Full Paper Proc. of Int. Conf. on Advances in Industrial and Production Engineering 2012

Process Parameter Optimisation of Grate (Sugar Mill Boiler) through Failure Mode and Effect Analysis and Taguchi Method A.Mariajayaprakash1 and T.Senthilvelan2 1

Department of Mechanical Engineering, Rajiv Gandhi College of Engineering and Technology, Puducherry, India, email: jayaprakashrgcet@gmail.com 2 Department of Mechanical Engineering, Pondicherry Engineering College, Puducherry, India, email: tsenthilvelan@hotmail.com

Abstract—Boiler is one of the essential components in sugar industries. In order to overcome the shortage of electricity, cogeneration option has been adopted in many of the sugar mills. Unexpected failures of boiler used in cogeneration process cause loss of production. In this research work, the failures are occurred in the screw conveyor, drum feeder and grate of the boiler during the cogeneration process. This paper describes the failures of grate and gives solution to rectify these failures. Three important statistical tools, namely, cause and effect diagram, Failure Mode and Effect Analysis (FMEA) and Taguchi method are employed to improve the quality of the grate. The estimation of both the optimum performance characteristics of grate and the optimum levels of parameters have been identified in this research paper and the results have been verified by practical experimentation. The confirmation experiments reveal that the quality of the grate has been enhanced by decreasing the failures when the input parameters have been set in the optimum range.

drum feeder and grate of the boiler during the cogeneration process.This paper describes the failure of grate and gives solution to overcome these failures. In order to overcome the failures and improve the quality of the grate, the following statistical tools are employed. First, Ishikawa or cause and effect diagram is used for sort out all the possible root causes of the failure. Failure Mode and Effect Analysis is a second tool helped to find out the most significant parameters to lead the failure of the boiler. Finally, Taguchi method is used to optimize the process parameters of the grate. II. BOILER The boiler used in this research work is vertical, water tube, top supported, high pressure boiler. The working pressure, generating capacity and steam temperature of this boiler are 66 kg/cm2, 75 tonnes/hour, and 4850C respectively. It is designed to burn bagasse (B), palm boom (P), cane trash (C) or a mixture thereof.

Key words—Sugar mill boiler, Ishikawa diagram, FMEA, Taguchi’s method, ANOVA.

A. Boiler Configuration The main components of above mentioned boiler are: fuel feeding system, furnace, super heater, attemperator, economizer, air preheater, FD fan, ID fan, dust collector and boiler feed water pumps. Out of these, some important components are described below:

I. INTRODUCTION India is the largest sugar producing country in the world. The sugar industry plays an important role in Indian economy. India faces a peak electric generating shortage of over 20% and an energy shortage of 12%. The growth of Indian economy is affected by shortage of power, which is one of the significant constraints. The energy shortage faced by the country can be reduced by adopting cogeneration in the sugar mills. The simultaneous generation of steam and power commonly referred to as co-generation [1]. Boiler is one of the essential components in the cogeneration process. The fuel that is used in the boiler is bagasse, which is a by-product of the sugar extraction process. In cogeneration process, boiler failures are one of the important reasons for unexpected shutdown of plant [2]. This shutdown leads to great loss of production. The majority of the articles reported that the boiler failures were occurred in the vertical furnace wall tubes, superheater tubes, cold and hot re-heatertubes, economizer and condensate preheater [3,4]. This research work is carried out one of the leading sugar mill located in Tamil Nadu. In this sugar mill, the failures are occurred in the screw conveyor, © 2012 AMAE DOI: 02.AIPE.2012.2.507

B. Grate The “Travagrate”continuous ash discharge spreader stoker is an overfeed type stoker complete with hydraulic grate drive and air swept fuel distributers. Fuel is burned in suspension as well as on the forward-travelling grate surface. It is designed for firing steam generators with capacities up to 500000 of steam per hour. The ash is continuously discharged over the front end of the grate into the ash handling system. The speed of the grate is controlled by the flow knob located at the front of the hydraulic drive unit. C. Storage Bunker (Silo) Storage bunker is also named as Silo. The fuel is fed to the vertical column of the storage bunker from the belt conveyor. The type of loading is vertical.

