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Machine Learning in Water Treatment

Edited by Rakesh Namdeti and Arlene Abuda Joaquin
Copyright: 2025   |   Expected Pub Date:2025//
ISBN: 9781394303496  |  Hardcover  |  
768 pages
Price: $225 USD
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One Line Description
Machine Learning in Water Treatment is a must-have for anyone interested in how artificial intelligence is transforming water treatment, offering practical insights, case studies, and a deep dive into cutting-edge machine learning techniques that can improve water quality management.

Audience
Chemical and civil engineers, environmental scientists, water resource management professionals, students, educators, and policymakers looking for solutions to revolutionize water management

Description
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.

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Author / Editor Details
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.

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Table of Contents
Preface
1. Overview of Wastewater Treatment and Water Purification
Sivarethinamohan R.
1.1 Clean Water: Its Significance for Society
1.2 Production of Clean Water
1.3 The Quality of Good Water
1.4 Standards for Drinking Water
1.5 The Significance of “Clean Water for All”
1.6 Value of Clean Water
1.7 Clean Water Conflict in the 21st Century
1.8 Water Pollutants’ Propensity to Harm Human Health
1.9 Impact of Clean Water on the General Well-Being of Humans
1.10 Why Communities Demand Clean Water for Socioeconomic Growth, Energy and Food Production, Survival and Health, and Healthy Ecosystems
1.11 Accomplishing SDGs 6.1 and 6.2 to Ensure Sustainable Water and Sanitation Management for All
1.12 Potential Clean Water Technologies in Use
1.13 Clean Water System
1.14 Steps Involved in Treating Wastewater
1.15 Water Purification Technology
1.16 Conclusion
References
2. A Brief Study on Methods of Preparing Data for Machine Learning Models
Chandra Pal M., Abhishek Dubey, Regula Thirupathi, Mohammed Ghouse Haneef Maqsood and Hansel Delos Santos
2.1 Introduction
2.2 Data Collection and Integration
2.3 Data Cleaning
2.4 Data Transformation and Feature Engineering
2.5 Data Splitting
2.6 Handling Imbalanced Data
2.7 Dimensionality Reduction
2.8 Data Augmentation
2.9 Feature Scaling for Time Series Data
2.10 Conclusion
References
3. Experimental Investigation of Greywater Treatment and Reuse Using a Wetland Adsorption System
Nageswara Rao Lakkimsetty, Clement Varaprasad Karu and Dadamiah PMD Shaik
3.1 Introduction
3.2 Materials
3.3 Analytical Techniques
3.4 Results and Discussion
3.5 Post and Pre-Treatment Analysis Results
3.6 Gas Chromatography and Mass Spectrometer (GC-MS)
3.7 Conclusions
References
4. Water Purification and Wastewater Treatment Challenges
Pradeep Kumar Ramteke and Ajit P. Rathod
4.1 Introduction
4.1.1 Overview of Global Water Scarcity and Pollution Issues
4.1.2 Importance of Water Purification and Wastewater Treatment
4.2 Current State of Water Purification Technologies
4.3 Challenges in Water Purification
4.4 Wastewater Treatments: Current Practices and Innovation
4.4.1 Emerging Contaminants in Water Treatment
4.4.2 Types of Emerging Contaminants
4.5 Wastewater Treatments Have an Effect on Human Health and the Environment
4.5.1 Limitation of Traditional Treatment Methods
4.5.2 Ineffectiveness in Removing Emerging Contaminants
4.5.3 Energy-Intensive Nature of Traditional Treatments Processes
4.5.4 Innovations in Energy-Efficient Treatment Solutions
4.5.5 Low-Energy Desalination
4.6 Management of Treatment Byproducts
4.6.1 Challenges Associated with Sludge Disposal
4.6.2 Environmental Risks of Brine Disposal
4.7 Impact of Climate Change on Water Resources
4.7.1 Changes in Water Availability and Quality
4.7.2 Infrastructure and Technological Upgrades
4.8 Sustainable Practices and Resource Recovery
4.9 Conclusion
References
5. Innovative Wastewater Treatment Technology: Integrating Microalgae in Aeration Reactors with Advanced Oxidation for Enhanced Water Quality
Nageswara Rao Lakkimsetty and G. Kavitha
5.1 Introduction
5.1.1 Aerobic Sludge Process
5.1.2 Advanced Oxidation Process
5.2 Methodology
5.2.1 Aeration-Based Wastewater Treatment Utilizing Active Microalgae
5.2.2 Separation of Treated Microalgae from Wastewater Effluent
5.2.3 Advanced Oxidation Treatment of Microalgae-Processed Wastewater Using H₂O₂
5.2.4 Characterization of the Treated and Purified Wastewater
5.3 Results and Discussion
5.3.1 Effect of Treatment on Nutrient Levels
5.3.2 Effect of Treatment on COD Levels
5.3.3 Effect of Treatment on DO and pH Levels
5.4 Conclusions
References
6. Hydrogen Production from Wastewater by Photo-Electrolysis: A Brief Review
