Machine Learning in Water Treatment

Preface xxvii 1 Overview of Wastewater Treatment and Water Purification 1 Sivarethinamohan R. 1.1 Clean Water: Its Significance for Society 1 1.2 Production of Clean Water 2 1.3 The Quality of Good Water 3 1.4 Standards for Drinking Water 3 1.5 The Significance of “Clean Water for All” 4 1.6 Value of Clean Water 4 1.7 Clean Water Conflict in the 21st Century 5 1.8 Water Pollutants’ Propensity to Harm Human Health 6 1.9 Impact of Clean Water on the General Well-Being of Humans 6 1.10 Why Communities Demand Clean Water for Socioeconomic Growth, Energy and Food Production, Survival and Health, and Healthy Ecosystems 7 1.11 Accomplishing SDGs 6.1 and 6.2 to Ensure Sustainable Water and Sanitation Management for All 8 1.12 Potential Clean Water Technologies in Use 8 1.13 Clean Water System 9 1.14 Steps Involved in Treating Wastewater 10 1.15 Water Purification Technology 11 1.16 Conclusion 12 References 13 2 A Brief Study on Methods of Preparing Data for Machine Learning Models 15 Chandra Pal M., Abhishek Dubey, Regula Thirupathi, Mohammed Ghouse Haneef Maqsood and Hansel Delos Santos 2.1 Introduction 16 2.2 Data Collection and Integration 16 2.3 Data Cleaning 17 2.4 Data Transformation and Feature Engineering 18 2.5 Data Splitting 19 2.6 Handling Imbalanced Data 19 2.7 Dimensionality Reduction 20 2.8 Data Augmentation 20 2.9 Feature Scaling for Time Series Data 21 2.10 Conclusion 21 References 22 3 Experimental Investigation of Greywater Treatment and Reuse Using a Wetland Adsorption System 23 Nageswara Rao Lakkimsetty, Clement Varaprasad Karu and Dadamiah PMD Shaik 3.1 Introduction 23 3.2 Materials 24 3.3 Analytical Techniques 24 3.4 Results and Discussion 25 3.5 Post and Pre-Treatment Analysis Results 25 3.6 Gas Chromatography and Mass Spectrometer (GC-MS) 26 3.7 Conclusions 29 References 29 4 Water Purification and Wastewater Treatment Challenges 31 Pradeep Kumar Ramteke and Ajit P. Rathod 4.1 Introduction 32 4.2 Current State of Water Purification Technologies 34 4.3 Challenges in Water Purification 35 4.4 Wastewater Treatments: Current Practices and Innovation 36 4.5 Wastewater Treatments Have an Effect on Human Health and the Environment 38 4.6 Management of Treatment Byproducts 41 4.7 Impact of Climate Change on Water Resources 44 4.8 Sustainable Practices and Resource Recovery 46 4.9 Conclusion 47 References 48 5 Innovative Wastewater Treatment Technology: Integrating Microalgae in Aeration Reactors with Advanced Oxidation for Enhanced Water Quality 55 Nageswara Rao Lakkimsetty and G. Kavitha 5.1 Introduction 55 5.2 Methodology 57 5.3 Results and Discussion 58 5.4 Conclusions 61 References 61 6 Hydrogen Production from Wastewater by Photo-Electrolysis: A Brief Review 65 Umareddy Meka 6.1 Introduction 66 6.2 Hydrogen Production Technologies 67 6.3 Wastewater as a Resource for Hydrogen Production 69 6.4 Photo-Electrolysis 71 6.5 Recent Advances in Photo-Electrolysis 74 6.6 Applications and Future Prospects 76 6.7 Environmental and Economic Considerations 78 6.8 Conclusion 80 References 81 7 Synopsis of Water Treatment Techniques 83 Prachiprava Pradhan and Ajit P. Rathod 7.1 Introduction 84 7.2 Pressure-Driven Membrane Technologies 85 7.3 Progress of Membrane Technologies for Water Treatment 86 7.4 Advancements in Membrane Technology for Wastewater Treatment 87 7.5 Conclusion 91 References 91 8 Physical Water Treatment Principles 97 Rajdeep Mallick, Soham Saha, Devanshi Datta, Susanket Pal and Subhasis Roy 8.1 Introduction to Physical Water Treatment 97 8.2 Principles of Physical Water Treatment 100 8.3 Advanced Physical Water Treatment Technologies 112 8.4 Case Studies and Applications 120 8.5 Conclusions 124 Acknowledgement 124 References 125 9 Chemical Purification Procedures