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Full Paper Proc. of Int. Conf. on Advances in Industrial and Production Engineering 2012 D. Drum Feeder The drum feeder is designed to withstand the vertical load due to the fuel column in the storage bunker. The drum is located eccentrically in the casing of the feeder .To avoid entrapment of fine fuel dust and fibers between the drum and casing, shroud plates are provided on top of the drum feeder at the fuel entry location from the storage bunker. Otherwise, that will cause jamming and over loading during inspection. In the drum, spikes are welded and arranged in an inclined axis to the centre line of rotation of the feeder. This arrangement ensures that efficient extraction of fuel. The drum feeder extracts fuel from the storage bunker and the quantity extracted is proportional to the speed of rotation of the drum. The drum feeder is driven by variable speed drive which is connected to combustion control of the boiler. The drum feeder capacity is 9500 kg/hr.

Figure 1. Cause and effect diagram

In FMEA technique, the risk priority order of failure modes are determined through the risk priority number (RPN ), which is the product of the occurrence (O), severity (S) and detection (D) of a failure. It means that the failure modes are identified and ranked with help of RPN [6]. The three risk factors are evaluated using 10 point scale. It is rated from 1(best) to 10 (worst) on the basis of degree. The failure modes with higher RPNs are assumed to be more important and will be given higher priorities for correction. In this paper, furnace temperature, oil pressure, hydraulic drive speed and fuel type are having higher RPN values. Hence these four process parameters are considered as most significant process parameters. The levels and ranges of the selected process parameters are chosen on the basis of light of literature, preliminary experiments and prior experience. The process parameters, ranges and their levels are represented in Table I.

E. Screw Conveyor The trough of the screw conveyor is of carbon steel and the bottom portion of the trough is lined with stainless steel plates to ensure smooth flow of fuel to the distributed chute. The lining is plug welded on to casing. Quick opening access doors on the casing are provided to enable access for cleaning the trough. The carbon steel screw has a toothed profile which gives positive conveying of the fibrous material. The flights of the screw are terminated at the outlet end to make sure that there is no packing of bagasse at the discharge end. The shaft ends screw conveyor is located in antifriction bearings at both ends. The shaft of the screw conveyor is directly driven by constant speed geared motor. The geared motor is coupled to the screw conveyor shaft through a tyre coupling. The drive is located in a common base frame. The screw conveyor capacity is 12000 kg/hr.

TABLE I. PROCESS PARAMETERS WITH

THEIR

RANGES

AND

LEVELS

III. SELECTION OF PROCESS PARAMETERS OF GRATE In order to identify the process parameters to cause the failure of grate during the process, an Ishikawa diagram (cause and effect diagram) has been constructed as shown in Fig. 1. Cause and effect diagram provides all the relevant sources and it is very useful to determine the root causes of a given problem.[5]. The identified process parameters are i)furnace- fuel heap, temperature, air flow ii) fuel- type, moisture, foreign material, size iii) hydraulic drive- speed, oil pressure, oil seal damaged, oil level, improper lubrication iv)operator- improper maintenance, mal function and fatigue. After constructing Ishikawa diagram, it is essential to perform Failure Mode Effect Analysis (FMEA). Failure Mode and Effect Analysis is a very powerful and effective quality tool to identify the potential failure modes, and their causes and effects. Proper identification of failure may lead to solutions that increase the product quality and productivity. In this research work, FMEA technique is applied to find out the most significant parameters causing the failure of grate during the cogeneration process. FMEA is usually carried out by a team of experienced and skilled Engineers and expertâ€™s knowledge. ÂŠ 2012 AMAE DOI: 02.AIPE.2012.2. 507

IV. TAGUCHI METHOD Taguchi method is an efficient and systematic approach to reduce the experiment trials as compared to traditional experimental design methods. In this method, high quality is achieved by optimizing product or process parameters without increasing the cost. Taguchi method uses a special design of orthogonal arrays (OA) to study the entire parameter space with small number of experiments only.The appropriate OA is selected on the basis of total degrees of freedom (DOF) 2

