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Artificial Intelligence and Data Science in Environmental Sensing

School of Engineering, Macquarie University, Sydney, NSW, Australia

UNESCO Centre for Membrane Science and Technology, School of Chemical Engineering, University of New South Wales, Sydney, NSW, Australia

Amin Beheshti

School of Computing, Macquarie University, Sydney, NSW, Australia

Series editor

School of Computing Science and Engineering, Vellore Institute of Technology (VIT), Vellore, India

Table of Contents

Cover image

Title page

Copyright

Contributors

Editor Bio

Preface

Chapter 1. Smart sensing technologies for wastewater treatment plants

1. Introduction

2. Online estimation

3. Fault detection and diagnostics

4. Multivariate analysis models

5. Conclusion and future direction

Chapter 2. Advancements and artificial intelligence approaches in antennas for environmental sensing

1. Printed antennas for wireless sensor networks

2 Printed antenna sensors for material characterization

3. Epidermal antenna for unobtrusive human-centric wireless communications and sensing

4. Artificial intelligence in antenna design

Chapter 3. Intelligent geo-sensing for moving toward smart, resilient, low emission, and less carbon transport

1. Introduction

2. The role of transport in the economy and environment

3. Geo-sensing; evolution in the geography

4. Geographic Information System as a revolution or/and an evolution

5. Geo-sensing for moving toward eco-routing and low-emission transport

6. Intelligent geo-sensing and AI as a new window to the future

7. Conclusion

Chapter 4. Language of response surface methodology as an experimental strategy for electrochemical wastewater treatment process optimization

1. Introduction

2. Strategy of response surface methodology

3. Practical application of RSM in electrochemical processes for wastewater treatment

4 Merits and demerits of RSM

5. Conclusions

Chapter 5. Artificial intelligence and sustainability: solutions to social and environmental challenges

1. Introduction

2. AI and social change: the case of food and garden waste management

3. AI and ecosystem services: insights into bushfire management and renewable energy production

4. Challenges of using AI to achieve sustainability

5. Implications and conclusion

Chapter 6. Application of multi-criteria decision-making tools for a site analysis of offshore wind turbines

1. Decision-making in renewable energy investments

2. Decision-making tools on the development and design of offshore wind power farms

3. Background of multiattribute decision-making tools

4. Background of multiobjective problems in offshore and wind farms

Chapter 7. Recent advances of image processing techniques in agriculture

1. Introduction

2 Application in plants detection

3. Application in livestock recognition

4. Application in fruits and vegetables recognition

5 Conclusion

Chapter 8. Tuning swarm behavior for environmental sensing tasks represented as coverage problems

1 Introduction

2. Preliminaries

3. System design: swarming for coverage tasks

4 Experimental analysis

5. Conclusions and future work

Appendix

Chapter 9. Machine learning applications for developing sustainable construction materials

1. Introduction

2 Prediction

3. Damage segmentation and detection

4. Mixture design

5. Multiobjective optimization

6. Conclusions

Chapter 10. The AI-assisted removal and sensor-based detection of contaminants in the aquatic environment

1. Introduction

2. AI-assisted techniques for PFAS detection and removal

3. Sensors for detection of PFAS

4. Biosensors

5. Disinfection by-products

6. AI-assisted techniques for removal of heavy metal

Chapter 11. Recent progress in biosensors for wastewater monitoring and surveillance

1. Introduction

2. Principles and working of BES as a biosensor

3. Biosensor for various pollutant monitoring

4. Photoelectrochemical biosensors

5. Biosensors as a perspective to monitor infectious disease outbreak

6. Conclusions, future trends, and prospective of biosensors

Chapter 12. Machine learning in surface plasmon resonance for environmental monitoring

1. Introduction

2 Surface plasmon resonance

3. Environmental hazard monitoring by SPR

4. Machine learning algorithms in SPR

5 Applications of ML in SPR

6. Conclusion and future perspectives Index

Copyright

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Contributors

Rouzbeh Abbassi, School of Engineering, Faculty of Science and Engineering, Macquarie University, Sydney, NSW, Australia

