Next-Generation Water Networks
Optimization, Control, Uncertainty, and AI
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Beschreibung
This book presents a comprehensive and forward-looking treatment of water network optimization, positioning water systems as complex cyber–physical infrastructures that must be designed and operated under increasing demands for efficiency, resilience, safety, and sustainability. It brings together optimization theory, control engineering, uncertainty quantification, and artificial intelligence/machine learning (AI/ML) into a unified framework tailored specifically for water distribution, industrial water networks, and wastewater systems.
Of particular interest to readers is the progressive methodological spectrum covered in the book. Beginning with deterministic and classical optimization methods, the book advances through surrogate-based modeling, graph neural networks, stochastic and robust optimization, and modern ML-enabled decision-making under uncertainty. Special emphasis is placed on learning-enabled control, physics-informed AI, and hybrid modeling approaches that integrate hydraulic principles with data-driven intelligence. Topics such as flow prediction, spray characterization, wastewater control, circular water systems, and process safety are treated with a strong focus on real-world applicability.
What distinguishes this book is its pragmatic and didactic approach. Each major concept is motivated by real engineering challenges and illustrated through conceptual diagrams, workflow schematics, comparison tables, and targeted case studies. The book introduces graph-based representations of water networks, simple depictions of uncertainty propagation, and structured matrices to compare methods, assumptions, and applicability. This visual and structured presentation lowers the barrier for readers transitioning from theory to implementation.
The main benefit to the reader is a clear roadmap for designing, optimizing, and controlling next-generation water networks using both classical and AI-driven tools. Researchers gain a coherent research landscape and open problems, while practitioners and graduate students obtain actionable methodologies that can be directly translated to real systems. Ultimately, the book equips readers to develop intelligent, resilient, and sustainable water networks in an era of growing complexity and uncertainty.
This book presents a comprehensive and forward-looking treatment of water network optimization, positioning water systems as complex cyber–physical infrastructures that must be designed and operated under increasing demands for efficiency, resilience, safety, and sustainability. It brings together optimization theory, control engineering, uncertainty quantification, and artificial intelligence/machine learning (AI/ML) into a unified framework tailored specifically for water distribution, industrial water networks, and wastewater systems.
Of particular interest to readers is the progressive methodological spectrum covered in the book. Beginning with deterministic and classical optimization methods, the book advances through surrogate-based modeling, graph neural networks, stochastic and robust optimization, and modern ML-enabled decision-making under uncertainty. Special emphasis is placed on learning-enabled control, physics-informed AI, and hybrid modeling approaches that integrate hydraulic principles with data-driven intelligence. Topics such as flow prediction, spray characterization, wastewater control, circular water systems, and process safety are treated with a strong focus on real-world applicability.
What distinguishes this book is its pragmatic and didactic approach. Each major concept is motivated by real engineering challenges and illustrated through conceptual diagrams, workflow schematics, comparison tables, and targeted case studies. The book introduces graph-based representations of water networks, simple depictions of uncertainty propagation, and structured matrices to compare methods, assumptions, and applicability. This visual and structured presentation lowers the barrier for readers transitioning from theory to implementation.
The main benefit to the reader is a clear roadmap for designing, optimizing, and controlling next-generation water networks using both classical and AI-driven tools. Researchers gain a coherent research landscape and open problems, while practitioners and graduate students obtain actionable methodologies that can be directly translated to real systems. Ultimately, the book equips readers to develop intelligent, resilient, and sustainable water networks in an era of growing complexity and uncertainty.
Presents comprehensive coverage spanning classical stochastic and robust optimization to ML-based uncertainty modeling Includes state-of-the-art AI and machine learning methodologies, tailored to water networks Provides an emphasis on real-world applicability bridging rigorous theory with practical implementation in water systems
Autor*in
Kishalay Mitra
Themen in »Next-Generation Water Networks«
Water Network Optimization Intelligent Water Systems Optimization and Control Artificial Intelligence and Machine Learning Uncertainty Quantification Sustainable and Circular Water Systems