Full Paper Proc. of Int. Conf. on Advances in Industrial and Production Engineering 2012 required.The degree of freedom is defined as the number of comparisons among process parameters needed to optimize the parameters .The DOF is determined by using number of factors, number of levels of each factor and number of interactions. In this study, the interaction effect between the process parameters is neglected. The total DOF required for four factors and three levels is 8 (= 4 x (3-1)). As per Taguchi’s method, the total DOF of selected OA must be greater than or at least equal o the total DOF required for the experiment [7]. Hence, in this study, L9 orthogonal array is selected. The selected orthogonal array having four columns and nine experiments runs is shown in Table II. The analyses were carried out using MINITAB13 statistical software. Another important tool used in Taguchi method is signal to noise ratio (S/N ratio). Signal to noise ratio (S/N ratio) is employed to analysis the quality characteristics of the product or process parameters. Regardless of the category of the quality characteristic, process parameter settings with the highest S/N ratio always yield the optimum quality with minimum variance. Usually, there are three categories of the quality characteristic in the analysis of the S/N ratio, i.e. the lower-the-better, the largerthe-better, and the more-nominal-the-better [5]. In this research work, smaller is better S/N ratio is applied to minimize the failures of grate occurred during the cogeneration process. smaller is better

TAVLE II. L9 ORTHOGONAL ARRAY

In order to observe the noise sources, the plant was being run three times for the same set of parameters given in Table III.The number of failures occurred during the process were noted and tabulated in Table IV. The percentage of failures of the grate has been calculated by the formula given below: No. of grate failures Percentage of grate failure = Total no. of failures TABLE III. EXPERIMENTAL L9 ARRAY

η = S/N ratio, yi = value of quality characteristic at ith setting, n= total number of trial runs at ith setting. V. METHODOLOGY This research work was carried out in one of the leading sugar mill located in Tamil Nadu. Cogeneration is adopted in the sugar mill. Three shifts were followed per day in the above mentioned sugar mill. Each shift carried 8 hours. During the cogeneration process, it was found that the plant was affected by failure of grate, which is one of the components of boiler. This paper describes that the failure of the grate and the reason for the failures are getting reduced. The significant process parameters affecting the grate were recorded in Table 1. The process parameters at different levels are assigned in the selected orthogonal array and that is shown in Table III.The cogeneration plant was run on the basis of Table III.It has nine trials.

© 2012 AMAE DOI: 02.AIPE.2012.2. 507

TABLE IV. N UMBER

3

OF

FAILURES OCCURRED DURING THE EXPERIMENTS

Full Paper Proc. of Int. Conf. on Advances in Industrial and Production Engineering 2012 The failures are ‘the smaller is the better type’ of quality characteristics. Hence, smaller is the better S/N ratio were computed for each of the 9 trials and the values were recorded in Table V.

υT = Total number of trials -1 = 26 VA=SSA/υA= 0.89 FA=VA/Ve= 0.61 Where T is the total of all results, N is the total number of experiments , n is the total number of trials, r is the total number of repetitions , C.F. is the correction factor, SSe is the error sum of square,υA is the DOF of parameter A, υT is the total DOF, VA is the variance due to parameter A, FA is the fratio for parameter A and NA1, NA2and NA3 are the number of trails with parameter A at levels 1, 2 and 3, respectively. The sum of squares SSB, SSC and SSD are calculated in the similar way. The results of analysis of variance (ANOVA) are shown in Table VII. In Table VII, asterisk refers to the fact that the contribution factor A is relatively small. Therefore, the level of this factor needs to be merged into the error row in order to decrease the variance and it is clear that the parameter D significantly affect both mean and variation in the grate failure.

TABLE V. PERCENTAGE OF FAILURE VALUES AND S/N R ATIOS AGAINST TRAIL NUMBERS

For example, for trial no. 1, the S/N ratio is: S/N ratio = – 10 log [1/3(1.892 +3.772 +1.892)] = – 8.52 The mean response refers to the average value of the performance characteristic for each parameter at different levels. The average values of grate failure and S/N ratios for each parameter at different levels are computed and tabulated in Table VI. The average values of grate and S/N ratios for each parameter at different levels are plotted in Figure 2 and Figure3, respectively. On seeing the Figs. 2 and 3, it is clear that the failures of the grate are minimum at the first level of parameter A (A1), the second level of parameter B (B2), the second level of parameter C (C2) and the first level of parameter D (D1). The S/N ratio is also maximum at the same levels of the parameters (A1, B2, C2, D1) as the best values for getting minimum failures of the grate. For example, the average values of grate failure and S/N ratio for factor A at level 1 are calculated as A (1)grate failure = [(y11+ y12+ y13) + (y21+ y22+ y23) + (y31+ y32+ y33)] / 9 = 3.35 A (1)S/N ratio = (S/N1 +S/N 2+ S/N 3)/ 3 = - 11.13

