and (15), the extra cost of the full electrical satellite for the designed lifetime (𝑃 ) can be expressed as: 𝑃 =K R(

) (

(

)

(17)

In the previous formula, it should be noted that both of the satellite lifetime (Y) and the GTO duration (n) are expressed in term of years. Also, the revenue loss due to the delayed usage of the satellite channels (𝑃 ) is presented as: 𝑃 =𝑛𝑁 𝑅

(18)

where𝑁 is total number of channels in all the satellite transponders. Therefore, the cost of the excess solar systems can be expressed as: 𝑃 =[K( ) (

(

) (

)

)]-(𝑛 𝑁 𝑅)

(19)

Figure 4 represents the analytical results of the economic study for the full electrical satellite with respect to the full chemical one and it is expressed under the following parameters; the satellite has a lifetime of 12 years and 288 channels in all its transponders. Also, the number of satellite transponders is 24, the GTO duration (n) is six months and the transponder weight (𝑤 ) is assumed to be 12 kg. From table 1, it is observed that the saved weight (𝑤 ) in the full electrical satellite over the full chemical one in worst case is about 1090 kg.

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Figure 4. The interest rate versus channel annual renting payment under various values of extra cost due to excess solar system Figure 4 illustrates the relation between the applied annual interest rate and the annual renting payment per channel for various values of the the extra cost, which is due to excess solar system manufacturing. The results show that as the applied interest rate increases, the estimated amount of the channel renting payment should be increased to have a considerable revenue to the satellite owner. For the same value of the annual interest rate, the figure demonstrates also a linear proportional relation between the extra cost of the solar system and the revenue due to the annual renting payment of the satellite channels. This means that as the extra cost of the solar system increases, which is charged in case of adding more payload, the revenue of the annual channels renting payment increases. This is because the success in reducing the satellite weight gives the opportunity to increase the satellite payload, and consequently add extra channels, through which biggest profits can be achieved from their annual rental. 4.2. Same number of transponders and satellite weight reduction A new economical key factor will be appeared in this case, which is the launching cost. For the same number of satellite transponders, the launching cost will decrease in a descending degree from full chemical to hybrid to full electrical propulsions due to the reduction in the relative satellite weight. As a comparison, the launching cost for a GEO satellite of 24 transponders is 112,705,188 M$, 96,253,506 M$ and 75,484,188 M$ using chemical propulsion, hybrid propulsion and electrical propulsion, respectively. It should be noted that each Kilogram in the GEO satellite launching into space may cost around €30,000/kg [14]. In this paper, the satellite manufacturing cost is fixed to an estimated value of 250 million dollars, regardless the propulsion type. The total cost of the satellite, including the launching and the manufacturing costs can be expressed as: 𝑃 =𝑁 R(

(

)

(20)

where (𝑁 ) is the total number of channels in all the satellite transponders, (R) is the channel annual rent, (Y) is the satellite lifetime and (i) is the annual interest rate. Design PV system for a small GEO satellite and studying the effect of using … (A. Lotfy)

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Figure 5 illustrates the relation between the annual interest rate versus the channel annual renting payment for the different types of propulsion. It should be noted that PTC, PTH and PTE represent the total cost of the full chemical satellite, the hybrid satellite and the full electrical satellite, respectively. For the same annual interest rate, the lowest annual renting payment per channel is offered in case of the full electrical satellite. The hybrid satellite offers a higher annual channel renting, which reaches its highest payment in case of the full chemical satellite. Therefore, the full electrical satellite presents superior economic advantages due to its competitive annual renting payment of the channels. Consequently, it can easily attract the service providers to rent its channels, comparing to the hybrid and the full chemical satellites of the same coverage areas. This means that the full electrical satellite can achieve a higher occupancy rate and accordingly greater revenues.

Figure 5. The interest rate versus the channel annual renting payment under various values of the total satellite cost due to using different propulsion techniques 5.

CONCLUSION The Electric propulsion is currently one of the most superior technologies and key players in the market of the telecommunication GEO satellites. This paper studied an optimum design of the solar PhotoVoltaic (PV) power system for different types of propulsions; full chemical, hybrid and full electrical satellites. The presented design uses the triple junction (3J) solar cells along with an advanced type of Li-ion battery storage system, Li-ion VES180 batteries. Analytical calculations are performed to evaluate the designed system using the various propulsion types. The technical calculations include the solar array area and mass, the required number of the assembled solar cells in addition to the size and mass of the batteries. Using the full electrical propulsion, the design can efficiently fulfil all the satellite power requirements, during both the transfer orbits and the on-station phases, while offering a big fund in the satellite total cost. This is because in spite of its requirement to have an extra solar array area and batteries capacity, to fulfill the orbit transition and maneuvering operations, it preserves an enormous fuel saving and thus magnificent overall weight reduction that reached 60%, compared to the chemical satellites. Finally, an economic analysis is applied to compare between the propulsion technologies and it achieves to conclude the benefits and the commercial feasibility in two various scenarios; the first is done by analyzing the economic benefits in case of fixing the satellite weight while increasing the number of transponders. The results draw a high revenue in case of using the electrical propulsion due to the massive increase in the extra number of satellite channels and consequently, in the financial rental returns. However, the second scenario discusses the saving profits due to reducing the satellite weight while using the same number of satellite transponders. The electrical satellite achieves the best results due to the huge fuel saving and consequently, the big fund offered in the launching cost. As a conclusion, the electrical propulsion draws great advantages and profits to the satellite market. Int. J. of Adv. in Appl. Sci. Vol. 8, No. 1, March 2019: 54 – 63

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REFERENCES [1] Hendrick Lubbers, Alexander Schneider, Markus Peukert., “Electric propulsion applications in the smallGEO product line,” Deutscher Luft- und Raumfahrtkongress, 2014. [2] Sheth, Vatsal., "Spacecraft Electric Propulsion–A review," International Journal of Research in Aeronautical and Mechanical Engineering vol.2(9), pp. 43-55, 2014. [3] Mohamed, Ahmed Mokhtar, et al., "Trade-off analysis of low earth orbit spacecraft power supply system by Genetic algorithm." Aerospace Conference, 2016 IEEE. IEEE, 2016. [4] J. R. Wertz, D. F. Everett and J. J. Puschell, “Space Mission Engineering: The New SMAD,” Eds. Hawthorne, CA, USA: Microcosm Press, 2011. [5] Azur-space “30% Triple Junction GaAs Solar Cell”, www.azurspace.com. [6] Donald Rapp, Tom Hamilton, “Solar Cell and Array Technology for Future Space Science Missions”. California institute of technology Pasadena, California, pp. 8, 2002. [7] De Tata, Marco, et al. "SGEO electric propulsion subsystem development status and future opportunities," Proc. 33rd Int. Electr. Propuls. Conf. 2013. [8] Dudeck, M., et al., "Plasma propulsion for geostationary satellites and interplanetary spacecraft." Romanian Journal of Physics, vol. 56, pp. 3-14, 2011. [9] Busek space propulsion and systems “BHT-1500 Busek Hall Effect Thruster”. [10] Leporini, A., et al. "Development of a 20 kW Class Hall Effect Thruster," Proceedings of the Space Propulsion 2016. [11] Saft-space “Saft batteries… powering outer space for 50 years: extreme performance batteries meeting the demands of space applications”, www.saftbatteries.com [12] Oraby, Osama A., et al., “UASat Solar Array Design and Performance Characteristics”. 2014. [13] L.Blank and A. Tarquin “Basic of Engineering Economics” McGraw Hill, New York, pp.29, 2008. [14] A. Bacon, "Micro Launch: The Electric Rocket"13th Reinventing Space Conference 9-13th 2015.

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International Journal of Advances in Applied Sciences (IJAAS) Vol. 8, No. 1, March 2019, pp. 64~72 ISSN: 2252-8814, DOI: 10.11591/ijaas.v8.i1.pp64-72

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Frequency control of islanded microgrid using fuzzy-PI and autotuned controllers Jitender Kaushal, Prasenjit Basak Electrical and Instrumentation Engineering Department, Thapar Institute of Engineering and Technology, India

Article Info

ABSTRACT

Article history:

Any mismatch between generation and demand causes frequency to deviate from nominal value which affects the microgrid operation and reliability of power flow. The load frequency changes abnormally, which is fuzzy in nature, due to low system inertia and unpredictable variation in wind and solar irradiance level. So a frequency controller is needed to solve this problem meeting generation and demand of an islanded microgrid system considering the fuzziness in frequency fluctuation. This paper presents a case study of a hybrid microgrid system consisting of PV system, wind turbine generator set, diesel generator set along with storage facility and equipped with a proposed fuzzy-PI controller for frequency control under islanded condition. This controller shows satisfactory steady-state response. Further, performance of the proposed fuzzy-PI controller is verified with that of an autotuned PI controller to get faster response. The change in frequency is found minimum in case of autotuned PI controller as compared to fuzzy-PI controller. The proposed fuzzy-PI controller is validated based on ITAE (4-7%) which is higher than that attained form autotuned-PI controller. The developed model is simulated in Matlab/Simulink environment in this case study.

