Machine Learning in Water Treatment
Wiley-Scrivener (Verlag)
978-1-394-30349-6 (ISBN)
Machine Learning in Water Treatment explores the complex fields of wastewater treatment and water purification, offering a thorough analysis of the cutting-edge machine learning methods used to solve problems with water quality control. It provides insights into how artificial intelligence can be incorporated with conventional procedures, bridging the gap between conventional water treatment techniques and state-of-the-art data-driven solutions. The book will cover the foundations of water treatment procedures, providing insights into the ideas behind physical, chemical, and biological treatment modalities. Difficulties in managing water and wastewater quality are paving the way for the use of machine learning as an effective tool for control and optimization.
Fundamentally, the book explains how machine learning models are used in water treatment system control, optimization, and predictive modeling. Readers will learn how to take advantage of machine learning algorithms’ potential for real-time treatment process optimization, quality issue identification, and water pollutant level prediction through a thorough investigation of data collection, preprocessing, and model creation. Case studies and real-world applications provide insightful information about the application of machine learning technologies in a variety of scenarios. With its unique combination of theoretical understanding and real-world applications, this book is an invaluable tool for understanding how water quality management is changing in the age of data-driven decision-making.
Rakesh Namdeti, PhD is a lecturer in the Department of Chemical Engineering at the University of Technology and Applied Sciences, Salalah. He has over 20 publications, including book chapters and articles in international journals of repute. His research interests include chemical processes, separation technology, and petroleum refining. Arlene Abuda Joaquin, PhD is lecturer in the Department of Chemical Engineering at the University of Technology and Applied Sciences, Salalah. She is credited with over 15 publications, including book chapters and articles in international journals. Her research focuses on water and wastewater treatment, water quality, and environmental pollution.
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
| Erscheinungsdatum | 18.11.2025 |
|---|---|
| Sprache | englisch |
| Themenwelt | Naturwissenschaften ► Chemie |
| ISBN-10 | 1-394-30349-1 / 1394303491 |
| ISBN-13 | 978-1-394-30349-6 / 9781394303496 |
| Zustand | Neuware |
| Informationen gemäß Produktsicherheitsverordnung (GPSR) | |
| Haben Sie eine Frage zum Produkt? |
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