TABLE VI. AVERAGE VALUES OF GRATE FAILURES AND S/N R ATIOS AT D IFFERENT L EVELS

B. Interpretation Method The results so far obtained are not sufficient to optimize the parameters of grate. Hence, the following interpretation methods are required to optimize the parameters of the grate. Percent contribution: Percent contribution is the function of the sum of squares of each significant item. Percent contribution to the total sum of square can be used to evaluate the importance of a change in the process parameter on these quality characteristics [9]. It is calcu- lated using the formulae given below: Percent contribution (P) = (SS’A / SST) *100 SS’A = SSA – (υe) (υA) VA = VA’ + V error Where VA’ is the expected amount of variation due solely to factor A given below: VA = SSA /υA VA’= SS’A /υA Estimating the mean: From Table VIII, it is clear that the parameter D (Fuel type) is the most influential factor and the parameters A, B, C have the least effect on the quality characteristic. The estimation of mean for grate failure is calculated by the following equation: µ = T+ (D1-T) Where T is the average values of grate failure at different levels.

A. Analysis of Variance (ANOVA) The statistical ANOVA is used to investigate which process parameters significantly affect the quality characteristic of grate [7,8]. In this paper, the sum of squares due to a parameter (SS) is calculated by the formulae given below: C.F = T2/N = (100.01)2/ 27 = 370.44 n r SST = Yij2– C.F i=1j=1 SSA = [(A (1)2 / NA1) + (A (2)2 / NA2) + (A (3)2 / NA3)] - C.F. = 1.78 SSe= SST” (SSA+ SSB+ SSC+ SSD) = 26.32 υA = number of levels of parameter A -1 = 2 © 2012 AMAE DOI: 02.AIPE.2012.2. 507

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Full Paper Proc. of Int. Conf. on Advances in Industrial and Production Engineering 2012 TABLE VII. ANOVA FOR GRATE FAILURES

The mean for a selected trial condition for parameters at (A1, B2, C2, D1) is 2.72. Confidence Interval around the estimated mean: An important step in Taguchi’s optimization technique is to conduct confirmation experiments for validating the predicted values. Thus a 95% confidence interval (CI) for the predicted mean of optimum quality characteristic on a confirmation test is estimated using the following two equations [10]. CI3 = [ F(α , 1, υe) Ve (1/ ηeff +1/r)]1/2 ηeff = N/ (1+ total DOF associated in the estimate of mean ) where α is the level of risk, Ve is the error variance, νe is the degrees of freedom for the error, ηeff is the effective number of replications and r is number of test trials.Using the values in Table V , the CI was calculated as follows ηeff = N/ 1+ (total DOF associated in the estimate of mean ) = 27/ (1+2) = 9 α = 1- confidence limits (95%) = 0.05 F ratio = (1, 0.05, 18) = 4.41 (tabulated) Confidence Interval CI3 = ±1.72 The 95% confidence interval of the predicted optimum of the grate failure during the process is: 1.0 <1.72 < 4.44.