Received Dec 1, 2018 Revised Jan 25, 2019 Accepted Feb 7, 2019 Keywords: Autotuned PI controller Fuzzy-PI controller Microgrid frequency control

Corresponding Author: Prasenjit Basak, Electrical and Instrumentation Engineering Department, Thapar Institute of Engineering and Technology, Patiala, Punjab, India. Email: prasenjit@thapar.edu

INTRODUCTION As the power demand is increasing at an alarming rate, pressure on conventional energy resources has increased leading to depletion of fossil fuels. So, non- conventional sources such as wind, PV, fuel cells, microturbines are being used to generate electrical power [1]. Microgrid (MG) is a concept that utilizes the energy generated by these distributed energy resources. Microgrids not only help in extracting power from non-conventional resources but also improves the power reliability and helps the concept of smart grid to emerge [2]. The concept of microgrid was first introduced by an organization namely, the consortium for Electric Reliability Technology Solutions (CERTS) in 1998 [3]. There are various advantages and disadvantages associated with a microgrid. One major advantage of microgrid is that it provides clean power as there is little or negligible pollution associated with microsources unlike burning of fossil fuels which act as a major source of pollution and global warming. Secondly, these systems can be used in geographically isolated conditions where it is not possible to extract power from conventional sources and set up large plants. Hence, power can be extracted from locally available microsources like wind, biomass, solar, geothermal, etc. [4]. Output power fluctuation is one major problem associated with microgrid which further leads to frequency and voltage fluctuation.

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A microgrid can operate in two modes, namely grid connected and islanded mode [5]. In grid connected mode the MG is connected to the utility grid, whereas in islanded mode it behaves as an independent entity having no interaction with utility grid. When operating in grid connected mode, the control of the system is majorly taken care of by utility grid. But under islanded condition the control of system is purely regulated by microgrid central controller. So, in order to ensure a continuous and reliable supply of power from microgrid, a proper control system for microgrid needs to be designed. There are various control strategies available for frequency control of microgrid; droop control is one of the conventional control strategies used for frequency restoration [6]. Nowadays intelligence-based controllers are used as the control scheme which needs to be fast and efficient. In this paper fuzzy PI based controller is used where output of fuzzy is fed as an input to PI in order to adjust the control parameters P and I [7]. In this case study, PV and wind generators have been used for the consideration of a MG. The power output from these two sources completely depends on climatic condition such as solar insolation and wind speed. So, a backup system is required to continue power supply at times when it is not possible to extract required power from renewable sources due to climatic variation. A diesel generator set and storage system for backup purpose has been incorporated. For storage a flywheel and battery systems are used. 2.

PROPOSED HYBRID MICROGRID SYSTEM A hybrid MG system has been considered as presented in [8].The proposed load frequency control model of MG is presented in Figure 1. The Mathematical model of this system is considered and is represented in the form of transfer function [9]. All the values are considered in per unit. The p.u. values required for load frequency control (LFC) model are given in Table 1. Microgrid consists of photovoltaic system, wind turbine generator set and a diesel generator set for generating the required power. The energy storage systems used are flywheel and a battery. These storage systems store the surplus power when demand is less than the generation and provide the required power when the renewable sources are not able to cope up with the load demands. The diesel generator set can also be used to meet load demands when power generated by PV and wind system is insufficient. The flywheel system, battery storage system and diesel generator system can also be termed as backup systems.

Figure 1. A Proposed hybrid microgrid model The net power generated ( Pnet ) can be expressed by (1)

Pnet  PPV  PWTG  PDEG  PBES  PFES where,

PBES

PPV

is power generated by PV system,

is power of battery storage system,

PFES

(1)

PWTG

is power generated by WTG,

PDEG

is power of DEG,

is power of flywheel system.

For simulation purpose a load of 350 kW is considered and rated power of all generating units used in microgrid system is presented in Table 2. Frequency control of islanded microgrid using fuzzy-pi and autotuned controllers (Jitender Kaushal)

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ISSN: 2252-8814

Table 1. Parameters for proposed model Parameters Tm Td Tic(s) Tfw(s) Tbs(s) R(Hz/pu) D(pu/Hz) Ti(s) 2H(pu/s)

Table 2. Power ratings of generating units

Value (pu) 0.42 0.081 0.004 0.1 0.12 3 0.0156 0.041 0.166

Generating unit PV panel WTG Diesel gen set Battery storage Flywheel storage Applied load

Rated power (kW) 30 100 160 45 45 350

2.1. Photovoltaic system Photovoltaic is a specialized term for sun-oriented vitality. PV cells are generally made of silicon material that discharges electrons when presented to light. The measure of electron discharge depends upon the power of light occurrence on it. This Silicon is secured with matrix that aids electrons specifically bearing to deliver electric current. These currents are guided into wire which is associated with battery or DC apparatus to convert the DC into AC [10]. Block diagram of a PV system is represented in Figure 2. For analysis purpose the rating of PV module used is 150W. Maximum current and voltage are 4.35A and 34.5V respectively, which produce a maximum power of 150.07W.

Figure 2. Block diagram of PV system The transfer function for PV system is given as in (2) 𝐺

∆

(2)

∆

where, ∆𝑃 = Change in output power of PV system, ∆Φ = Change in solar irradiation. For analysis purpose change in solar irradiation is considered using a step function. 2.2. Wind generator system The wind generator system produces power by the virtue of blowing winds. The energy encapsulated by the blowing winds is used to run the wind generator set as shown in Figure 3. The output power produced by wind turbine generator set totally depends upon velocity of wind. For the purpose of simulation, the wind speed is assumed to be constant 10 m/s; and it delivers a constant power of 100 kW to the microgrid [11].

Figure 3. Block diagram of WTG set The (3) represents the transfer function for wind generator system 𝐺

∆ ∆

Int. J. of Adv. in Appl. Sci. Vol. 8, No. 1, March 2019: 64 – 72

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where, ∆𝑃 =Change in output power of wind generator system. ∆𝑃 =Change in mechanical power of wind turbine. 2.3. Diesel generator set Diesel generator set is used to ensure the continuous supply of power, when the PV and wind systems are not able to cope up with the power demand; the extra power demand is met by Diesel engine generator. Fuel consumption in diesel engine model depends upon mechanical power input and speed [12]. The governor adjusts the fuel inrush and thus regulates the power output of engine and generator. This helps in frequency restoration and the controller unit is used in conjugation with diesel model. The transfer function for DEG is presented in (4). 𝐺

∆ ∆

(4)

where, ∆𝑃 =Change in output power of DEG, ∆𝑓 = Deviation in system frequency. 2.4. Storage system Battery and flywheel systems are used for storage purpose; they play a vital role in maintaining system stability [13].When the power generated by renewable sources is in surplus; it is captured and stored by storage systems. The stored energy is delivered back to the system when power generated by renewable sources is insufficient to meet load demand. The (5) and (6) represent transfer function for flywheel and battery storage system respectively. 𝐺

∆

(5)

∆

where, ∆𝑃 =Change in exchanged power of flywheel system. ∆𝑓 =System frequency deviation 𝐺 ∆𝑃

∆ ∆

(6)

= Change in exchanged power of batter storage system, ∆𝑓 = Deviation in system frequency.

2.5. Power system: utility side There are two important terms associated with power system such as inertia and power oscillation damping. Reliability of any power plant depends upon the inertia associated with large rotating masses. The imbalance between supply and demand of power system is overcome by managing inertia. Damping in system could occur on account of any abnormal condition in the system. Transfer function for microgrid system is represented by (7) ∆𝑓 =

(7)

where, 𝐷 = Load damping constant, 2𝐻 = inertia constant 3.

RESEARCH METHOD: MICROGRID FREQUENCY CONTROL METHODOLOGIES A lot of frequency control techniques for microgrid are available. Earlier techniques, like frequency droop control, tie line frequency control is presented in many literatures. These systems are slow, complex and not very efficient. With advancements in power system, the need for intelligent controllers has emerged. In this case study fuzzy-PI controller is used. The frequency error and rate of change of error are provided as input to the fuzzy controller and the output of fuzzy is provided as input to PI controller. The fuzzy-PI controller is validated using autotuned- PI controller in the Matlab-Simulink environment. 3.1. Fuzzy based PI control For a PI controller the values of P and I are generally pre-assigned. If any fluctuation occurs in the system, model of the PI controller is not able to take account of them and hence the performance of controller degrades. Therefore, we use fuzzy based PI controller which provides an intelligent control by updating the Frequency control of islanded microgrid using fuzzy-pi and autotuned controllers (Jitender Kaushal)

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ISSN: 2252-8814

PI controller parameters if there is any variation in system operation condition. As represented in Figure 4 fuzzy system output is provided as an input to PI controller which further controls the plant with updated values of P and I. The fuzzy inputs i.e. the error and change in error are provided to fuzzy logic controller. They are fuzzified using seven membership functions Positive big (PB), Positive medium (PM), Positive small (PS), Zero (ZE), Negative big (NB), Negative medium (NM), Negative small (NS). The membership functions for input and output are depicted in Figure 5. A fuzzy controller consists of following four main elements [14]. 3.1.1. Fuzzification It is the process of converting the user inputs provided to controller into a form that inference system can implement the provided rules.