Umareddy Meka
6.1 Introduction
6.1.1 Background and Significance
6.1.2 Objective of the Review
6.2 Hydrogen Production Technologies
6.2.1 Conventional Methods
6.2.2 Emerging Technologies
6.3 Wastewater as a Resource for Hydrogen Production
6.3.1 Characteristics of Wastewater
6.3.2 Advantages and Challenges
6.4 Photo-Electrolysis
6.4.1 Principles and Mechanisms
6.4.2 Types of Photo-Electrolysis Systems
6.5 Recent Advances in Photo-Electrolysis
6.5.1 Efficiency Improvements
6.5.2 Materials Innovation
6.6 Applications and Future Prospects
6.6.1 Current Applications – Implementation and Challenges
6.6.2 Potential Future Developments
6.7 Environmental and Economic Considerations
6.7.1 Sustainability Aspects
6.7.2 Cost Analysis
6.8 Conclusion
References
7. Synopsis of Water Treatment Techniques
Prachiprava Pradhan and Ajit P. Rathod
7.1 Introduction
7.2 Pressure-Driven Membrane Technologies
7.3 Progress of Membrane Technologies for Water Treatment
7.4 Advancements in Membrane Technology for Wastewater Treatment
7.4.1 Carbon Nanotubes
7.4.2 Silver Nanoparticles
7.4.3 Titanium Dioxide Nanoparticles
7.4.4 Iron Oxide Nanoparticles
7.4.5 Zinc Oxide Nanoparticles
7.4.6 Aluminum Oxide Nanoparticles
7.4.7 Silicon Dioxide Nanoparticles
7.5 Conclusion
References
8. Physical Water Treatment Principles
Rajdeep Mallick, Soham Saha, Devanshi Datta, Susanket Pal and Subhasis Roy
8.1 Introduction to Physical Water Treatment
8.1.1 Understanding Water Quality
8.1.1.1 Definition of Water Quality and its Importance in Various Applications
8.1.1.2 Common Contaminants in Water and Their Sources
8.2 Principles of Physical Water Treatment
8.2.1 Filtration
8.2.1.1 Fundamentals of Filtration Processes
8.2.1.2 Types of Filtration Media and Their Properties
8.2.1.3 Mechanisms of Particle Removal in Filtration Systems
8.2.2 Sedimentation
8.2.2.1 Introduction to Sedimentation as a Process for Particle Separation
8.2.2.2 Principles of Settling Velocity and Sedimentation Basin Design
8.2.2.3 Factors Affecting Sedimentation Efficiency, Such as Particle Size, Density, and Hydraulic Loading
8.2.2.4 Optimization Strategies for Enhancing Sedimentation Performance in Water Treatment Plants
8.2.3 Flotation
8.2.3.1 Overview of Flotation as a Separation Process for Suspended Solids and Oils
8.2.3.2 Principles of Bubble-Particle Attachment and Flotation Kinetics
8.2.3.3 Types of Flotation Systems and Their Applications
8.2.3.4 Factors Influencing Flotation Efficiency and Performance
8.2.4 Disinfection
8.2.4.1 Importance of Disinfection in Water Treatment for Pathogen Removal
8.2.4.2 Overview of Physical Disinfection Methods, Including Ultraviolet (UV) Radiation and Ozone Treatment
8.2.4.3 Mechanisms of Microbial Inactivation and Disinfection Kinetics
8.2.4.4 Considerations for Selecting and Optimizing Physical Disinfectio Processes
8.3 Advanced Physical Water Treatment Technologies
8.3.1 Membrane Processes
8.3.1.1 Introduction to Membrane Filtration and Separation Techniques
8.3.1.2 Types of Membrane Materials and Their Applications
8.3.1.3 Operating Principles and Membrane Module Configurations
8.3.2 Electrocoagulation and Electroflotation
8.3.2.1 Overview of Electrochemical Water Treatment Processes
8.3.2.2 Principles of Electrocoagulation and Electroflotation for Pollutant Removal
8.3.2.3 Electrode Materials, Reactor Design, and Operating Parameters
8.3.2.4 Potential Advantages and Limitations of Electrochemical Treatment Methods
8.4 Case Studies and Applications
8.4.1 Industrial Applications
8.4.2 Case Studies Illustrating the Application of Physical Water Treatment Principles in Various Industries
8.4.3 Challenges and Solutions Encountered in Implementing Physical Water Treatment Technologies in Industrial Settings
8.5 Conclusions
Acknowledgement
References
9. Chemical Purification Procedures of Water
Senthilnathan Nachiappan, Jayakaran Pachiyappan, Balakrishna Moorthy, Senthil Rathi Balasubramani and Karuppasamy Ramanathan
9.1 Introduction to Water Purification
9.1.1 Importance of Water Purification
9.2 Traditional Chemical Purification Methods
9.2.1 Chlorination
9.2.2 Coagulation and Flocculation
9.3 Emerging Chemical Purification Technologies
9.3.1 Advanced Oxidation Processes
9.3.2 Membrane Filtration Technologies
9.4 Nanotechnology in Water Purification
9.5 Environmental and Health Impacts of Chemical Purification
9.6 Regulatory Frameworks and Standards in Water Purification
9.7 Future Directions and Research Opportunities
9.8 Conclusions
References
10. Biological Treatment Methods for Remediating Wastewater
Pradeep Kumar Ramteke and Ajit P. Rathod
10.1 Introduction
10.2 Fundamentals of Wastewater and Its Treatment
10.2.1 Characterization of Wastewater
10.3 Microbiology of Wastewater Treatment
10.3.1 Microbial Ecology in Wastewater