Shadi Abpeikar, School of Engineering and Information Technology, University of New South Wales, Canberra, ACT, Australia

Hossein Adel, School of Civil Engineering, Iran University of Science and Technology, Tehran, Iran

Sreenatha Anavai, School of Engineering and Information Technology, University of New South Wales, Canberra, ACT, Australia

Ivan Bakhshayeshi

School of Computing, Macquarie University, Sydney, NSW, Australia

Faculty of Science and Engineering, Macquarie University, Sydeny, NSW, Australia

Nima Bayat-Makou, The Edward S. Rogers Sr., Department of Electrical and Computer Engineering, University of Toronto, Toronto, ON, Canada

Amin Beheshti, School of Computing, Macquarie University, Sydney, NSW, Australia

Rhiannon Blake, School of Geography, Earth, and Environmental Sciences, University of Birmingham, Birmingham, United Kingdom

Bahareh Dabirmanesh, Department of Biochemistry, Faculty of Biological Sciences, Tarbiat Modares University, Tehran, Iran

Eila Erfani, Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW, Australia

Milad Rabbabni Esfahani, Department of Chemical and Biological Engineering, The University of Alabama, Tuscaloosa, AL, United States

Karu P. Esselle, School of Electrical and Data Engineering, University of Technology Sydney (UTS), Sydney, NSW, Australia

Zahra Falahati, Department of Biological Sciences, Institute for Advanced Studies in Basic Sciences (IASBS), Zanjan, Iran

Helia Farhood, School of Computing, Macquarie University, Sydney, NSW, Australia

Vikram Garaniya, Australian Maritime College, College of Sciences and Engineering, University of Tasmania, Launceston, TAS, Australia

Mahew Garra, School of Engineering and Information Technology, University of New South Wales, Canberra, ACT, Australia

Omid Ghaffarpasand, School of Geography, Earth, and Environmental Sciences, University of Birmingham, Birmingham, United Kingdom

Ebrahim Ghasemy, Centre Énergie Matériaux Télécommunications, Institut National De La Recherché, Varennes, QC, Canada

A. Yagmur Goren, Department of Environmental Engineering, Izmir Institute of Technology, Urla, Izmir, Turkey

Supriya Gupta

Environment and Sustainability Department, CSIR-Institute of Minerals and Materials Technology, Bhubaneswar, Odisha, India Academy of Scientific and Innovative Research (AcSIR), CSIRHuman Resource Development Centre, CSIR-HRDC Campus, Ghaziabad, India

Asghar Habibnejad Korayem, School of Civil Engineering, Iran University of Science and Technology, Tehran, Iran

Bavly Hanna, Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW, Australia

Ahmad Hosseinzadeh, Centre for Technology in Water and Wastewater, University of Technology Sydney, Sydney, NSW, Australia

Majid Ilchi Ghazaan, School of Civil Engineering, Iran University of Science and Technology, Tehran, Iran

Ahmad Miri Jahromi

Computational Biology and Chemistry Group (CBCG), Universal Scientific Education and Research Network (USERN), Tehran, Iran Department of Petroleum Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran

Alexandros Karatopouzis, Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW, Australia

Elika Karbassiyazdi, Centre for Technology in Water and Wastewater, University of Technology Sydney, Sydney, NSW, Australia

Kathryn Kasmarik, School of Engineering and Information Technology, University of New South Wales, Canberra, ACT, Australia

Khosro Khajeh, Department of Biochemistry, Faculty of Biological Sciences, Tarbiat Modares University, Tehran, Iran

Md Mohiuddin Khan, School of Engineering and Information Technology, University of New South Wales, Canberra, ACT, Australia

Alireza Khataee

Department of Environmental Engineering, Gebze Technical University, Gebze, Turkey