Figure 4. Control structure of fuzzy-PI system 3.1.2. Rule base It is a set of ‘if’ and ‘then’ rules, which consists the fuzzy logic representation of user’s description on attaining the best possible control. Rule base for proposed control scheme is represented in Table 3. 3.1.3. Inference mechanism It imitates the user’s idea of attaining a best possible control, in order to understand and apply the rules for achieving a good control. Table 3. Rules for proposed fuzzy-PI controller Kp Ki

e ce NB NM NS ZE PS PM PB

N P P P P P P P P P P P P P

Z Z P Z P P P P P P P Z Z Z

N N Z Z Z Z P P Z Z Z Z N N

N N N N N N Z Z N N N N N N

N N Z Z Z Z P P Z Z Z Z P P

Z Z P Z P P P P P P P Z Z Z

P P P P P P P P P P P P P P

3.1.4. Defuzzification It is the process which finally provides the input to the system, by converting the decision of inference system.

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(b)

(c)

Figure 5. Membership function of (a) error or change in error (b) Kp (c) Ki 3.2. Autotuned PI Autotuning is a process that automatically computes the value of control parameters for any given system to bring out the best possible control without user interaction. It is much more efficient and reliable as compared to manual computation of parameters. Autotuning process basically includes three main steps: a. Choosing the model to be processed. b. Collecting the data associated with the process and fitting it into the model. c. Computing the control parameters. The process identification is done basically by two methods; such as step response method and relay feedback method. The data about the process can be provided either by triggering the process intentionally or by observing the process behaviour under normal conditions. After the attainment of data, it is converted into process description. The tuning rules can be formulated in three ways: a. Tuning the parameters in such a way that the process output matches with the reference model used. b. Computing parameters based on some closed loop system description. c. Tuning based on human description. All these methods are based on solving a certain set of equations. The detailed process of Autotuning is presented in [15]. The tuning of PI controller is done automatically using the PI controller block readily available in MATLAB/Simulink library. The tuned values of P and I attained are 1.4999 and 8.7186 respectively. 3.2.1. Controller modes PI controller uses proportional (P) mode and integral mode (I) for computing a control signal. The PI control signal is computed as given in (8). 𝑢(𝑡) = 𝐾 𝑒(𝑡) + 𝐾 ∫ 𝑒(𝑡)𝑑𝑡

(8)

The above equation can be written in equivalent form as 𝑢(𝑡) = 𝐾 𝑒(𝑡) +

∫ 𝑒(𝑡)𝑑𝑡

(9)

a. Proportional mode The control law for proportional mode is given by 𝑢 (𝑡) = 𝐾𝑒(𝑡) + 𝑈

(10)

here, 𝑢 (𝑡) is the output of proportional controller, K represents the gain of the system and 𝑈 is the bias or reset value. The P makes the action of controller proportional to the error. The P action depends on instantaneous values of error. Only for non-zero values of error the output value of controller will be nonzero. This means that the value of P at steady state will be zero, but this is not feasible. So, in order to maintain value of P controller at a constant level a bias is added to the system. Which means that even if value of error is zero the value of controller output will be some constant and hence maintain the controller action. But if an integral controller is used in conjugation with P controller, then there is no need to add an external bias. The reset is attained by action of I controller. Some characteristics of proportional control are stated below. Frequency control of islanded microgrid using fuzzy-pi and autotuned controllers (Jitender Kaushal)

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Increasing the value of P control will reduce the steady state error. Increasing the value of P beyond certain point will only result in increased overshoot. It helps in reducing the rise time

b. Integral mode The output generated by integral action is proportional to accumulated error. The I-control signal is given as: 𝑢 (𝑡) =

(11)

∫ 𝑒(𝑡) + 𝑢(0)

here;

is the reset time or integral time constant and u (0) is the output of controller at t=0.

The integral controller considers the sum of all the errors. When input of integrator is e, steady state cannot be achieved unless e is non-zero. So, it ensures a zero error at steady state. The I-control not only considers the present value of error but also accounts for the previous value of error. The action of I controller is slow, and it does not respond to sudden changes quickly. -

Some key features of the I-controller are: It helps in eliminating the steady state error. Increasing the value of I beyond certain limit leads to increase in overshoot. It reduces the rise time.

RESULTS AND DISCUSSION For performing simulations three different cases have been considered by varying input signals of PV, wind and change in load. In the case studies, to carry out comparative analysis of Fuzzy PI controller and autotuned PI controller, the results for both the schemes have been depicted. The effectiveness of proposed control scheme is illustrated by simulating the model in MATLAB/Simulink environment using Sim-Power blocks. Case Study 1 The model is simulated by considering a disturbance of 0.2 pu in load and keeping other two signals i.e. solar insolation and wind speed constant to value of 1000 W/m2 and 10 m/s respectively. Both the systems are studied and the values of ITAE (Integral of time absolute error) are observed. The outputs for both the schemes are presented in Figure 6 (a) and (b). It is noted that the values of ITAE in both cases is relatively same. The autotuned scheme has shown fast response time and steady-state level compared to fuzzy-PI.

(a)

(b)

Figure 6. Change in frequency vs. time graph for (a) autotuned-PI controller (b) fuzzy-PI controller Case Study 2 Under second case the wind speed is assumed to be constant 10 m/s and a change of 0.2 pu in both load and PV system is considered. The response for autotuned and Fuzzy-PI scheme is presented in Figure 7 (a) and (b) respectively. The values for control parameters and ITAE are presented in Table 4. It can be Int. J. of Adv. in Appl. Sci. Vol. 8, No. 1, March 2019: 64 – 72

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inferred from the values that error in case of autotuned PI is less than that in fuzzy-PI control scheme. But the fuzzy-PI scheme also generates a good response based on the referred system problem.

(a)

(b)

Figure 7. Change in frequency vs. time graph for (a) autotuned PI (b) fuzzy-PI control scheme Case Study 3 The simulation in this case is done where change in all three signals i.e. load, solar insolation and wind speed of 0.2 pu are considered. Based on the simulation results presented in Figure 8 (a) and 8 (b) it can be inferred that the peak values in both the cases are almost same. The autotuned controller improves the performance of system with minimum value of ITAE. But the value of ITAE for Fuzzy-PI is also close to that obtained while using autotuned controller. The mapping between autotuned and fuzzy based PI controller is presented in Figure 9 corresponding to ITAE. It is observed that value of ITAE in autotuned-PI controller is less as compared to fuzzy-PI controller for all three cases.

(a)

(b)

Figure 8. Change in frequency vs. time graph for (a) autotuned PI (b) Fuzzy-PI controller Table 4. PI controller parameters 1 2 3

Autotuned PI P I ITAE(%age) 1.4999 8.7186 0.1808 1.4999 8.7186 0.176 1.4999 8.7186 0.2045

P 1.000011 1.000048 1.000028

Fuzzy-PI I 7.000045 7.000192 7.000810

ITAE (%age) 0.1944 0.1886 0.2137

Figure 9. Mapping of autotuned and fuzzy-PI based on ITAE Frequency control of islanded microgrid using fuzzy-pi and autotuned controllers (Jitender Kaushal)