10.3.2 Types of Microorganisms in Treatment Processes
10.3.3 Metabolic Pathways and Biochemical Reactions
10.4 Differences between Anaerobic Treatment Methods and Aerobic Treatment Methods
10.5 Biofilm-Based Treatment Processes
10.5.1 Biofilm Formation and Dynamic
10.5.2 Advantage of Moving Bed Biofilm Reactors (MBBRs)
10.5.3 Advantage of Membrane Biofilm Reactors (MBRs)
10.6 Advanced Biological Treatment Technologies
10.6.1 Phytoremediation
10.6.2 Monitoring and Control in Biological Treatment
10.7 Case Studies and Practical Applications
10.8 Challenges and Future Directions
10.9 Conclusion
References
11. Techniques for Gathering, Preparing, and Managing Water Quality Data
B.V.S. Praveen, B. Ganesh, Raj Kumar Verma, M. Neha Shree and M. Sandeep Kumar
11.1 Introduction
11.1.1 Overview of AI and ML
11.1.2 Importance of AI and ML in Water Quality Management (WQM)
11.1.3 Challenge of AI and ML in WQM
11.2 Data Collection and Preprocessing for AI/ML Models
11.2.1 Gathering Water Quality Data
11.2.2 Data Cleaning and Handling Missing Data
11.2.3 Data Normalization and Feature Scaling
11.2.4 Preparing Datasets for AI/ML Models: Training, Validation, and Test Sets
11.3 Applying Machine Learning to Water Quality Analysis
11.3.1 Supervised Learning: Predicting Water Quality Parameters
11.3.2 Unsupervised Learning: Clustering Water Bodies by Pollution Levels
11.3.3 Time-Series Forecasting for Water Quality Trends
11.3.4 Anomaly Detection for Early Warning of Pollution Events
11.4 Deep Learning Approaches for Water Quality Data Management
11.4.1 Neural Networks for Complex Water Quality Predictions
11.4.2 Convolutional Neural Networks (CNNs) for Image-Based Water Quality Assessments
11.4.3 Recurrent Neural Networks (RNNs) for Time-Dependent Water Quality Monitoring
11.5 AI for Real-Time Water Quality Monitoring and Management
11.6 Challenges and Future Directions in AI/ML for Water Quality Data
11.7 Conclusions
References
12. Overview of Machine Learning and Its Uses
Chandra Pal M., Abhishek Dubey, Suresh Kumar, Mohammed Maqsood and Mohammed Arshad Ali
12.1 Introduction to the Key Concepts
12.1.1 ML and Relationship with AI
12.1.2 Importance of Data in ML
12.1.3 Different Types of ML
12.1.4 Uses of Machine Learning with Examples
12.2 The Essential Building Blocks of ML
12.2.1 Data Preprocessing
12.2.2 Feature Engineering
12.2.3 Model Selection
12.2.4 Model Evaluation
12.2.5 Challenges
12.2.6 Limitations
12.3 Future Trends and Developments
Bibliography
13. Advanced Techniques for Water Quality Data Management Using Machine Learning
BVS Praveen, Raj Kumar Verma, M. Neha Sree and Y. Varsha
13.1 Introduction
13.2 Overview of Machine Learning
13.3 Advanced Machine Learning Techniques for Different Water Environments
13.3.1 Supervised Learning (RFs and SVMs)
13.3.1.1 Brief Theory of SVMs
13.3.1.2 Dataset for SVM
13.3.1.3 Data Processing for SVM
13.3.1.4 Modeling and Performance Criteria
13.3.1.5 Linear and Kernel Discriminant Analysis
13.3.1.6 Linear and Kernel Partial Least Squares Regression
13.3.1.7 Model Performance Criteria
13.3.1.8 Result
13.3.2 Time-Series Forecasting (Long Short-Term Memory Networks (LSTM))
13.3.2.1 LSTM for Water Quality Modeling
13.3.2.2 Water Quality Simulation of LSTM and Process-Driven Numerical Models
13.3.2.3 Performance of LSTM Compared with Other ML Models
13.3.2.4 LSTM Enhanced by Preprocessing
13.3.2.5 LSTM Combination with CNN
13.3.2.6 LSTM with Attention Mechanism
13.3.2.7 LSTM Combination with Transfer Learning (TL)
13.3.2.8 Influence of Data Transformation and Static Inputs on Model Performance
13.3.2.9 LSTM-Based Approaches for Water Quality Simulation
13.4 Challenges and Limitations on Water Quality in Machine Learning
13.5 Conclusions
References
14. Water Treatment Process Optimization Techniques
Prachiprava Pradhan and Ajit P. Rathod
14.1 Introduction
14.2 Optimization of Drinking Water Treatment Plant
14.2.1 Design of Wastewater Treatment Plants (WWTPs)
14.2.2 Approach and Model of Optimization
14.3 Water Treatment Process Optimization
14.3.1 Physical Processes
14.3.2 Chemical Processes
14.3.3 Biological Processes
14.4 Conclusion
References
15. Optimization of Biological Treatment Processes Through Machine Learning
for Remediating Wastewater
Aparna Ray Sarkar and Dwaipayan Sen
15.1 Introduction
15.2 Conventional Activated Sludge Treatment (CAS)
15.3 Sequencing Batch Reactor (SBR)
15.4 Integrated Fixed Film Activated Sludge (IFAS)
15.5 Moving Bed Media Bio Reactor (MBBR)
15.6 Membrane Bioreactor (MBR)
15.7 Machine Learning: A Tool to Explore Wastewater Remediation Process
15.7.1 Artificial Neural Network (ANN)
15.7.2 Adaptive Neuro-Fuzzy Inference Systems (ANFIS)
15.7.3 Grey Wolf Optimization (GWO)
15.8 Application of ML in Bioremediation of Wastewater and Parametric Optimization