Research Laboratory of Advanced Water and Wastewater Treatment Processes, Department of Applied Chemistry, Faculty of Chemistry, University of Tabriz, Tabriz, Iran

Mohammad Khedri, Computational Biology and Chemistry Group (CBCG), Universal Scientific Education and Research Network (USERN), Tehran, Iran

Ahmed A. Kishk, The Department of Electrical and Computer Engineering, Concordia University, Montreal, QC, Canada

Ali Lalbakhsh

The School of Engineering, Macquarie University, Sydney, NSW, Australia

School of Electrical and Data Engineering, University of Technology Sydney (UTS), Sydney, NSW, Australia

Reza Maleki

Computational Biology and Chemistry Group (CBCG), Universal Scientific Education and Research Network (USERN), Tehran, Iran

Department of Chemical Engineering, Shiraz University, Shiraz, Iran

Yamini Mial

Environment and Sustainability Department, CSIR-Institute of Minerals and Materials Technology, Bhubaneswar, Odisha, India

Academy of Scientific and Innovative Research (AcSIR), CSIRHuman Resource Development Centre, CSIR-HRDC Campus, Ghaziabad, India

Sweta Modak, Department of Chemical and Biological Engineering, The University of Alabama, Tuscaloosa, AL, United States

Masoud Mohseni-Dargah

Department of Biochemistry, Faculty of Biological Sciences, Tarbiat Modares University, Tehran, Iran

School of Engineering, Macquarie University, Sydney, NSW, Australia

Hadi Mokarizadeh, Department of Chemical Engineering, Amirkabir University of Technology, Tehran, Iran

Parisa Nasrollahi, Department of Nanobiotechnology, Faculty of Biological Sciences, Tarbiat Modares University, Tehran, Iran

Arman Nedjati, Industrial Engineering Department, Quchan University of Technology, Quchan, Iran

Matineh Pooshideh, School of Computing, Macquarie University, Sydney, NSW, Australia

Yaşar K. Recepoğlu, Department of Chemical Engineering, Izmir Institute of Technology, Urla, Izmir, Turkey

Nabi Rezvani, School of Computing, Macquarie University, Sydney, NSW, Australia

Roy B.V.B. Simorangkir, Tyndall National Institute, Cork, Ireland

Pratiksha Srivastava, Australian Maritime College, College of Sciences and Engineering, University of Tasmania, Launceston, TAS, Australia

Firouzeh Taghikhah

College of Asia and the Pacific, Australian National University, Canberra, ACT, Australia

Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW, Australia

Sara Tayari, Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW, Australia

Phi Vu Tran, School of Engineering and Information Technology, University of New South Wales, Canberra, ACT, Australia

Mohammad Yazdi

Centre for Risk, Integrity, and Safety Engineering (C-RISE), Faculty of Engineering and Applied Science, Memorial University of Newfoundland, St. John's, NL, Canada

School of Engineering, Faculty of Science and Engineering, Macquarie University, Sydney, NSW, Australia

Esmaeil Zarei, Centre for Risk, Integrity, and Safety Engineering (C-RISE), Faculty of Engineering and Applied Science, Memorial University of Newfoundland, St. John's, NL, Canada

Editor Bio

Mohsen Asadnia is an Associate Professor and group leader in Mechatronics-biomechanics at Macquarie University, Australia. He received his PhD degree in Mechanical Engineering from Nanyang Technological University, Singapore. Prior to joining Macquarie University, Mohsen had several teaching and research roles with the University of Western Australia, Massachuses Institute of Technology, and Nanyang Technological University. His work has resulted in over 150 peer-reviewed journal articles published in prestigious journals, including Nature Communication, Advanced Materials, and Nano-Micro Leers. His research interest lies in environmental/biomedical sensors, artificial hearing implant devices, microfluidics, artificial intelligence, and bio-inspired sensing.