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CONCLUSION The integration of DER’s into a microgrid system consisting of varying load can cause a frequency fluctuation issue. So, to resolve this, fuzzy-PI based controller is designed to minimize the frequency deviation in the system. In this case study, the performance of controller is evaluated by considering three different cases of disturbance, by causing variation in input signal to PV panel, wind speed and change in load. Further the proposed scheme is compared with autotuned-PI control where PI parameters are automatically tuned for the readily available controller. In this case study, it is evident from the results that both the schemes generate satisfactory results. The overshoots in both cases are being relatively same. The case study results show that the autotuned PI controller performs better than fuzzy-PI controller by generating a faster steady state response. The value of ITAE in case of autotuned PI controller is observed less as compared to fuzzy-PI controller. In future proposition the autotuned and fuzzy-PI control technique would be used in case of two-area hybrid microgrid system. REFERENCES [1] R. H. Lasseter, “Dynamic Distibution using (DER) distributed energy resources,” Proc. IEEE Power Eng. Soc. Transm. Distrib. Conf., pp. 932–934, 2006. [2] T. Senjyu, T. Nakaji, K. Uezato, and T. Funabashi, “A Hybrid Power System Using Alternative Energy Facilities in Isolated Island,” IEEE Trans. Energy Convers., vol. 20, no. 2, pp. 406–414, 2005. [3] H. Nikkhajoei and R. H. Lasseter, “Distributed generation interface to the CERTS microgrid,” IEEE Trans. Power Deliv., vol. 24, no. 3, pp. 1598–1608, 2009. [4] S. Baudoin, I. Vechiu, and H. Camblong, “A review of voltage and frequency control strategies for islanded microgrid,” Syst. Theory, Control Comput. (ICSTCC), 2012 16th Int. Conf., vol. 33, no. 0, pp. 1–5, 2012. [5] J. A. P. Lopes, C. L. Moreira, and A. G. Madureira, “Defining control strategies for microgrids islanded operation,” IEEE Trans. Power Syst., vol. 21, no. 2, pp. 916–924, 2006. [6] A. Klem, M. H. Nehrir, and K. Dehghanpour, "Frequency stabilization of an islanded microgrid using droop control and demand response," 2016 North American Power Symposium (NAPS), Denver, CO, pp. 1-6, 2016. [7] M. S. Bisht and Sathans, "Fuzzy based intelligent frequency control strategy in standalone hybrid AC microgrid," 2014 IEEE Conference on Control Applications (CCA), Juan Les Antibes, pp. 873-878, 2014. [8] Maneesh, “Frequency control of a microgrid by using PI controller,” 2015 Int. Conf. Energy, Power Environ. Towar. Sustain. Growth, ICEPE 2015, 2016. [9] D. J. Lee and L. Wang, “Small-signal stability analysis of an autonomous hybrid renewable energy power generation/energy storage system part I: Time-domain simulations,” IEEE Trans. Energy Convers., vol. 23, no. 1, pp. 311–320, 2008. [10] S. Satapathy, K. M. Dash, and B. C. Babu, “Variable step size MPPT algorithm for photo voltaic array using zeta converter - A comparative analysis,” 2013 Students Conf. Eng. Syst. SCES 2013, 2013. [11] P. Wang and R. Billinton, “Reliability benefit analysis of adding WTG to a distribution system,” IEEE Trans. Energy Convers., vol. 16, no. 2, pp. 134–139, 2001. [12] G. S. Stavrakakis and G. N. Kariniotakis, "A general simulation algorithm for the accurate assessment of isolated diesel-wind turbines systems interaction. I. A general multimachine power system model," in IEEE Transactions on Energy Conversion, vol. 10, no. 3, pp. 577-583, 1995. [13] S. Singh, M. Singh, and S. C. Kaushik, “Optimal power scheduling of renewable energy systems in microgrids using distributed energy storage system,” IET Renew. Power Gener, 2016. [14] M. S. Bisht and Sathans, “Fuzzy based intelligent frequency control strategy in standalone hybrid AC microgrid,” 2014 IEEE Conf. Control Appl. CCA 2014, pp. 873–878, 2014. [15] C. Andersson and M. Lindberg, “Autotuning of a PID-controller,” October, p. 38, 2004.

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International Journal of Advances in Applied Sciences (IJAAS) Vol. 8, No. 1, March 2019, pp. 73~82 ISSN: 2252-8814, DOI: 10.11591/ijaas.v8.i1.pp73-82

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Comparative study of the most efficient maximum power point tracking techniques for a photovoltaic system I. M. Abdel-Qawee1, H. G. Hamed2, Maged N. F. Nashed3, Ayman Y. Yousef4, Khaled M. Hasaneen5 1,2,4,5

Electrical Engineering Department, Faculty of Engineering at Shoubra, Benha University, Egypt Department of Power Electronics and Energy Conversion, Electronics Research Institute, Egypt

Article Info

ABSTRACT

Article history:

In Photovoltaic (PV) systems, maximum power point tracking (MPPT) is an important challenge which faces this system. In this paper, a comparative study between two high efficient MPPT control techniques is presented. These techniques are based on the incremental conductance (IC) method. The first technique is based on optimized PI-controller gains and the other is based on Fuzzy Logic Control (FLC). The comparison between the algorithms is investigated under different environmental conditions. The comparison criteria which are taken into consideration are: the percentage tracking factor (%TF), the tracking time, the ripple voltage in the steady-state, the dynamic response, and the complexity of implementation.

Received Dec 7, 2018 Revised Feb 5, 2019 Accepted Feb 20, 2019 Keywords: Fuzzy logic control Genetic algorithm Incremental conductance MPPT Photovoltaic

Corresponding Author: I. M. Abdel-Qawee, Electrical Engineering Department, Faculty of Engineering at Shoubra, Benha University, 108 Shoubra street, Cairo, Egypt. Email: islam.ahmed@feng.bu.edu.eg

INTRODUCTION One of the drawbacks of the PV system that the PV module conversion efficiency is low (only in the range from 12%-22%) [1]. Also, its efficiency is influenced by the change in the irradiance level and the cell temperature. Therefore, the maximum power obtained from the PV module for any electrical load should be extracted by using MPPT control at any irradiance level and cell temperature to improve the conversion efficiency of the PV module. The MPPT control techniques differ according to the required sensors, cost, control complexity, the time elapsed to reach the MPP (tracking time), and the required accuracy [2]. The MPPT control techniques can be divided into two categories, according to the accuracy, namely direct and indirect techniques [3]. Direct techniques are independent on previous knowledge about the PV modules characteristics, such as perturb and observe (P&O) method, incremental conductance method, fuzzy logic control method and artificial neural network (ANN) method. Indirect techniques need previous evaluation of PV module which is based on mathematical relationship obtained from empirical data; such as look-up table, open-circuit PV voltage, and short circuit PV current. The most usual MPPT control techniques also can be classified according to the implementation simplicity. The techniques can be divided into two main groups, [4]. The first group contains the conventional techniques such as: constant voltage method [2, 5], fractional open-circuit voltage and fractional short-circuit current methods [6, 7], perturb and observe method [2, 7-9], and incremental conductance method [2, 5, 10]. The other group contains the techniques which depend on the artificial intelligent (AI) techniques such as: Fuzzy logic control method [11, 12], and artificial neural network method [6].

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In this paper, a comparative study of the most efficient MPPT control techniques are investigated. These techniques are based on the concept of the IC method. The first technique is based on optimized PIcontroller gains and the other is based on FLC. The paper is organized as follows: in section 2, modelling and simulation of the PV module under changing the environment conditions using Matlab/Simulink and the concept of MPPT controllers are presented. In section 3, the proposed MPPT techniques based on IC are investigated. In section 4, a comparative study between the MPPT controllers are carried out under various scenarios in weather conditions. Finally, the main conclusion about each MPPT control technique are presented in the last section. 2.

MODELING AND SIMULATION OF A PV MODULE There are different electrical equivalent mathematical models used to describe the PV module. Some of these models are based on single-diode and two-diode models [13]. Due to simple implementation, less simulation time, and high efficient way for MPPT, the single-diode model is represented as shown in Figure 1, [2, 13-15]. The current source Iph represents the module photocurrent. Rs and Rsh are series and intrinsic shunt resistances of the module respectively that represent the cell losses. The current losses due to recombination losses in the cell are represented by the shunt diode. The current model of a PV module can be expressed as [16]:  V  I R I PV  I ph  I o exp PV PV s   aN sVt

  V PV  I PV R s 1    R sh  

(1)

And the power of a PV module can be obtained from:

P V PV I PV PV

  V  I PV R s V PV I ph  I o exp  PV  aN V   s t 

  V PV  I PV R s   1  R sh  

  

(2)

Figure 1. Equivalent circuit of a single-diode solar cell model The I-V and P-V characteristics of the PV module are obtained when the cell temperature (T) and irradiance level (G) are changed and compared with those given in the KC200GT PV module datasheet, [17]. Figure 2 shows the simulation results taking into account the effect of changing the cell temperature on the IV and P-V characteristics of the PV module. The effect of changing the irradiance levels on the I-V and P-V characteristics of the module are shown in Figure 3. Due to the high capital cost of PV modules, DC-DC converters with MPPT control techniques are essential in order to extract the maximum output power from the PV module under any weather condition. Usually the boost converter, Figure 4, is used as the required output voltage is higher than the PV output voltage. Therefore, number of the series PV modules is reduced when using the boost converter, and consequently, the cost of the PV system will be reduced [10]. The PV module output current is always continuous which is essential for efficient MPPT [18, 19]. There are many methods used to control the duty cycle of the boost converter to operate at the maximum power point (MPP) of the PV module. The IC method is one of the well-known methods [2]. This method is based on the fact that the slope of the P-V curve of the PV module is equal to zero at the MPP, i.e dPPV/dVPV = 0, positive in the left of the MPP, and negative in the right of the MPP. From this fact, the MPP can be found in terms of the IC of the PV module by the following expressions. Int. J. of Adv. in Appl. Sci. Vol. 8, No. 1, March 2019: 73 – 82

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dI dP d (V PV I PV )   I PV V pv PV  0 dV dV PV dV PV ΔI PV I    PV (a) ΔV PV V PV ΔI pv I pv or  (b) ΔV pv V pv ΔI PV I PV or  (c) ΔV PV V PV

(3)

Where: (a) represents the condition at the MPP, (b) represents the condition in the left of the MPP, and (c) represents the condition in the right of the MPP. The flowchart which describes this algorithm is shown in Figure 5 [20]:

(a)

(b)

Figure 2. I-V and P-V characteristics of the PV module at G=1000W/m2 and varying T. (a) I-V curves, (b) P-V curves of the PV module

(a)

(b)

Figure 3. I-V and P-V characteristics of the PV module at T=25 oC and varying irradiation level. (a)I-V curves, (b) P-V curves of the PV module

Figure 4. Boost converter Comparative study of the most efficient maximumpower point tracking techniques … (I. M. Abdel-Qawee)