of Water 131 Senthilnathan Nachiappan, Jayakaran Pachiyappan, Balakrishna Moorthy, Senthil Rathi Balasubramani and Karuppasamy Ramanathan 9.1 Introduction to Water Purification 131 9.2 Traditional Chemical Purification Methods 133 9.3 Emerging Chemical Purification Technologies 135 9.4 Nanotechnology in Water Purification 139 9.5 Environmental and Health Impacts of Chemical Purification 139 9.6 Regulatory Frameworks and Standards in Water Purification 140 9.7 Future Directions and Research Opportunities 140 9.8 Conclusions 141 References 142 10 Biological Treatment Methods for Remediating Wastewater 145 Pradeep Kumar Ramteke and Ajit P. Rathod 10.1 Introduction 146 10.2 Fundamentals of Wastewater and Its Treatment 148 10.3 Microbiology of Wastewater Treatment 151 10.4 Differences between Anaerobic Treatment Methods and Aerobic Treatment Methods 153 10.5 Biofilm-Based Treatment Processes 154 10.6 Advanced Biological Treatment Technologies 157 10.7 Case Studies and Practical Applications 159 10.8 Challenges and Future Directions 161 10.9 Conclusion 162 References 162 11 Techniques for Gathering, Preparing, and Managing Water Quality Data 169 BVS Praveen, B. Ganesh, Raj Kumar Verma, M. Neha Shree and M. Sandeep Kumar 11.1 Introduction 170 11.2 Data Collection and Preprocessing for AI/ML Models 172 11.3 Applying Machine Learning to Water Quality Analysis 175 11.4 Deep Learning Approaches for Water Quality Data Management 183 11.5 AI for Real-Time Water Quality Monitoring and Management 185 11.6 Challenges and Future Directions in AI/ML for Water Quality Data 186 11.7 Conclusions 187 References 187 12 Overview of Machine Learning and Its Uses 191 Chandra Pal M., Abhishek Dubey, Suresh Kumar, Mohammed Maqsood and Mohammed Arshad Ali 12.1 Introduction to the Key Concepts 192 12.2 The Essential Building Blocks of ml 194 12.3 Future Trends and Developments 200 Bibliography 201 13 Advanced Techniques for Water Quality Data Management Using Machine Learning 203 BVS Praveen, Raj Kumar Verma, M. Neha Sree and Y. Varsha 13.1 Introduction 204 13.2 Overview of Machine Learning 205 13.3 Advanced Machine Learning Techniques for Different Water Environments 206 13.4 Challenges and Limitations on Water Quality in Machine Learning 219 13.5 Conclusions 221 References 221 14 Water Treatment Process Optimization Techniques 225 Prachiprava Pradhan and Ajit P. Rathod 14.1 Introduction 226 14.2 Optimization of Drinking Water Treatment Plant 227 14.3 Water Treatment Process Optimization 230 14.4 Conclusion 233 References 233 15 Optimization of Biological Treatment Processes Through Machine Learning for Remediating Wastewater 237 Aparna Ray Sarkar and Dwaipayan Sen 15.1 Introduction 238 15.2 Conventional Activated Sludge Treatment (CAS) 239 15.3 Sequencing Batch Reactor (SBR) 240 15.4 Integrated Fixed Film Activated Sludge (IFAS) 242 15.5 Moving Bed Media Bio Reactor (MBBR) 244 15.6 Membrane Bioreactor (MBR) 245 15.7 Machine Learning: A Tool to Explore Wastewater Remediation Process 247 15.8 Application of ML in Bioremediation of Wastewater and Parametric Optimization 259 15.9 Conclusion 262 References 262 16 Innovative Techniques for Enhancing Water Treatment Efficiency 265 B. Sumalatha, D. Syam Babu, B. Sudarsini and M. Indira 16.1 Introduction to Water Treatment Process and Optimization 266 16.2 Importance and Goals of Process Optimization 266 16.3 Overview of Water Treatment Process 269 16.4 Performance Metrics and Evaluation Criteria 271 16.5 Advanced Optimization Techniques 274 16.6 Optimization of Specific Treatment Processes 277 16.7 Machine Learning Optimization Approaches 279 16.8 Challenges and Limitations 282 16.9 Future Directions and Innovations 282 16.10 Conclusions 283 References 283 17 Advancement in Machine Learning-Aided