Amir Razmjou received his PhD in Chemical Engineering from the University of New South Wales, Australia, in 2012, and since then he has accrued multidisciplinary skills to develop innovative technologies for biomedical and environmental applications. His surface architecturing skills using functional nanostructured materials alongside biofunctionalization have helped him to develop innovative membranes for desalination and water treatment and nanobiosensors.

Amin Beheshti is a Full Professor of Data Science and the Director of AI-enabled Processes Research Centre, School of Computing, Macquarie University. Amin is also the Head of the Data Analytics Research Lab and Adjunct Academic in Computer Science at UNSW Sydney. Amin completed his PhD and postdoctoral degrees in Computer Science and Engineering at UNSW Sydney and holds master's and bachelor's degrees in Computer Science both with First

Class Honours. He is the leading author of several authored books in data, social, and process analytics and has co-authored with other high-profile researchers.

Preface

Industrialization and population growth have resulted in significant environmental implications such as global warming, waste disposal, ocean acidification, deforestation, etc. A sustainable clean energy future requires systems with zero carbon and water footprints, which requires advanced materials and autonomous processes. The Digital revolution has fast-forwarded the transition from fossil fuel to a renewable civilization, which can reverse the damage caused by human activities to the environment. Sensors are the pillars of the digital revolution which generates data for developing advanced mathematical models and autonomous processes. Novel sensing technologies and advancements in data processing are our greatest tools to fight against various ways that humans have affected the environment such as overpopulation, pollution, burning fossil fuels, deforestation, etc. Using these technologies, we can find ways to reduce climate change, soil erosion, poor air and water quality, and keep our planet “green” for the next generations. In the last decade, considerable research works have been carried out on developing sensitive, low-powered, and durable sensors for environmental sensing. Artificial intelligence (AI) and big data processing techniques and algorithms made it possible to create continuous monitoring systems to minimize the effect of human activities on the environment. We devoted this book to exploring new opportunities and possibilities in using advanced devices and AI for various environment sensing applications which will be published in the name of “Artificial Intelligence and Data Science in Environmental Sensing.”

This book is divided into 12 chapters which are 1. Smart sensing technologies for wastewater treatment plants; 2. Recent advancement in antennas for environmental sensing; 3. Intelligent geo-sensing for moving toward smart, resilient, low emission, and less carbon transport; 4. Language of Response Surface Methodology (RSM) as an experimental strategy for electrochemical wastewater treatment process optimization; 5. Artificial intelligence and sustainability: solutions to social and environmental challenges; 6. Application of multiaribute decision making tools for site analysis of offshore wind turbines; 7. Recent Advances of Image Processing Techniques in Agriculture; 8. Applications of Swarm Intelligence in Environmental Sensing; 9. Machine learning applications for developing sustainable construction materials; 10. The AI-assisted removal process of contaminants in the aquatic environment; 11. Recent progress in biosensors and data processing systems for wastewater monitoring and surveillance; 12. Machine learning in surface plasmon resonance for environmental monitoring. The authors sincerely thank various researchers who contributed to the chapters by sharing their findings and knowledge.

Mohsen Asadnia
Amir Razmjou

Chapter 1: Smart sensing technologies for wastewater treatment plants

Reza Maleki 1 , Ahmad Miri Jahromi 1 , Ebrahim Ghasemy 2 , and Mohammad Khedri 1 1 Computational Biology and Chemistry Group (CBCG), Universal Scientific Education and Research Network (USERN), Tehran, Iran 2 Centre Énergie Matériaux Télécommunications, Institut National De La Recherché, Varennes, QC, Canada