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Figure 5. Flowchart of the incremental conductance method To improve the performance of the IC method and making it an adaptive, the IC method with conventional PI-controller gains can be used [2]. The basic idea of this algorithm is that the summation of the conductance and the IC (e), as obtained in (4), is equal to zero at the MPP, [6]. Therefore, this idea is used to generate an error signal as an input to a simple PI-controller. This error signal should be zero at the MPP [2, 6].

e

I PV ΔI PV  V PV ΔV PV

(4)

Simulink block diagram of this algorithm is shown in Figure 6. The error signal is compared to zero and the result is the input of the PI-controller which generates the desired duty cycle [2]. The PV module power response using this algorithm is shown in Figure 7. The proportional and integral controller gains (Kp, KI) are selected to be 0.1 and 20 respectively, using the trial and error process, such that the tracking time and the maximum peak-to-peak power fluctuation (PPV,max ) are minimized. It can be seen from this figure that the PPV,max in the steady state is nearly zero at all conditions of irradiance levels. The maximum tracking time (ttrack) is (34 msec) at start up, where the irradiance level at 0.8 p.u, while in the case of suddenly change of the irradiance level from 0.8 p.u. to 0.6 p.u and from 0.6 to 1 p.u., the ttrack is (18 msec) and (14 msec) respectively. However, the ttrack is (60 msec) when the irradiance level is suddenly changed from 1 p.u to 0.4 p.u. The effect of changing the controller gains of the PI-controller on the PV module power response is shown in Figure 8. Keeping KP=0.1, if KI is increased from 20 to be 35, the tracking time will be reduced and if it is decreased from 20 to be 5, the tracking time will be increased with slight effect on the PPV,max as shown in Figure 8(a). While changing the proportional gain with keeping KI=20 has a slight effect on the tracking time and the PPV,max as shown in Figure 8(b). It can be concluded from these simulation results that there are optimal values for KP and KI at which the PI-controller gives the best response. These optimal values can be obtained using an optimization technique. Also, it can be noticed that the proportional gain has a minor effect compared to the integral gain. Therefore, in the proposed technique, the integral PI-controller gain will be determined using the genetic algorithm (GA) optimization technique. Details of this algorithm is discussed in detail in the next section.

VPV IPV/VPV

e D dIPV/dVPV

ZOH1 IPV

ZOH2

Figure 6. Simulink block diagram of the IC method based on PI-controller Int. J. of Adv. in Appl. Sci. Vol. 8, No. 1, March 2019: 73 – 82

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250 G3=1 (PPV,max=200.15W)

200

G4=0.4 (PPV,max=79.61W) 79.7

G1=0.8 (PPV,max=160.49 W)

150

79.6 200.16 79.5 0.70.71 0.72

(W)

G2=0.6 (PPV,max=120.275W)

100

160.5

200.15 120.3

160.49

0 0

0.1

200.14 0.5 0.51 0.52

120.2

160.48 0.13 0.14 0.15

0.2

0.30.305 0.31

0.3

0.4

t (sec)

0.5

0.6

0.7

Figure 7. PV module power response of the IC method with PI-controller, at (KP = 0.1 and KI =20)

(a)

(b)

Figure 8. PV module power response of the IC method with PI-controller at, (a) different values of integrator gain, (b) different values of proportional gain 3. THE PROPOSED MPPT TECHINQUES 3.1. Incremental conductance method with optimized PI-controller gains As mentioned before, the integral control gain of the PI-controller has the major effect on the tracking time compared to the proportional gain. Therefore, in this algorithm, the GA optimization technique is used to select the optimal value of the integral control gain of the PI-controller. Flowchart of the algorithm is shown in Figure 9. To improve the performance of the PV module power response, the GA is used offline to obtain two values of KI, at low irradiance level (less than 0.5 p.u.) and at high irradiance level (above 0.5 p.u.). The obtained values of KI at the lowest irradiance level (0.2 p.u.) and at the highest irradiance level (1 p.u.) are found to be (45.5) and (20.3) respectively. Simulink block diagram of this algorithm is shown in Figure 10. The signal error (e) is compared to zero and the result, error, is the input of the discrete PI-controller with optimal integral gains. Where in the “discrete integral block” Ts is the controller sampling time. The appropriate integral gain depends on the value of the irradiance level that is measured using an irradiance level sensor (G). The value of Kp is assumed constant (0.1) at any irradiance level. The PV module power response using this algorithm is shown in Figure 11. It can be seen from this figure that the power fluctuations in the steady state is nearly zero at all irradiance levels. The tracking time is (34 msec) at the irradiance level 0.8 p.u. The tracking times in the case of suddenly change from 0.8 p.u. to 0.6 p.u. and from 0.6 p.u. to 1 p.u. are (18 msec) and (14 msec) respectively. The tracking time is (50 msec) when the irradiance level is suddenly changed from 1 p.u to 0.4 p.u. The comparisons between the power response of the PV module using IC method with conventional integral gain and with optimal integral gains are shown in Figure 12. The figure shows that both algorithms are nearly identical when the irradiance levels are above 0.5 p.u., where the integral control gain in both algorithms is nearly 20. However, at low irradiance levels (less than 0.5 p.u.), the IC method with optimal integral gain has lower tracking time than that with conventional integral gain. Comparative study of the most efficient maximumpower point tracking techniques … (I. M. Abdel-Qawee)

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ISSN: 2252-8814 Start

Randomly generate an initial population of KI Run Figure 10 then calculate the fitness function (|error| 2)

Roulette wheel

Crossover and Mutation operator

Yes

Iter <n c or CF >10 -4 No Store K I and the PV module power response nc : number off iteration CF: cost funtion

Δ PPV,max and ttrack are matched

Yes End

Figure 9. Flowchart of the GA optimization technique

Figure 10. Simulink block diagram of the IC method based on optimized PI-controller gains

Figure 11. PV module power response of the IC method with optimized PI-controller gains at KP = 0.1 and KI =20.3 at (G>0.5 p.u.), KI =45.5 at (G<0.5 p.u.)

Figure 12. PV module power response of the IC method with optimized and conventional PI-controller gains

3.2. Incremental conductance method based on fuzzy logic controller FLC, in general, is one of the most powerful control methods which is used in many applications to improve the performance of the system [21, 22]. In the PV system, FLC is able to design an adaptive step size of the duty cycle without having knowledge of the mathematical model of the system, even if the system has nonlinearity behaviour. The effectiveness of the FLC MPPT algorithm depends on the input and output Int. J. of Adv. in Appl. Sci. Vol. 8, No. 1, March 2019: 73 – 82

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variables which are selected for the system. The output variable of the FLC algorithm is the change in the duty cycle (ΔD) command that will be added to the duty cycle at the pervious step. There are different methods to select the input variables of the FLC, [23]. Simulink block diagram of this method is shown in Figure 13. The figure shows that the input signal of the FLC block is generated based on the IC method (applying (4)) and the output of the FLC block is the change in the duty cycle which is added to the duty cycle at the previous step. The PV module power response using this algorithm is shown in Figure 14. It can be seen from this figure that the power fluctuations in the steady state is nearly zero at all irradiance levels. The tracking time is (26 msec) at the irradiance level 0.8 p.u., at start up. The ttrack in the case of suddenly change from 0.8 p.u. to 0.6 p.u. and from 0.6 p.u. to 1 p.u. are (15 msec) and (20 msec) respectively. The ttrack is (65 msec) when the irradiance level is suddenly changed from 1 p.u. to 0.4 p.u.

ZOH1 VPV

Gain1

IPV

ZOH3

𝜟𝑫

ZOH2

FLC

Saturation

Gain2

D Memory

Unit delay 1

Unit delay 2

Figure 13. Simulink block diagram of the FLC based on the IC method

250 200

G3=1 (PPV,max=200.15W)

G4=0.4 (PPV,max=79.61W)

G1=0.8 (PPV,max=160.49 W)

79.7

p P V (W )

G2=0.6 (PPV,max=120.275W)

79.6

150

200.16

100

200.15

79.5 0.7 0.71 0.72

50 0 0

160.55

120.3

160.5

120.2

160.45 0.1

0.105

0.1

0.11

200.14 0.5

0.51 0.52

0.3 0.305 0.31

0.2

0.3

0.4

t (sec)

0.5

0.6

0.7

Figure 14. PV module power response of the IC method based on FLC Comparing these results with the results in Ref. [23], it was found that the PV module is operated at the MPP, Figure 14, without any power fluctuations at all irradiance levels at steady-state due to the output of the FLC block is the change in the duty cycle comparing with Ref. [23] which has some power fluctuations at steady-state as it takes the output variable of the FLC block as the desired duty cycle. For example, considering that the irradiance level is suddenly changed from 1 p.u. to 0.4 p.u. at the same cell temperature, 25 oC, it was found that the ΔPPV,max is (0.08 W) and the ttrack is (114 msec) in Ref. [23] compared with (0 W) and (65 msec) as shown in the figure. 4.