15.9 Conclusion
References
16. Innovative Techniques for Enhancing Water Treatment Efficiency
B. Sumalatha, D. Syam Babu, B. Sudarsini and M. Indira
16.1 Introduction to Water Treatment Process and Optimization
16.2 Importance and Goals of Process Optimization
16.2.1 Overview of Key Concepts of Process Optimization
16.2.2 Improving Water Quality
16.2.3 Reducing Operational Costs
16.2.4 Meeting Regulatory Requirements
16.2.5 Environment Sustainability
16.2.6 Enhancing Process Efficiency
16.2.7 Ensuring Public Health and Safety
16.2.8 Economic Aid and Community Benefits
16.3 Overview of Water Treatment Process
16.3.1 Primary Treatment
16.3.2 Secondary Treatment
16.3.3 Tertiary Treatment
16.4 Performance Metrics and Evaluation Criteria
16.4.1 Define the Key Performance Indicators
16.4.2 Turbidity Removal
16.4.3 Chemical Dosage and Usage
16.4.4 Filtration Efficiency
16.4.5 BOD and COD
16.4.6 Sludge Production and Management
16.4.7 Water Recovery
16.4.8 Pathogen Inactivation
16.5 Advanced Optimization Techniques
16.5.1 Genetic Algorithms
16.5.2 Particle Swarm Optimization
16.5.3 Simulated Annealing
16.5.4 Ant Colony Optimization
16.5.5 Tabu Search
16.5.6 Swarm Intelligence
16.5.7 Machine Learning-Based Optimization
16.6 Optimization of Specific Treatment Processes
16.6.1 Optimizing Coagulation in Wastewater Treatment
16.6.2 Optimizing Flocculation in Wastewater Treatment
16.6.3 Optimizing Filtration in Wastewater Treatment
16.6.4 Optimizing Disinfection in Wastewater Treatment
16.7 Machine Learning Optimization Approaches
16.7.1 Reinforcement Learning (RL)
16.7.2 Deep Learning
16.7.3 Neural Networks
16.7.4 Supervised Learning Techniques
16.7.5 Unsupervised Learning Techniques
16.7.6 Hybrid Machine Learning Models
16.8 Challenges and Limitations
16.9 Future Directions and Innovations
16.10 Conclusions
References
17. Advancement in Machine Learning-Aided Advanced Oxidation Processes for Water Treatment
Prashant Kumar, Suparna Bhattacharyya and Biswajit Debnath
17.1 Introduction
17.2 Fundamentals of Advanced Oxidation Processes and Machine Learning
17.3 Machine Learning Applications in AOPs for Water Treatment
17.4 Case-Studies and Successful Implementations
17.5 Challenges and Future Directions
17.6 Conclusion
References
18. Machine Learning Strategies for Wastewater Treatment Toward Zero Liquid Discharge in a Lignocellulosic Biorefinery
P. Kalpana, S. Sharanya and P. Anand
18.1 Introduction
18.2 Processing of Biomass
18.2.1 Wastewater Characterization
18.2.2 Challenges in Traditional Wastewater Treatment Methods
18.3 Development of Models in Treatment Process
18.3.1 Supervised Learning Models
18.3.1.1 Regression Model
18.3.1.2 Support Vector Regression
18.3.1.3 Neural Networks
18.3.2 Unsupervised Learning Models
18.3.2.1 Clustering Algorithms
18.3.2.2 Dimensionality Reduction
18.3.3 Reinforcement Learning Models
18.3.3.1 Model-Free Reinforcement Learning
18.3.3.2 Model-Based Reinforcement Learning
18.3.3.3 Deep Reinforcement Learning
18.4 Implementation Steps for Machine Learning in ZLD
18.4.1 Problem Formulation and Data Collection
18.4.2 Data Preprocessing
18.4.3 Feature Selection and Dimensionality Reduction
18.4.4 Model Selection and Training
18.4.5 Model Evaluation and Optimization
18.4.6 Implementation and Integration
18.4.7 Documentation and Communication
18.5 Conclusion
Acknowledgements
References
19. Machine Learning Techniques in Water Treatment
Naveen Prasad B. S., Umareddy Meka, Rajasekaran R. and Saikat Banerjee
19.1 Introduction
19.1.1 Filtration
19.1.2 Sanitization
19.1.3 Key Challenges in Water Treatment
19.1.4 Energy Utilization
19.1.5 Expense
19.2 Overview of Machine Learning
19.3 Applications of ML in Water Treatment
19.3.1 Prediction of Water Quality Parameters
19.3.2 Anomaly Detection and Fault Diagnosis
19.3.3 Prognostic Maintenance of Water Treatment Apparatus
19.3.4 Real-Time Monitoring and Prediction of Water Quality
19.3.5 Adaptive Treatment Systems
19.3.6 Prediction of Water Quality in Distribution Networks
19.3.7 Forecasting Aquatic Water Quality
19.3.8 Employed ML Models for Water Quality Prediction
19.3.9 Remote Sensing and Monitoring: Utilizing Satellite Data or IoT-Enabled Sensors Combined with ML for Real-Time Monitoring
19.3.10 Remote Sensing
19.3.11 Sensors Enabled by IoT
19.3.12 Machine Learning
19.3.13 Agricultural and Crop Surveillance
19.3.14 Environmental Surveillance and Climate Alteration
19.4 Data Sources and Preprocessing for Water Treatment
19.4.1 Types of Data Used in Water Treatment
19.4.2 Types of Sensors Used in Water Control Plants
19.4.2.1 Sensors for Measuring Water Quality
19.4.2.2 Sensors for Monitoring Dynamic Fluid Processes
19.4.2.3 Types of Sensors Used to Monitor Water Pump Diagnostics
19.4.3 Water Quality Tests