Advanced Oxidation Processes for Water Treatment 293 Prashant Kumar, Suparna Bhattacharyya and Biswajit Debnath 17.1 Introduction 293 17.2 Fundamentals of Advanced Oxidation Processes and Machine Learning 296 17.3 Machine Learning Applications in AOPs for Water Treatment 298 17.4 Case-Studies and Successful Implementations 303 17.5 Challenges and Future Directions 315 17.6 Conclusion 316 References 316 18 Machine Learning Strategies for Wastewater Treatment Toward Zero Liquid Discharge in a Lignocellulosic Biorefinery 323 P. Kalpana, S. Sharanya and P. Anand 18.1 Introduction 324 18.2 Processing of Biomass 327 18.3 Development of Models in Treatment Process 330 18.4 Implementation Steps for Machine Learning in ZLD 335 18.5 Conclusion 338 Acknowledgements 339 References 339 19 Machine Learning Techniques in Water Treatment 345 Naveen Prasad B. S., Umareddy Meka, Rajasekaran R. and Saikat Banerjee 19.1 Introduction 345 19.2 Overview of Machine Learning 351 19.3 Applications of ML in Water Treatment 352 19.4 Data Sources and Preprocessing for Water Treatment 357 19.5 Supervised Learning Techniques for Water Treatment 371 19.6 Unsupervised Learning Techniques 376 19.7 Deep Learning in Water Treatment 380 19.8 Reinforcement Learning in Water Treatment 388 19.9 Case Studies and Real-World Applications 392 19.10 Challenges and Limitations of ML in Water Treatment 395 19.11 Future Trends and Research Directions 401 19.12 Conclusion 404 References 405 20 Bionanocomposites as Innovative Bioadsorbents for Wastewater Remediation: A Comprehensive Exploration 413 Rebika Baruah and Archana Moni Das 20.1 Introduction 413 20.2 Research Methods 415 20.3 Application of Bionanocomposites in the Wastewater Treatment 432 20.4 Conclusion 447 Acknowledgments 447 References 447 21 Utilizations of Machine Learning Algorithms in the Context of Biological Wastewater Treatment: Recent Developments and Future Prospects 453 Sonanki Keshri and Ujwala N. Patil 21.1 Introduction 454 21.2 Principles of Water Treatment Methods 456 21.3 Introduction to Machine Learning in Wastewater Treatment 459 21.4 ml in Wastewater Treatment 463 21.5 Case Studies and Practical Applications 468 21.6 Applications in Water Quality Management 470 21.7 Challenges and Limitations 473 21.8 Future Prospects and Research Directions 473 21.9 Final Conclusions 474 References 474 22 A Comprehensive Review on Machine Learning Techniques for Wastewater and Water Purification 483 Sonanki Keshri and Sudha S. 22.1 Introduction 484 22.2 Synopsis of Water Treatment Techniques 486 22.3 Machine Learning Algorithms and their Application in Wastewater Treatment 492 22.4 Wastewater Treatment Modeling Using ml 495 22.5 Application of ML in Water-Based Agriculture 504 22.6 Challenges with ML Implementation in Water Treatment and Monitoring 505 22.7 Recommendations for ML Implementation in Water Treatment and Monitoring 506 22.8 Conclusions 507 References 508 23 Water and Wastewater Treatment and Technological Remedies for Preserving Water Quality and Implementation of Machine Learning 517 Nishat Fatima and Prema P. M. 23.1 Introduction 517 23.2 Conventional Water and Wastewater Treatment Methods 518 23.3 Technological Innovations for Water Quality Preservation 523 23.4 ml in Water and Wastewater Treatment 530 23.5 Conclusion 532 References 532 24 Experimental Study on Wastewater Treatment and Reuse Using a Biofiltration System with Machine Learning-Based Optimization 535 Jayakaran Pachiyappan and Senthilnathan Nachiappan 24.1 Introduction 535 24.2 Objectives 538 24.3 Scope of the Chapter 538 24.4 Literature Review 539 24.5 Methodology 540 24.6 Results and Discussion 542 24.7 Conclusion 544 References 544 25 A Review on Machine Learning in Environmental Engineering: A Focus on the Gray