Abstract

The wastewater treatment plants performance is a function of various factors including wastewater quality, management conditions of the treatment plant, and environmental issues. Disposal of wastewater with acceptable quality characteristics to a variety of receiving sources is one of the environmental problems that today's societies face. In addition to transmiing microbial and chemical pathogens to humans, wastewater release destroys many aquatic species in rivers, lakes, and oceans. Due to its inherent and nonlinear characteristics, modeling a municipal sewage refinery is complex and difficult. Due to the increasing concerns about the environmental effects of refineries due to poor operation, fluctuations of process variables, and problems of online analyzers, artificial process control algorithms such as artificial neural networks have aracted a lot of aention due to increasing intelligence. An artificial intelligence network is a set of neurons that are located in different layers, forming a special architecture based on the

connection between neurons. So that, the neuron is a nonlinear mathematical unit, and as a result, a neural network will be a complex and nonlinear system. This chapter discusses the literature to conduct a large-scale bibliometric analysis of traits inside the application of artificial intelligence generation to wastewater treatment. In addition, the use of marine sensors for simultaneous collection of relevant environmental data in parallel with the acquisition of visual data in error detection and detection, online estimation, and analysis of multivariate models will be investigated.

Keywords

Artificial intelligence; Environmental sensing; Treatment plants; Wastewater treatment

1. Introduction

The most important goals of constructing wastewater treatment systems include the protection of homogeneity, environmental protection, preventing the pollution of water sources, and the reuse of treated wastewater in sectors such as agriculture and industry [1,2]. So, reducing water pollutants and improving water quality in the wastewater treatment plant (WWTP) is essential. The establishment of WWTPs alone does not solve environmental concerns, but in order to reach the desired environmental standards, the performance of treatment plants must be constantly monitored and evaluated. Typical parameters that are considered to evaluate the performance of WWTPs are biological oxygen demand (BOD), suspended solids, soluble solids, and pH of wastewater [3–6]. If these parameters meet the standards, effluents could be used in sectors such as agriculture and industry. This can help solve the water shortage crisis to some extent [7].

The complex composition of wastewater has different diffusion properties and concentrations of pollutants and effluents in WWTPs [8–11]. Wastewater is rich in toxic substances such as lead, copper,

nickel, silver, mercury, chromium, zinc, cadmium or tin, nutrients, and organic maer and can also have a wide range of pH [12,13]. Complex natural phenomena, human activities, and the process of wastewater treatment have led to great uncertainty in wastewater treatment systems. These uncertainties fluctuate randomly due to the amount, quality, and efficiency of wastewater disposal [14]. Currently, with stricter regulations on effluent quality, the operation of a WWTP has become more difficult and complex. Improper use of a WWTP can lead to general health and environmental problems. The entry of effluent from these treatment plants into water sources can spread various human diseases [15]. Wastewater treatment operations include a set of complex processes and their dynamics are nonlinear and change over time and can directly overshadow the operation of the treatment plant. Fig. 1.1 shows the simple process of wastewater treatment. Moreover, the input characteristics of each treatment plant vary depending on the area covered. Therefore, the performance of any treatment plant strongly depends on recognizing the main factors affecting the treatment plant. In addition, random disturbances and effective variability will force operators to perform suitable operational controls on the system [12,13]. Also, modern WWTPs face stricter emission restrictions as well as new regulations on energy efficiency and resource recycling [16].

FIGURE 1.1 Wastewater treatment plant process to prevent disposal of wastewater into the environment.

Given the above, today, in addition to the operation of the treatment plant, it is important to pay aention to mathematical models to predict the performance of the treatment plant. The response of the process to any change can be examined by mathematical simulation and can ultimately be achieved with an output stream of optimal quality and low operating costs. As mentioned, due to the variable nature of wastewater, to maintain the stability of treatment processes in optimal conditions, the proper operation of the WWTP is of great importance. However, the WWTP modeling is very difficult due to the nonlinear relationships of effective parameters, but the use of conceptual models to prevent growing concerns about environmental impacts and to help engineers to predict treatment plant behavior as well as complex treatment processes has received tremendous aentions [17–19]. In this regard, artificial intelligence models can be used as an effective tool to simulate the behavior of the treatment system. Therefore,

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