COMPARISON ETWEEN THE TECHNIQUES In this section, comparison between the two proposed algorithms are presented. The comparison criteria are considered to be: the percentage tracking factor, the ripple voltage in the steady-state, the dynamic response, Comparative study of the most efficient maximumpower point tracking techniques … (I. M. Abdel-Qawee)

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and the complexity of the implementation. To investigate these comparison criteria and selecting the best technique, the PV system will be tested under normal operating cell temperature (NOCT) and under the worst atmospheric conditions. 4.1. Comparison under normal Ooperating cell temperature The performance of the PV system using the proposed algorithms under normal operating cell temperature, G=0.8 and T=47 oC, [17], is investigated. The PV module power response using both algorithms is shown in Figure 15. It can be seen from the figure that the maximum extracted power using both algorithms is nearly (142 W) which is equal to the maximum PV module power under this operating condition, [17]. Therefore, the percentage tracking factor for both algorithms is 100%. The tracking time using IC method based on FLC algorithm is (30 msec), while the tracking time using the IC method based on the optimized PIcontroller gains algorithm is (40 msec). The maximum peak-to-peak power fluctuations (ΔPPV,max ) in both algorithms is nearly zero. The ripple voltage in the steady-state of the PV module is around 0.005 V using both algorithms as shown in Figure 16. However, the maximum overshoot voltage obtained using the IC method based on optimized PI-controller gains algorithm is (32 V) which is almost equal to that obtained using the IC method based on FLC algorithm, which is (30 V). 4.2. Comparison under worst atmospheric conditions To investigate the robustness of the proposed MPPT control techniques with the standalone PV system under the worst atmospheric conditions, the irradiance level is suddenly changed from the highest level, 1 p.u., to the lowest level, 0.2 p.u at T=25 oC, and the cell temperature is suddenly changed from 25 oC to 75 oC at G=1 p.u. [17]. The effect of changing the irradiance level and the PV cell temperature, under the worst atmospheric conditions, on the PV module power response are shown in Figures 17 and 18 respectively. It can be shown from these figures that the PV module power is nearly constant around the MPP without any power fluctuations using both algorithms at steady-state. Therefore, the percentage tracking factor in both cases is 100%. However, at start up, where the irradiance level is at 1 p.u. and the cell temperature is at 25 oC, the tracking time using the IC method based on FLC algorithm is (21 msec) which is less than that using the other algorithm which is (28 msec). When the irradiance level is suddenly changed from the highest level to the lowest level, Figure 17, the tracking time using the IC method based on optimized PI-controller gains algorithm is (90 msec) which is less than that using the IC method based on FLC algorithm which is (155 msec). Also, when the cell temperature is suddenly changed from 25 oC to 75 oC, Figure 18, the tracking time using IC method based on optimized PIcontroller gains algorithm is (13 msec) which is less than that using the IC method based on FLC algorithm, which is (22 msec). The PV module voltage response under the largest change in the irradiance level and the cell temperature are shown in Figure 19 and Figure 20 respectively. The figures show that the ripple voltage in the steady-state under the standard test condition (STC) and at (T=75 oC, G=1 p.u.) is (0.005 V) using both algorithms. However, the maximum ripple voltage in the steady-state when the irradiance level is change from 1 p.u. to 0.2 p.u is less than (1 V) using the IC method based on FLC algorithm, while it is around (2.5 V) using the IC method based on optimized PI-controller gains algorithm. 30

150

142.025 142.0248

(V)

100

0.22

23.48 23.47

23.46

23.45 0.21

0.21 0.215

(W)

142.0246

50 IC method based on FLC IC method based on optimized PI-controller gain P =142W [27]

PV,max

0 0

0.05

0.1

0.15 t (sec)

0.2

0.25

0.3

Figure 15. PV module power response under NOCT

0 0

0.212

IC method based on FLC IC method based on optimized PI-controller gain

0.05

0.1

0.15

t (sec)

0.2

0.25

0.3

Figure 16. PV module voltage response under NOCT

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250 IC method based on FLC IC method based on optimized PI-controller gain

IC method based on FLC IC method based on optimized PI-controller gain 200

200

T=25 oC

100

150

(W)

G=1 p.u.

(W)

150

G=0.2 p.u.

100

T=75 oC 50

0 0

0.05

0.1

0.15

0.2

t (sec)

0.25

0.3

0.35

0 0

0.4

Figure 17. PV module power response under the worst atmospheric condition in the irradiance level change (at T=25 oC)

(V) PV

G=0.2 p.u.

26.37

(V) PV

0.25

0.3

0.35

0.4

G=1 p.u.

20 15

26.36 26.35

0.15

0.16 24 0.46

0.1

0.2

t (sec)

0.3

T=25 oC 26.37 26.36

5 0 0

0.2

t (sec)

IC method based on FLC IC method based on optimized PI-controller gain

0.15

35 IC method based on FLC IC method based on optimized PI-controller gain

0.1

Figure 18. PV module power response under the worst atmospheric condition in the PV cell temperature change (at G=1 p.u.)

0.05

0.4

26.35

T=75 oC 0.15

20.24

0.16

0.48

0.5

Figure 19. PV module voltage response under the worst atmospheric condition in the irradiance level change (at T=25 oC)

0 0

20.26

20.22

0.1

0.2

t (sec)

0.3

0.46

0.4

0.48

0.5

Figure 20. PV module voltage response under the worst atmospheric condition in the PV cell temperature change (at G=1 p.u.)

CONCLUSION In this paper, the tracking of MPP in a standalone PV system has been discussed. Two MPPT controllers have been modeled and simulated using Matlab/Simulink at different atmospheric conditions. The simulation results are verified with the experimental results which in the PV module manufacture datasheet [17]. The comparison criteria that taken into consideration are: the percentage tracking factor, the tracking time, the ripple voltage in the steady-state, the dynamic response, and the complexity of the controller. It can be concluded that both methods are almost 100% of tracking factor and the maximum peak to peak fluctuation of the PV module power is nearly zero. The IC method based on FLC algorithm has a better steady-state performance at the NOCT and at the worst atmospheric conditions compared to IC method based on optimized PI-controller gains algorithm. However, the dynamic performance of the PV system using the IC method based on optimized PI-controller gains algorithm is better than that using the IC method based on FLC algorithm. The PV system using the IC method based on FLC does not require offline computational processes but it depends on the skill of the designer when designing the membership functions and selecting the appropriate fuzzy rules. However, The PV system using the IC method based on optimized PI-control gains requires offline computational processes to optimize the integral controller gain. REFERENCES [1]

[2]

S. N. Sakib, S. P. Mouri, Abu Kowsary, M. Rahamany, and M. Shamim Kaiser, “Theoretical Efficiency of AlAs/GaAs/GaAs0.91Bi0.085 Based New Multijunction Solar Cell and Effects of Solar Radiation and Sun Concentration on it,” International Conference on Microelectronics, Computing and Communications (MicroCom), 23-25 Jan. 2016, pp. 1-6. M. A. Gomes de Brito, L. Galotto, Jr., L. P. Sampaio, G. de Azevedo e Melo, and C. A. Canesin, “Evaluation of the Main MPPT Techniques for Photovoltaic Applications,” IEEE Transactions on Industrial Electronics, vol. 60, no. 3, pp.1156-1167, March 2013.

Comparative study of the most efficient maximumpower point tracking techniques … (I. M. Abdel-Qawee)

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[12] [13] [14] [15] [16] [17] [18] [19] [20] [21] [22] [23]