19.4.3.1 Water Quality Tests for Physical Parameters
19.4.3.2 Water Quality Tests for Chemical Parameters
19.4.3.3 Biological Parameters of Water
19.4.4 Chemical Levels
19.4.5 Flow Rate Data
19.4.6 Data Cleaning and Preprocessing Methods
19.4.6.1 Handling Missing Data
19.4.6.2 Noise Reduction
19.4.6.3 Data Normalization and Standardization
19.4.6.4 Data Transformation
19.4.7 Importance of Feature Engineering for Water Treatment Systems
19.4.7.1 Improving Model Performance
19.4.7.2 Enhanced Decision-Making and Control
19.4.7.3 Facilitating Anomaly Detection
19.4.7.4 Domain-Specific Insights
19.4.7.5 Improving Interpretability
19.4.7.6 Handling Sensor and System Limitations
19.4.7.7 Adapting to Regulatory Requirements
19.4.7.8 Feature Engineering Examples in Water Treatment
19.4.7.9 Supporting ML Applications
19.5 Supervised Learning Techniques for Water Treatment
19.5.1 Regression Models
19.5.1.1 Linear Regression
19.5.1.2 Polynomial Regression
19.5.1.3 Ridge and Lasso Regression
19.5.1.4 Support Vector Regression
19.5.1.5 Artificial Neural Networks
19.5.1.6 Model Selection Criteria
19.5.2 Classification Models
19.5.2.1 Logistic Regression
19.5.2.2 Decision Trees
19.5.2.3 Random Forest
19.5.2.4 Support Vector Machines
19.5.2.5 Naive Bayes
19.5.2.6 Model Selection Criteria
19.5.3 Predicting Pollutant Concentrations Using Supervised Learning
19.5.4 Classifying Contamination Risks in Water Treatment
19.6 Unsupervised Learning Techniques
19.6.1 Clustering Techniques in Water Treatment
19.6.1.1 K-Means Clustering
19.6.1.2 Hierarchical Clustering
19.6.1.3 Density-Based Spatial Clustering of Applications with Noise
19.6.2 Dimensionality Reduction Techniques in Water Treatment
19.6.2.1 Principal Component Analysis
19.6.2.2 Independent Component Analysis
19.6.2.3 t-Distributed Stochastic Neighbor Embedding
19.6.3 Anomaly Detection in Water Treatment
19.6.3.1 Autoencoders for Anomaly Detection
19.6.3.2 Isolation Forest
19.6.4 Process Insights and Optimization
19.6.4.1 Clustering for Process Optimization
19.6.4.2 Dimensionality Reduction for Process Simplification
19.7 Deep Learning in Water Treatment
19.7.1 Overview of DL and NNs
19.7.2 Convolutional Neural Networks for Image-Based Water Monitoring (e.g., Algae Detection, Turbidity)
19.7.3 Recurrent Neural Networks for Time-Series Prediction of Water Quality or Consumption
19.8 Reinforcement Learning in Water Treatment
19.9 Case Studies and Real-World Applications
19.10 Challenges and Limitations of ML in Water Treatment
19.10.1 Data Scarcity and Data Quality Issues
19.10.2 Computational Challenges
19.10.3 Black-Box Nature and Lack of Interpretability
19.10.4 Regulatory Concerns and the Need for XAI in Critical Infrastructure
19.11 Future Trends and Research Directions
19.11.1 Role of AI and ML in the Future of Smart Water Treatment Plants
19.11.2 Integration of IoT, Cloud Computing, and Edge Computing with ML for Water Treatment
19.11.3 The Effects of AI-Driven Water Management on Society and Ethical Issues
19.11.4 Research Gaps and Future Work Needed in Applying ML for Water Sustainability
19.12 Conclusion
References
20. Bionanocomposites as Innovative Bioadsorbents for Wastewater Remediation: A Comprehensive Exploration
Rebika Baruah and Archana Moni Das
20.1 Introduction
20.2 Research Methods
20.2.1 Preparation of Cellulose-Supported Silver Bionanocomposites
20.2.2 Plant Extract–Mediated Synthesis of ZnO NPs
20.2.3 Characterization
20.3 Application of Bionanocomposites in the Wastewater Treatment
20.3.1 Results and Discussion
20.3.1.1 Photocatalytic Activity of AGC NCs
20.3.1.2 Photocatalytic Activity of ZnO NPs and Mechanism of Action
20.4 Conclusion
Acknowledgments
References
21. Utilizations of Machine Learning Algorithms in the Context of Biological Wastewater Treatment: Recent Developments and Future Prospects
Sonanki Keshri and Ujwala N. Patil
21.1 Introduction
21.2 Principles of Water Treatment Methods
21.2.1 Synopsis of Water Treatment Techniques
21.2.2 Physical Treatment Techniques
21.2.3 Chemical Treatment Techniques
21.2.4 Biological Treatment Techniques
21.3 Introduction to Machine Learning in Wastewater Treatment
21.3.1 Overview of MLAs
21.3.2 Importance of Machine Learning in Wastewater Treatment
21.3.3 Evolution of MLAs in Wastewater Treatment
21.4 ML in Wastewater Treatment
21.4.1 Decontamination by Chlorination Management of By-Products
21.4.2 Adsorption Processes
21.4.3 Membrane-Filtration Processes
21.5 Case Studies and Practical Applications
21.6 Applications in Water Quality Management
21.7 Challenges and Limitations
21.8 Future Prospects and Research Directions
21.9 Final Conclusions
References
22. A Comprehensive Review on Machine Learning Techniques for Wastewater
and Water Purification
Sonanki Keshri and Sudha S.
