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Keywords

Artificial Intelligence in Wastewater, Machine Learning ,Smart Water Treatment,
Real-Time Monitoring,Process Automation,Predictive Analytics, Wastewater Modelling,, Neural Networks, Fuzzy Logic Systems, Internet of Things (IoT),
SCADA Integration, Decision Support Systems,Optimization Algorithms,
Energy-Efficient Treatment, Sludge Valorization, Circular Economy,
Smart Sensors, Water Reuse Optimization, Explainable AI, Sustainable Wastewater Management

ARTIFICIAL INTELLIGENCE IN WASTEWATER TREATMENT : INNOVATIONS FOR SUSTAINABLE ENVIRONMENTAL MANAGEMENT - Volume 01

Edited By Manoj Chandra Garg, Pinki Sharma, Smriti Agarwal, Monika Simon

Browse All Volumes of Innovations For Sustainable Environment and Societial Management
Language: English | Imprint: NIPA

Hardback

ISBN: 9789372195835
Pages: 300 | Length: 152 mm | Breadth: 17 mm | Height: 229 mm | Weight: 675 GSM
Print Book Price: 150.00 USD (Get 10% OFF)
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Free Worldwide Delivery Within 10-15 Days By Indian Post (Traceable Methods)

EBook

EISBN: 9789372199130
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eChapter

eChapter price starts from: USD135.00

This book provides a forward-looking and practical examination of how Artificial Intelligence (AI) is reshaping wastewater treatment and advancing sustainable environmental management. Organized into four thematic parts, it bridges foundational knowledge with cutting-edge innovations, offering an integrated view of the future of water technologies.

The book begins with the fundamentals of AI and machine learning as applied to core wastewater processes. Readers learn how intelligent algorithms—ranging from neural networks to optimization models—enable accurate forecasting, dynamic control, and enhanced biological treatment efficiency. The second part highlights AI-driven monitoring and automation, including smart sensors, SCADA-enabled systems, and real-time process adjustments that improve operational stability and regulatory compliance.

Decision-support tools and emerging technologies form the focus of Part III. This section explores multi-criteria evaluation frameworks, IoT-connected smart plants, predictive analytics, and next-generation advancements such as explainable AI. The final part addresses sustainability and specialized applications, detailing AI-driven strategies for sludge valorization, zero-waste initiatives, high-salinity wastewater treatment, and optimized water reuse.

Combining scientific depth with practical insights, this book serves as an essential resource for researchers, engineers, utility managers, environmental consultants, and policymakers committed to building smarter, more resilient, and sustainable wastewater management systems

1 Artificial Intelligence in Wastewater Treatment

1 - 300 (300 Pages)
USD150.00 USD135.00
 

Part I: AI and Machine Learning in Core Wastewater Treatment Processes
Chapter 1: Machine Learning Applications in Wastewater Treatment
Chapter 2: Optimization of Biological Treatment Processes Using AI
Chapter 3: AI for Energy Efficiency in Wastewater Treatment
Chapter 4: AI-Powered Wastewater Treatment System Design and Optimization
Part II: AI-Enabled Monitoring, Control, and Automation
Chapter 5: Artificial Intelligence for Wastewater Quality Monitoring, Process Control, and Automation
Chapter 6: Smart Wastewater Systems: Leveraging AI for Quality Monitoring and Assessment
Chapter 7: AI-Driven Process Control and Automation”
Part III: Decision Support, Evaluation, and Emerging Technologies
Chapter 8: AI-Based Decision Support Systems for Wastewater Management
Chapter 9: Future Perspectives and Emerging Trends in AI for Wastewater Treatment
Chapter 10: AI-Enabled Fuzzy Multi-Criteria Approach for Evaluating Wastewater Treatment Technologies
Chapter 11: AI and Internet of Things (IoT) Integration in Smart Wastewater Systems
Part IV: Sustainability and Specialized Applications
Chapter 12: AI for Sludge Management and Byproduct Utilization
Chapter 13: Harnessing Artificial Intelligence for Sustainable Management of Saline Wastewater in Rajasthan's Saline Lakes
Chapter 14: Wastewater Reuse Optimization Using Machine Learning

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