Water Treatment 547 Vamsi Krishna Kudapa, Patchamatla J. Rama Raju, Arbind Ghataney and Nageswara Rao Lakkimsetty 25.1 Introduction 548 25.2 Gray Water Treatment by Using ML Techniques 549 25.3 Usage of ML in Gray Water Treatment 554 25.4 ANN-Based IoT Incorporation of Gray Water Treatment in Malaysia: A Case Study 556 25.5 Case Study 2: Implementation of RF Model in Gray Water Treatment 557 25.6 Challenges and Future Directions for ML-Based Gray Water Treatment 557 25.7 Conclusion 558 Bibliography 558 26 Machine Learning Techniques for Wastewater Treatment and Water Purification: Review of State-Of-The-Art Practices and Applications 561 Swarnadeep Saha, Protyasha Kundu, Sumanta Banerjee and Anindita Kundu 26.1 Introduction 562 26.2 Literature Survey 564 26.3 ml Models 570 26.4 Case Study I: Prediction of Water Quality Index Using ElasticNet 576 26.5 Case Study II: Prediction of Water Potability Using Extra Trees Classifier 579 26.6 Conclusion 581 References 583 27 Application of Predictive Modeling Approaches for Water Quality Prediction 587 Ritam Das, Jumasri Ganguly, Saubhagya Mukherjee, Ivy Ray, Raj Kumar Arya and Pramita Sen 27.1 Introduction 588 27.2 Water Quality Measurement Parameters 590 27.3 Overview of Predictive Modeling and Its Significance in WQ Prediction 592 27.4 Brief Discussion on ML Models 594 27.5 Steps of ML Algorithms in WQ Prediction 599 27.6 Comparing Model Predictions with Experimental Results 600 27.7 Challenges and Future Perspectives 604 References 604 28 Next-Generation Water Purification: Harnessing Machine Learning for Optimal Treatment and Monitoring 609 Rompicherla Srividya, A.V. Raghavendra Rao, Boppena Karuna, Kolluru Sree Manaswini and Sravani Sameera Vanjarana 28.1 Introduction to Machine Learning Techniques 610 28.2 Supervised Learning Techniques 611 28.3 Unsupervised Learning Techniques 615 28.4 Reinforcement Learning Techniques 619 28.5 Hybrid and Ensemble Techniques 622 28.6 Deep Learning Techniques 628 28.7 Emerging Techniques and Future Directions 630 References 630 29 Revolutionizing Water Treatment Facilities with Machine Learning: Techniques, Applications, and Case Studies 637 A.V. Raghavendra Rao, Rompicherla Srividya, Sravani Sameera Vanjarana, B. Karuna and Archana Rao P. 29.1 Introduction 638 29.2 ml Techniques in Water Treatment 639 29.3 Applications of ML in Water Treatment 648 29.4 Case Studies 651 29.5 Challenges and Opportunities 654 29.6 Prospective Developments in ML for Water Treatment Facilities 656 29.7 Conclusion 660 References 660 30 Advanced Techniques for Water Treatment Process Optimization 671 V. Sravani Sameera, Rompicherla Srividya, Anup Ashok, KSNV Prasad, Boppena Karuna, Ganesh Botla and A.V. Raghavendra Rao 30.1 Introduction 671 30.2 ml Techniques for Optimization 673 30.3 Integration of ML Models with Real-Time Monitoring 679 30.4 Challenges and Limitations 683 30.5 Hybrid Optimization Models 686 30.6 Economic and Environmental Impacts 689 30.7 Future Trends and Advancements 692 30.8 Conclusions 696 Bibliography 697 31 Regression Models for Prediction and Evaluation of Water Contamination: A Comparative Study 707 Vamsi Krishna Kudapa, Santhosh Chanemougam, Salman Ahmad and Nageswara Rao Lakkimsetty 31.1 Introduction 707 31.2 Regression Models for Water Quality Prediction 708 31.3 Case Studies on Predictive Water Contamination via Regression 714 31.4 Performance Evaluation Comparison for Different Models 715 31.5 Conclusion 716 Bibliography 717 32 Implications of Regression Analysis for Predicting Water Contamination Levels 719 Nirlipta Priyadarshini Nayak and Rahul Kumar Singh 32.1 Introduction 719 32.2 Regression Analysis for Water Quality Prediction 721 32.3 Existing Regression Analysis Model 723 32.4 Conclusion 724 References 725 Index 729