22.1 Introduction
22.2 Synopsis of Water Treatment Techniques
22.2.1 Physical Treatment
22.2.2 Chemical Treatment
22.2.3 Biological Treatment
22.3 Machine Learning Algorithms and their Application in Wastewater Treatment
22.3.1 Artificial Neural Network Models
22.3.2 Random Forest Model
22.3.3 Support Vector Machine Model
22.4 Wastewater Treatment Modeling Using ML
22.4.1 Chlorination and Disinfection By-Product Management
22.4.2 Adsorption Processes
22.4.3 Membrane Filtration Process
22.5 Application of ML in Water-Based Agriculture
22.6 Challenges with ML Implementation in Water Treatment and Monitoring
22.7 Recommendations for ML Implementation in Water Treatment and Monitoring
22.8 Conclusions
References
23. Water and Wastewater Treatment and Technological Remedies for Preserving Water Quality and Implementation of Machine Learning
Nishat Fatima and Prema P. M.
23.1 Introduction
23.2 Conventional Water and Wastewater Treatment Methods
23.2.1 Physical Treatment
23.2.2 Chemical Treatment
23.2.3 Biological Treatment
23.3 Technological Innovations for Water Quality Preservation
23.3.1 Nanotechnology in Water Treatment
23.3.1.1 Nanomaterials for Filtration
23.3.1.2 Removal of Heavy Metals and Pollutants
23.3.1.3 Disinfection and Pathogen Removal
23.3.1.4 Desalination
23.3.1.5 Sensors and Monitoring of Water Quality
23.3.1.6 Nanotechnology for Wastewater Treatment
23.3.1.7 Energy-Efficient Water Purification
23.3.2 Electrochemical Methods
23.3.3 Smart Sensors and Internet of Things Integration
23.3.4 Real-Time Water Quality Monitoring and Automated Control Systems
23.3.5 IoT for Enhanced Treatment Plant Management
23.4 ML in Water and Wastewater Treatment
23.4.1 Predictive Analytics and Process Optimization
23.4.1.1 Forecasting Contamination Levels and Treatment Efficiency
23.4.1.2 Optimization of Operational Parameters for Cost-Effectiveness
23.4.2 Fault Detection and Preventive Maintenance
23.4.3 Image Processing and Remote Sensing
23.5 Conclusion
References
24. Experimental Study on Wastewater Treatment and Reuse Using a Biofiltration System with Machine Learning-Based Optimization
Jayakaran Pachiyappan and Senthilnathan Nachiappan
24.1 Introduction
24.1.1 Background and Motivation
24.1.1.1 Growing Water Scarcity and the Need for Sustainable Water Management
24.1.1.2 Gray Water As a Potential Resource for Reuse in Nonpotable Applications
24.1.1.3 Wetland Adsorption Systems as a Cost-Effective and Eco-Friendly Treatment Method
24.1.1.4 Role of ML in Optimizing Treatment Processes
24.2 Objectives
24.3 Scope of the Chapter
24.4 Literature Review
24.5 Methodology
24.5.1 Experimental Setup
24.5.2 Data Collection
24.5.3 ML Integration
24.5.4 Optimization Framework
24.6 Results and Discussion
24.6.1 Performance of Wetland Adsorption System
24.6.2 ML Model Performance
24.6.3 Optimization Results
24.6.4 Insights and Implications
24.7 Conclusion
References
25. A Review on Machine Learning in Environmental Engineering: A Focus on the Gray Water Treatment
Vamsi Krishna Kudapa, Patchamatla J. Rama Raju, Arbind Ghataney and Nageswara Rao Lakkimsetty
25.1 Introduction
25.2 Gray Water Treatment by Using ML Techniques
25.2.1 Machine Learning Methods Overview
25.2.1.1 Training Artificial Neural Networks
25.2.1.2 Support Vector Machines
25.2.1.3 Random Forest
25.2.1.4 Fuzzy Logic and Hybrid Models
25.3 Usage of ML in Gray Water Treatment
25.3.1 Predictive Capability of the Contaminant Removal Efficiency
25.3.2 One-Step Process to Optimize Adsorption
25.3.3 Smart Monitoring and Management Systems
25.4 ANN-Based IoT Incorporation of Gray Water Treatment in Malaysia: A Case Study
25.5 Case Study 2: Implementation of RF Model in Gray Water Treatment
25.6 Challenges and Future Directions for ML-Based Gray Water Treatment
25.7 Conclusion
Bibliography
26. Machine Learning Techniques for Wastewater Treatment and Water Purification: Review of State-Of-The-Art Practices and Applications
Swarnadeep Saha, Protyasha Kundu, Sumanta Banerjee and Anindita Kundu
26.1 Introduction
26.2 Literature Survey
26.2.1 Chlorination and Disinfection in Water Treatment Process
26.2.2 Adsorption Processes
26.2.3 Membrane Filtration Process
26.3 ML Models
26.3.1 Decision Trees
26.3.2 RF Classifier
26.3.3 Support Vector Machines
26.3.4 Artificial Neural Networks
26.3.5 XGBoost
26.4 Case Study I: Prediction of Water Quality Index Using ElasticNet
26.4.1 Overview of ElasticNet
26.4.2 Experimental Process and Results
26.5 Case Study II: Prediction of Water Potability Using Extra Trees Classifier
26.5.1 Overview of Extra Trees Classifier
26.5.2 Experimental Process and Results
26.6 Conclusion