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Mei Shan Ngan and Chee Wei Tan, “A Study of Maximum Power Point Tracking Algorithms for Stand-alone Photovoltaic Systems,” Applied Power Electronics Colloquium (IAPEC), Johor Bahru, Malaysia, 8-19 April 2011, pp. 22-27. M. S. Ebrahim, A. M. Atallah, and A. S. Emarah, “Maximum Power Point Tracking for PV System using Fuzzy Logic Control,” Fut International Conference on New Energy and Environmental Engineering (ICNEEE), 11-14 April 2016, pp. 157-162. XIE Wei and HUI Jing, “MPPT for PV System Based on A Novel Fuzzy Control Strategy,” International Conference on Digital Manufacturing & Automation, 18-20 December 2010, pp. 960-963. Trishan Esram and Patrick L. Chapman, “Comparison of Photovoltaic Array Maximum Power Point Tracking Techniques,” IEEE Transactions on Energy Conversion, Vol. 22, No. 2, pp. 439-449, June 2007. Jadhav Pankaj Shankarrao, “FPGA Implementation of Maximum Power Point Tracking Algorithm for PV System,” Master Thesis, National Institute of Technology Rourkela, May 2013. Ahmed K. Abdelsalam, Ahmed M. Massoud, Shehab Ahmed, and Prasad N. Enjeti, “High-Performance Adaptive Perturb and Observe MPPT Technique for Photovoltaic-Based Microgrids,” IEEE Transactions on Power Electronics, Vol. 26, No. 4, pp. 1010-1021, April 2011. Moein Jazayeri, Sener Uysal, and Kian Jazayeri, “Evaluation of Maximum Power Point Tracking Techniques in PV Systems Using MATLAB/Simulink,” Sixth Annual IEEE Green Technologies Conference, 3-4 April 2014, pp. 54-60. Gaber El Saady, El Noby Ahmed, and Samy Faddel, “Analysis and Control of Standalone PV/Battery Generation System,” 16th International Middle- East Power Systems Conference -MEPCON'2014, Ain Shams University, Cairo, Egypt, December 23-25, 2014. Jay Robert B. del Rosario and Elmer Dadios, “Development of a Fuzzy Logic- Based PV Solar Tracking System Simulated Using QT Fuzzy Engine,” 7th IEEE International Conference on Humanoid, Nanotechnology, Information Technology Communication and Control, Environment and Management (HNICEM), Philippines, 12-16 November 2014, pp. 1-8. Subiyanto, Azah Mohamed, and M. A. Hannan, “Hardware Implementation of Fuzzy Logic Based Maximum Power Point Tracking Controller for PV Systems,” The 4th International Power Engineering and Optimization Conf. (PEOCO2010), Shah Alam, Selangor, Malaysia, 23-24 June 2010, pp. 435-439. Marcelo Gradella Villalva, Jonas Rafael Gazoli, and Ernesto Ruppert Filho, “Comprehensive Approach to Modeling and Simulation of Photovoltaic Arrays,” IEEE Transactions on Power Electronics, Vol. 24, No. 5, pp. 1198-1208, May 2009. M. Petkov, D. Markova, and St. Platikanov, “Modelling of Electrical Characteristics of Photovoltaic Power Supply Sources,” Contemporary Materials (Renewable energy sources), pp. 171-177, 2011. K.H. Hussein, I. Muta, T. Hoshino, and M. Osakada, “Maximum Photovoltaic Power Tracking: An Algorithm for Rapidly Changing Atmospheric Conditions,” IEE Proceedings- Generation, Transmission and Distribution, Vol. 142, no. 1, pp. 59-64, Jan 1995. Heba N. Mohamed and Soliman A. Mahmoud, “Temperature Dependence in Modeling Photovoltaic Arrays,” 20th International Conference on Electronics, Circuits, and Systems (ICECS), 8-11 December 2013, pp. 747-750. KC200GT High Efficiency Multicrystal Photovoltaic Module Datasheet Kyocera. [Online]. Available: http://www.kyocera.com.sg/products/solar/pdf/kc200gt.pdf. Mona Marodkar, Sarala Adhau, Manisha Sabley, and Praful Adhau, “Design and Simulation of DC-DC Converters for Photovoltaic System based on MATLAB,” 2015 International Conference on Industrial Instrumentation and Control (ICIC) College of Engineering Pune, India, May 28-30, 2015, pp. 1478-1483. S. Kolsi1, H. Samet, and M. Ben Amar, “Design and Analysis of DC-DC Converters Connected to a Photovoltaic Generator and Controlled by MPPT for Optimal Energy Transfer throughout a Clear Day,” Journal of Power and Energy Engineering (JPEE), Vol. 2, No. 1, pp. 27-34, January 2014. Mahmoud M. Hussein, “Control of a Stand-Alone Photovoltaic Energy System,” Fut International Conference on New Energy and Environmental Engineering (ICNEEE), 11-14 April 2016, pp. 191-197. Ganji Sai Kumar, G. Ramudu, and D. Vijay Arun, “Analysis and Implementation of Bidirectional DC to DC Converter by Using Fuzzy Logic Controller,” The International Journal of Engineering and Science (IJES), Vol. 3, no. 6, pp. 22-39, 2014. B. Stalin and T. S. Sivakumaran, “Performance Evaluation of Bidirectional Soft Switching Converter with Implementation of PI and Fuzzy Logic Controllers,” International Journal of Advanced Engineering Technology,” Vol. 8, no. 3, July-September 2016, pp. 42-53. Jaw-Kuen Shiau, Yu-Chen Wei, and Bo-Chih Chen, “A Study on the Fuzzy-Logic-Based Solar Power MPPT Algorithms Using Different Fuzzy Input Variables,” Algorithms, Vol. 8, pp. 100-127, 2015.

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International Journal of Advances in Applied Sciences (IJAAS) Vol. 8, No. 1, March 2019, pp. 83~94 ISSN: 2252-8814, DOI: 10.11591/ijaas.v8.i1.pp83-94

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Effect of chemical reaction on MHD flow with heat and mass transfer past a vertical porousplate in the presence of viscous dissipation S. Kar1, N. Senapati2, B. K. Swain3 1,2Department 3Indira

of Mathematics, Ravenshaw University, India Gandhi Institute of Technology, India

Article Info

ABSTRACT

Article history:

An attempt is made to study an unsteady MHD free convective flow with heat and mass transfer past a semi-infinite vertical porous plate immersed in a porous medium. Presence of viscous dissipation and chemical reaction are taken into account. It is assumed that the plate is moved with uniform velocity in the direction of fluid flow. Viscous dissipation term leads nonlinearity in the governing equations. Applying perturbation technique, the solutions for velocity, temperature and concentration are obtained. The effect of various parameters such as Rc, Gr, Gc, Sc etc. On velocity, temperature and concentration are shown through graphs.

Received Dec 9, 2018 Revised Feb 7, 2019 Accepted Feb 21, 2019 Keywords: Chemical reaction Eckert number MHD Soret number Viscous dissipation

Corresponding Author: Satyabrat Kar, Department of Mathematics, Ravenshaw University, 753003 Cuttack, Odisha, India. Email: satyabratkr@gmail.com

NOMENCLATURE 𝐶∗ Species concentration (𝑘𝑔 𝑚 ) 𝐶 Specific heat at constant pressure ( 𝐽𝑘𝑔 𝐾) 𝐶∗ Species concentration in the free stream (𝑘𝑔 𝑚 ) 𝐶∗ Species concentration at the surface (𝑘𝑔 𝑚 ) 𝐷 Chemical molecular diffusivity (𝑚 𝑠 ) 𝑔 Acceleration due to gravity (𝑚𝑠 ) 𝐺𝑟 Thermal Grashof number Mass Grashof number 𝐺𝑐 Permeability parameter 𝐾 M Hartmann number 𝑁𝑢 Nusselt number Prandtl number 𝑃𝑟 Radiative heat flux 𝑞 𝑆ℎ Sherwood number Schmidt number 𝑆𝑐 Temperature (K) 𝑇∗ 𝑇∗ Fluid temperature at the surface (K) Fluid temperature in the free stream (K) 𝑇∗ Journal homepage: http://iaescore.com/online/index.php/IJAAS

84 u

 Dimensionless velocity component (𝑚𝑠

ISSN: 2252-8814 )

Greek symbols 𝛽 Coefficient of volume expansion for heat transfer (𝐾 ) 𝛽 Coefficient of volume expansion for mass transfer (𝐾 ) 𝜃 Dimensionless fluid temperature (K) 𝑘 Thermal conductivity (𝑊𝑚 𝐾 ) 𝜈 Kinematic viscosity (𝑚 𝑠 ) 𝜌 Density (𝑘𝑔𝑚 ) Electrical conductivity 𝜎 𝐶 Dimensionless species concentration (𝑘𝑔𝑚 ) 𝜏 Shearing stress (𝑁𝑚 ) Subscripts 𝑤 Conditions on the wall ∞ Free stream condition 1.

INTRODUCTION In recent years, the subject of magnetohydrodynamics has attracted the attention of many authors in view of its application to the problems in geophysics, astrophysics and many engineering and industrial applications, like cooling of metal in nuclear reactors and magnetic control of iron flow in steel industry, etc. [1-4]. The investigation of unsteady natural convective flow of viscous incompressible fluid past vertical bodies has also several engineering and technological applications. Gupta [5] studied transient free convection of an electrically conducting fluid from a vertical plate in the presence of magnetic field. Free convection effects on flow past an exponentially accelerated vertical plate was studied by Singh and Kumar [6]. Jha et al. [7] analyzed mass transfer effects on exponentially accelerated infinite vertical plate with constant heat flux and uniform mass diffusion. Recently Ahmmed et.al. [8] studied the unsteady MHD free convection and mass transfer flow past a vertical porous plate. Chemical reactions happen at a characteristic reaction rate at a given temperature and chemical concentration. Different chemical reactions ae used in combinations during chemical synthesis in order to find a desired product. In biochemistry, a consecutive series of chemical reactions form metabolic path ways. The order of chemical reaction is defined as the sum of the powers of the concentration of the reactants in the rate equation of that particular chemical reaction. A first order reaction is the one in which the rate is proportional to concentration of a single reactant. In the present paper, first order reaction is taken into acount. Moreover, coupled heat and mass transfer problems in the presence of chemical reaction are of importance in many processes such as drying, distribution of temperature and moisture over agricultural fields and groves of fruit trees, damage of crops due to freezing, evaporation at the surface of a water body and energy transfer in a wet cooling tower, and flow in a desert cooler. Chamkha and Aly [9] obtained numerical solution of steady boundary-layer stagnation point flow of a polar fluid toward a stretching surface embedded in porous media in the presence of the effects of Soret and Dufour numbers and first-order homogeneous chemical reaction. Aurangzaib et al. [10] investigated the effect of thermal stratification and chemical reaction on free convection boundary layer MHD flow with heat and mass transfer of an electrically conducting fluid over time dependent stretching sheet. Abd El-Aziz [11] obtained numerical results to study the effect of time dependent chemical reaction on stagnation point flow and heat transfer of nanofluid over a stretching sheet. Pal and Mandal [12, 13] investigated the mixed convection boundary layer flow of nanofluids at a stagnationpoint over a permeable stretching/shrinking sheet subject to thermal radiation, heat source/sink, viscous dissipation and chemical reaction using numerical method. In the physical realm, many irriversible processes are present. Some exmples are heat flow through a thermal resistance, fluid flow through a flow resistance, chemical reactions etc.. The irreversible process by means of which the work done by a fluid on adjacent layers due to the action of shear forces is transformed into heat is defined as viscous dissipation. It can be seen in many places such as in hydraulic engineering, waves or oscillations etc. viscous dissipation has different applications in various industries. Significant temperature rises are observed in polymer processing flows such as injection molding or extrusion at high rates. Aerodynamic heating in the thin boundary layer around high speed aircraft raises the temperature of the skin. A number of authors have considered viscous heating effects on Newtonian flows. Mahajan et al. [14] reported the influence of viscous heating dissipation effects in natural convective flows, showing that the heat transfer rates are reduced by an increase in the dissipation parameter. Isreal Cookey et al. [15] investigated the influence of viscous dissipation and radiation on unsteady MHD free convection flow past an infinite Int. J. of Adv. in Appl. Sci. Vol. 8, No. 1, March 2019: 83 – 94