AND

S/N R ATIOS

Figure 3. Average values of S/N ratios for each parameter at different levels TABLE VIII. ANOVA FOR G RATE FAILURES, INCLUDING PERCENT C ONTRIBUTION

Confirmation experiments: Three confirmation experiments were conducted at the optimum setting of the process parameters. The furnace temperature was set at the first level (A1), oil pressure was at the second level (B2), hydraulic drive speed was set at the second level (C2) and fuel type was set at the first level (D1). The average of the respondent failures in each experiment is found to be 1.88%; the result was within the CI of the protected optimum of the grate failures. The confirming experiments results gave 1.88% <4.44 % (maximum of CI). Therefore, the selected parameters as well as their appropriate levels are significant enough to obtain the desired result. Figure 2. Average values of grate failures for each parameter at different levels

© 2012 AMAE DOI: 02.AIPE.2012.2. 507

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Full Paper Proc. of Int. Conf. on Advances in Industrial and Production Engineering 2012 VI. RESULT AND ANALYSIS

affects the quality of grate during the process with help of percent contribution. The optimum levels of furnace temperature, oil pressure, hydraulic drive speed and fuel type are estimated. The predicted range of optimum of the grate failure during the process is 1.0<1.72<4.44.

The failures of the grate were observed and recorded during the co-generation process,ANOVA was carried out using the results of observations and then the interpretation methods were used to obtain the percent contribution of each parameter and optimum levels of each parameter: 1. The percent contribution of each parameter to the variation of grate failure and optimum parameter (under economic condition) are shown in Table VIII. 2. The optimum levels of various parameters for minimum grate failure of boiler were shown in Table IX. 3. The predicted range of optimum of grate is 1.0<1.72<4.44.

REFERENCES [1] Y. G. Sangamesh, G. Suchitra and S.H. Jangamshetti, “Performance Assessment of 2500 TCD Cogeneration Plant” Int. J. Sci. Eng. Res.,Vol. 3, Issue 5, May 2012. [2] Graham R. Lobley and Waleed L. Al-Otaibi, “Understanding Boiler Tube Failures”, Saudi Aramco J. Tech., Summer 2008. [3] William L. Macmillan and Ezar J. Ahrend “A New Approach to boiler, pipeline and turbine inspections”, 18th World Conference on Non destructive Testing, Durban, South Africa,16-20 April 2012. [4] Zainal Zakaria and Nor Ismail Hashim, “Corrosion-erosion on waste heat recovery boiler system via blowdown optimization” J. Petroleum Gas Eng., Vol. 3(3), pp. 41-50, March 2012. [5] Sanjyot S. Borker and Prabir Kumar Bandyopadhyay, “Better moisture control in coke manufacturing- a case study”,Indian J. Sci.Tech., Vol. 4, No. 9, Sep 2011. [6] C. Sowmya Danalakshmi and G. Mohankumar, “Analysis of down time and reliabilty estimation in a process industry,” Int. J. Design and Manuf. Tech., Vol.3, No.2, July 2009. [7] Ferit Ficici, Murat Kapsiz and Mesut Durat, “Applications of taguchi design method to study wear behaviour of boronized AISI 1040 steel”, Int. J. Phys. Sci., Vol. 6(2), pp.237-243, January 2011. [8 C. F. Ng, S. Kamaruddin, A.N. Siddiquee and Z.A. Khan, “Experimental investigation on the recycled HDPE and optimization of injection moulding process parameters via Taguchi method” Int. J. Mech. Mater. Eng., Vol.6, No.1,pp. 81-91, 2011. [9] A.K. Lakshminarayanan and V. Balasubramanian, “Process parameters optimisation for frictionstirwelding of RDE- 40 aluminium alloy using Taguchi technique”, Trans.Non ferrous Metal. Soc. China, 18(3): 548-554. 2008. [10] G.J. Tzou, , C.C.Tsao and Y.C. Lin, “ Improvement in the thermal conductivity of aluminum substrate for the desktop PC Central Processing Unit (CPU) by the Taguchi method”,Exp.Therm. Fluid Sci. vol.34, pp.706–710, 2010.

TABLE IX.OPTIMUM PARAMETERS UNDER ECONOMIC CONSIDERATIONS

CONCLUSION In this study, process parameters of grate have been optimized by using Taguchi method and the following conclusions are drawn: It is proved that, the quality of the grate during the cogeneration process is improved by Taguchi’s method at the lowest possible cost. Ishikawa diagram or cause and effect diagram is very effective to sort out all the possible causes affecting the quality of grate during the process. The most significant parameters affecting the quality of the grate during the process are identified by using the FMEA tool. It has been observed that the parameter fuel type significantly

© 2012 AMAE DOI: 02.AIPE.2012.2. 507

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