References
27. Application of Predictive Modeling Approaches for Water Quality Prediction
Ritam Das, Jumasri Ganguly, Saubhagya Mukherjee, Ivy Ray, Raj Kumar Arya and Pramita Sen
27.1 Introduction
27.1.1 Parameters Indicating WQ Degradation
27.1.2 Water Quality Management Organizations
27.2 Water Quality Measurement Parameters
27.2.1 Physical Parameters
27.2.2 Chemical Parameters
27.2.3 Microbiological Parameters
27.3 Overview of Predictive Modeling and Its Significance in WQ Prediction
27.3.1 Types of Learning for Predictive Modeling
27.3.1.1 Supervised Learning
27.3.1.2 Unsupervised Learning
27.3.1.3 Reinforced Learning
27.3.2 Significance of Predictive Modeling in WQ Prediction
27.4 Brief Discussion on ML Models
27.4.1 Classification and Regression Models
27.4.1.1 Support Vector Regressor
27.4.1.2 Artificial Neural Network (ANN)
27.4.1.3 K-Nearest Neighbors
27.4.1.4 Decision Tree
27.4.2 Ensemble Models
27.4.2.1 Random Forest
27.4.2.2 Gradient Boosting
27.4.2.3 Bagging
27.4.2.4 Deep Cascade Forest
27.5 Steps of ML Algorithms in WQ Prediction
27.6 Comparing Model Predictions with Experimental Results
27.7 Challenges and Future Perspectives
27.7.1 Nature of Data
27.7.2 Model Complexity and Overfitting
27.7.3 Environmental Aspects
References
28. Next-Generation Water Purification: Harnessing Machine Learning for Optimal Treatment and Monitoring
Rompicherla Srividya, A.V. Raghavendra Rao, Boppena Karuna, Kolluru Sree Manaswini and Sravani Sameera Vanjarana
28.1 Introduction to Machine Learning Techniques
28.1.1 Overview of Machine Learning in Water and Wastewater Purification
28.1.2 Importance and Benefits of ML in Environmental Engineering
28.2 Supervised Learning Techniques
28.2.1 Classification Algorithms
28.2.2 Regression Algorithms
28.3 Unsupervised Learning Techniques
28.3.1 Introduction
28.3.2 Clustering Algorithms
28.3.3 Dimensionality Reduction
28.3.4 Anomaly Detection
28.4 Reinforcement Learning Techniques
28.4.1 Introduction to Reinforcement Learning
28.4.2 Core Concepts in RL
28.4.3 RL Algorithms
28.5 Hybrid and Ensemble Techniques
28.5.1 Hybrid Techniques
28.5.2 Ensemble Techniques
28.6 Deep Learning Techniques
28.6.1 Introduction to Deep Learning
28.6.2 Convolutional Neural Networks
28.6.3 Recurrent Neural Networks and Long Short-Term Memory Networks
28.7 Emerging Techniques and Future Directions
References
29. Revolutionizing Water Treatment Facilities with Machine Learning: Techniques, Applications, and Case Studies
A.V. Raghavendra Rao, Rompicherla Srividya, Sravani Sameera Vanjarana, B. Karuna and Archana Rao P.
29.1 Introduction
29.2 ML Techniques in Water Treatment
29.2.1 ML Techniques
29.2.1.1 Supervised Learning
29.2.1.2 Unsupervised Learning
29.2.1.3 Reinforcement Learning
29.2.1.4 Advanced ML Techniques
29.2.2 ML Techniques in Water Treatment
29.3 Applications of ML in Water Treatment
29.3.1 Contaminant Level Prediction and Treatment Optimization
29.3.2 Treatment Optimization
29.3.3 Pattern Recognition and Data Structure Analysis
29.3.4 Control Strategy Optimization
29.4 Case Studies
29.4.1 Forecasting Municipal Water Demand: Calgary, Alberta, Canada
29.4.1.1 Background and Objective
29.4.1.2 Methodology
29.4.1.3 Results and Findings
29.4.1.4 Conclusion
29.4.2 Remote Sensing and Water Quality Estimation in Hong Kong
29.4.2.1 Background and Objectives
29.4.2.2 Methodology
29.4.2.3 Results and Findings
29.4.2.4 Conclusion
29.4.3 Groundwater Nitrate Pollution Using Quantile Regression and Uncertainty Estimation and Error Calibration Methods
29.4.3.1 Background and Objectives
29.4.3.2 Methodology
29.4.3.3 Results and Findings
29.4.3.4 Conclusion
29.4.4 Water Quality Index Estimation in Yazd-Ardakan Plain, Iran
29.4.4.1 Background and Objectives
29.4.4.2 Methodology
29.4.4.3 Results and Findings
29.4.4.4 Conclusion
29.5 Challenges and Opportunities
29.5.1 Challenges
29.5.1.1 Customization to Local Conditions
29.5.1.2 Model Interpretability
29.5.1.3 Data Quality Enhancement
29.5.2 Opportunities
29.5.2.1 Ensuring Input Data Veracity and Dependability
29.6 Prospective Developments in ML for Water Treatment Facilities
29.6.1 Improving Model Robustness
29.6.1.1 Advanced Algorithms and Hybrid Models
29.6.1.2 Incorporating Ensemble Learning
29.6.1.3 Handling Missing and Noisy Data
29.6.1.4 Real-Time Data Processing and Edge Computing
29.6.1.5 Continuous Learning and Adaptation
29.6.2 Enhancing Regulatory Compliance
29.6.2.1 Transparent and Interpretable Models
29.6.2.2 Incorporating Domain Expertise
29.6.2.3 Ensuring Data Quality and Integrity
29.6.2.4 Compliance with Data Privacy Regulations
29.6.2.5 Automated Reporting and Documentation