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heated vertical plate in a porous medium with time dependent suction. Zueco [16] used network simulation method (NSM) to study the effects of viscous dissipation and radiation on unsteady MHD free convection flow past a vertical porous plate. Suneetha et al. [17] have analyzed the thermal radiation effects on hydromagnetic free convection flow past an impulsively started vertical plate with variable surface temperature and concentration by taking into account of the heat due to viscous dissipation. Recently Suneetha et al. [18] studied the effects of thermal radiation on the natural conductive heat and mass transfer of a viscous incompressible gray absorbing-emitting fluid flowing past an impulsively started moving vertical plate with viscous dissipation. Very recently, Hiteesh [19] studied the boundary layer steady flow and heat transfer of a viscous incompressible fluid due to a stretching plate with viscous dissipation effect in the presence of a transverse magnetic field. Due to above significant role, it is the purpose of this paper to examine the effect of viscous dissipation and chemical reaction on the MHD flow of a viscous incompressible fluid past a semi-infinite vertical plate. This flow problem was previously studied by Ahmmed et.al [8] in the absence of these two parameters. 2.

MATHEMATICHAL FORMULATION A two dimensional unsteady flow of a laminar and incompressible fluid past a semi-in finite vertical moving plate embedded in a uniform porous medium is considered. Fluid is assumed to be viscous and electrically conducting. A uniform transverse magnetic field 𝐵0 is applied in the presence of pressure gradient. Thermal diffusion and thermal radiation are also considered to be present. In the given system 𝑥’ axis is taken along the plate and 𝑦’ axis normal to it. No voltage is applied to the system and induced magnetic field is negligible. Since we assume a semi-infinite plate surface, the flow variables are the functions of 𝑦’and 𝑡’ only. Then under the above assumption the unsteady flow with usual Boussinesq's approximation is governed by the following equations. (1)

=0 𝜌

+𝑣

+𝜇

− 𝜌𝑔 −

(2)

𝑢 − 𝜎𝛽 𝑢

(3)

(4) Boundary conditions for the velocity, temperature and concentration fields are given as follows at y’= 0 at y’= ∞ Now, v = −v 1+∈ Ae

(5) (6) (7)

in free stream, we have

(8) Eliminating

using (2) and (8), we obtain

Effect of chemical reaction on MHD flow with heat and mass transfer past a vertical … (S. Kar)

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ISSN: 2252-8814

(9) By using the equation state, we obtain (ρ∞ − ρ) = β(T − T∞ ) + β (C − C∞ )

(10)

The (9) becomes

(11) The radioactive heat flux term by using the Roseland approximation is given by 𝑞 =−

(12)

T may be expressed as a linear combination of the temperature. This is accomplished by expanding in a Tayler series about T∞ and neglecting higher order terms. T

= 4T T∞ − 3T∞

(13)

by using (12) and (13)into (3) becomes

(14) To get the solution of (1) to (4) with boundary conditions (5) and (6), the following non dimensional parameters are used.

Using the dimensionless parameters in (11), (14), and (4), we get

Int. J. of Adv. in Appl. Sci. Vol. 8, No. 1, March 2019: 83 – 94

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𝑢 = 𝑈 , 𝜃 = 1 + 𝜖𝑒 , 𝜃 → 0, 𝜑 = 1 + 𝜖𝑒 𝑎𝑡 𝑦 = 0 𝑢 → 𝑈 = 1 + 𝜖𝑒 , 𝜃 → 0, 𝜑 → 0 𝑎𝑠 𝑦 = ∞

(19)

Perturbation method is used to solve (16), (17) and (18). The following forms are considered.

From (16), (17), and (18), we obtained (20) (21) (22) (23)

Corresponding boundary conditions are

(26) Second Perturbation technique is used to solve (20), (21), (22), (23), (24), and (25). The following forms are considered

From (20), (21),(22),(23),(24) and (25), we obtained

(33) Effect of chemical reaction on MHD flow with heat and mass transfer past a vertical … (S. Kar)

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Solving (31) and (33), we obtained

Solving (35) and (37), we obtained

Solving (27) and (32), we obtained

Solving (36) and (28), we obtained

Solving (29) and (34), we obtained

Int. J. of Adv. in Appl. Sci. Vol. 8, No. 1, March 2019: 83 – 94

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Int. J. of Adv. in Appl. Sci.

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Solving (30) and (38), we obtained

Effect of chemical reaction on MHD flow with heat and mass transfer past a vertical … (S. Kar)

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Int. J. of Adv. in Appl. Sci. Vol. 8, No. 1, March 2019: 83 – 94

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RESULTS AND DISCUSSION To discuss the physical significance of various parameters involved in the results, the numerical calculations have been carried out. The effects of the various parameters entering in the governing equations on the velocity, temperature and concentration are shown through graphs. Figure 1 shows velocity profiles are depicted for various values of solutal Grashoff number Gc which is defined by the ratio of the species buoyancy force to viscous hydrodynamic force. It is noticed that when buoyancy force dominates viscous hydrodynamic force, velocity increases. Velocity profiles increases to a peak value near the plate then decreases. It can be said that velocity profiles converge pointwise. Figure 2 shows the influence of chemical reaction parameter on the velocity field. Increase in Rc results increament in velocity. Figure 3 shows velocity profiles are depicted against y for various values of Prandtl number Pr. It shows oscillatory effect. At the vicinity of the plate velocity increases with incresing values of Pr. But at certain values of y profiles intersect then leads opposite effect. In Figure 4, it is noticed that increasing values of Gr enhance the velocity. Figure 5 shows the oscillatory values of velocity. When Q increases velocity increases at first then decreases. Figure 6 shows concentration profiles are depicted for different values of Rc. It can be easily said that concentration decreases with increase in Rc. Figure 7 and Figure 8 show concentration is influenced equally by Sc and So respectively. Concentration increases with Effect of chemical reaction on MHD flow with heat and mass transfer past a vertical … (S. Kar)

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increasing values of these parameters near the plate. After certain value of y, concentration decreases when these parameters increase. Figure 9 shows temperature profiles are drawn against y for different values of Pr. It shows that values of Pr are directly proportional to temperature. In Figure 10 and Figure 11, it is noticed that temperature decreases with increasing values of R and Q.

Figure 1. Velocity profiles for different values of Gc

Figure 2. Velocity profiles for different values of Rc

Figure 3. Velocity profiles for different values of Pr

Figure 4. Velocity profiles for different values of Gr

Figure 5. Velocity profiles for different values of Q

Figure 6. Concentration profiles for different values of Rc

Int. J. of Adv. in Appl. Sci. Vol. 8, No. 1, March 2019: 83 – 94

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Figure 7. Concentration profiles for different values of Sc

Figure 8. Concentration profiles for different values of So

Figure 9. Temperature profiles for different valueses of Pr

Figure 10. Temperature profiles for different valueses of R

Figure 11. Temperature profiles for different valueses of Q

Effect of chemical reaction on MHD flow with heat and mass transfer past a vertical … (S. Kar)

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CONCLUSION Here unsteady MHD free convection flow with viscous dissipation and chemical reaction was studied. Due to addition of viscous dissipation, we find a non linearity in our energy equation. So we use Perturbation technique two times to solve the whole system. The different results are discussed through graphs. To precise the paper, the index was not included here. Finally some conclusions are drawn as follows: The parameters Gc, Gr and Rc enhance the velocity; Concentration decreases by the increament of chemical reaction parameter; The enhancing values of parameters R and Q result a decrease in temperature. But Pr shows reverse effect on temperature; In case of velocity Pr and Q show the oscillatory effect.; Similarly Sc and So shows oscillatory effect in case of concentration. REFERENCES [1] R. S. Sapieszko and E. Matijevi, “Preparation of well-defined colloidal particles by thermal decomposition of metal chelates. I. Iron oxides,” J. Collo. Interf. 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International Journal of Advances in Applied Sciences (IJAAS) Volume 8, issue 1, Mar. 2019