29.6.3 Promoting Interdisciplinary Collaboration
29.6.3.1 Integrating Multidisciplinary Knowledge
29.6.3.2 Collaborative Research and Development
29.6.3.3 Education and Training Programs
29.6.3.4 Developing User-Friendly Tools and Interfaces
29.6.3.5 Policy Advocacy and Regulatory Frameworks
29.7 Conclusion
References
30. Advanced Techniques for Water Treatment Process Optimization
V. Sravani Sameera, Rompicherla Srividya, Anup Ashok, KSNV Prasad, Boppena Karuna, Ganesh Botla and A.V. Raghavendra Rao
30.1 Introduction
30.2 ML Techniques for Optimization
30.2.1 Supervised Learning
30.2.2 Unsupervised Learning
30.2.3 Reinforcement Learning
30.2.4 Deep Learning
30.3 Integration of ML Models with Real-Time Monitoring
30.3.1 Use of Sensors and IoT Devices
30.3.1.1 Sensor Types and Functions
30.3.1.2 Role of IoT in Data Transmission and Communication
30.3.1.3 Integration into Water Treatment Systems
30.3.2 Real-Time Data Processing and Analysis
30.3.2.1 Data Processing Pipeline
30.3.2.2 ML Models for Real-Time Analysis
30.3.2.3 Integration with Control Systems
30.3.3 Case Studies on Dynamic Optimization
30.4 Challenges and Limitations
30.4.1 Data Quality and Availability
30.4.1.1 Data Scarcity
30.4.1.2 Data Quality
30.4.1.3 Data Accessibility
30.4.2 Model Interpretability and Transparency
30.4.2.1 Complexity of Models
30.4.2.2 Lack of Transparency
30.4.2.3 Stakeholder Trust
30.4.2.4 Regulatory Compliance
30.4.3 Need for Domain Expertise
30.4.3.1 Complexity of Water Treatment
30.4.3.2 Interpreting Technical Data
30.4.3.3 Model Validation and Tuning
30.4.3.4 Integration with Operational Knowledge
30.4.4 Ethical Considerations and Data Privacy
30.4.4.1 Bias in Data
30.4.4.2 Transparency and Accountability
30.4.4.3 User Awareness
30.4.4.4 Environmental Impact
30.5 Hybrid Optimization Models
30.5.1 Combining ML with Traditional Methods
30.5.2 Case Studies on Hybrid Approaches
30.5.2.1 Case Study
30.6 Economic and Environmental Impacts
30.6.1 Economic Impacts
30.6.1.1 Cost Savings in Operations
30.6.1.2 Reduction in Maintenance Costs
30.6.1.3 Optimization of Labor Costs
30.6.1.4 Economic Benefits of Improved Water Quality
30.6.2 Environmental Impacts
30.6.2.1 Reduction in Chemical Usage
30.6.2.2 Energy Efficiency and Reduced Carbon Footprint
30.6.2.3 Waste Minimization
30.6.2.4 Protection of Natural Water Bodies
30.6.3 Case Studies Highlighting Economic and Environmental Impacts
30.6.3.1 Case Study 1: Calgary, Alberta, Canada
30.6.3.2 Case Study 2: Hong Kong Coastal Waters
30.6.4 Challenges and Considerations
30.6.4.1 Data Quality and Availability
30.6.4.2 Model Interpretability
30.6.4.3 Customization to Local Conditions
30.6.4.4 Interdisciplinary Collaboration
30.7 Future Trends and Advancements
30.7.1 Big Data Analytics for Water Treatment
30.7.1.1 Data Collection and Integration
30.7.1.2 Real-Time Monitoring and Predictive Analytics
30.7.1.3 Advanced Data Analysis Techniques
30.7.1.4 Decision-Support Systems
30.7.2 The Creation of Sophisticated Predictive Models
30.7.2.1 Machine and Deep Learning Models
30.7.2.2 Hybrid and Ensemble Models
30.7.2.3 Model Validation and Calibration
30.7.2.4 Real-Time and Adaptive Modeling
30.7.2.5 Case Studies and Applications
30.7.3 Integrating AI with Water Infrastructure
30.7.3.1 AI-Powered Smart Water Networks
30.7.3.2 Predictive Maintenance
30.7.3.3 AI Automates Process Control in Water Treatment Facilities
30.7.3.4 AI-Powered Water Quality Monitoring Systems
30.7.3.5 AI-Driven DSSs
30.7.3.6 Case Studies and Applications
30.7.3.7 Future Directions
30.8 Conclusions
Bibliography
31. Regression Models for Prediction and Evaluation of Water Contamination: A Comparative Study
Vamsi Krishna Kudapa, Santhosh Chanemougam, Salman Ahmad and Nageswara Rao Lakkimsetty
31.1 Introduction
31.2 Regression Models for Water Quality Prediction
31.2.1 Linear Regression
31.2.2 Polynomial Regression
31.2.3 Ridge and Lasso Regression
31.2.4 Support Vector Regression
31.3 Case Studies on Predictive Water Contamination via Regression
31.3.1 Case Study 1. MLR-Based Predictions of Nitrate Contamination in Groundwater
31.3.2 Case Study 2: Prediction of Heavy Metal Contamination in River Water Based on Polynomial Regression
31.4 Performance Evaluation Comparison for Different Models
31.5 Conclusion
Bibliography
32. Implications of Regression Analysis for Predicting Water Contamination
Levels
Nirlipta Priyadarshini Nayak and Rahul Kumar Singh
32.1 Introduction
32.2 Regression Analysis for Water Quality Prediction
32.3 Existing Regression Analysis Model
32.4 Conclusion
References
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