Next-Generation Water Networks

Optimization, Control, Uncertainty, and AI
Erscheinungsjahr 2027
ISBN: 978-981-9268-89-4
Verlag: Springer

Buch, Englisch, 372 Seiten, Format (B × H): 155 mm x 235 mm

Reihe: Modeling and Optimization in Science and Technologies

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.

Zielgruppe

Research

Autoren/Hrsg.

Mitra, Kishalay

Karimi, Iftekhar A.

Samavedham, Lakshminarayanan

Banerjee, Raja

Fachgebiete

• Technische Wissenschaften Bauingenieurwesen Konstruktiver Ingenieurbau, Baustatik
• Technische Wissenschaften Umwelttechnik | Umwelttechnologie Umwelttechnik

Inhaltsverzeichnis

Chapter 1. Introduction to Water Network Optimization.- Chapter 2. Smart Water Infrastructure: Systems Engineering Perspectives and Opportunities.- Chapter 3. Pragmatic Water Network Optimization: From Theory to Real-World Solutions.- Chapter 4. Evolution of Control Strategies in Water Network Optimization.- Chapter 5. Multi-Stage Water Network Optimization for Petroleum Refinery: A Mathematical Programming Model.- Chapter 6. Surrogate-Based Optimization in Water Networks.- Chapter 7. Active Topology Identification in Water Distribution Networks: Leveraging Graph Neural Networks to Overcome Observability Limits in Dense Grids.- Chapter 8. Deep Learning for Spray Pattern Recognition in Water Distribution and Control Systems.- Etc...

Über Autor(innen)

Kishalay Mitra is Professor of Chemical Engineering at IIT Hyderabad, with associated faculty roles in the departments of artificial intelligence, climate change, and the Greenko School of Sustainability. Prior to joining academia, he spent nearly 15 years in industrial R&D at General Electric Global Research, Bengaluru, and Tata Research Development and Design Centre, Pune. His research focuses on artificial intelligence, machine learning, optimization, uncertainty quantification, and process systems engineering, with applications in sustainable energy, climate change, bioprocessing, advanced materials, and sustainable supply chains. He has led nationally significant research projects funded by DST, DBT, MoE, DRDO, Coal India Limited, and Tata Steel. He has held visiting appointments at Washington University in St. Louis and the University of Washington, Seattle. He has contributed to developing interdisciplinary academic programs integrating AI/ML with chemical engineering. Prof. Mitra has also served on several expert and technical committees of leading national funding and research organizations.

Iftekhar A Karimi is Professor of chemical and biomolecular engineering at the National University of Singapore. He is Leading Expert in the area of process systems engineering with a unique blend of experience from academia and industry. His current research interests include energy transition, decarbonization, hydrogen, ammonia, energy efficiency, machine learning, and LNG. He has worked on several practical topics related to the design, simulation, and optimization of chemical, biological, and environmental systems. He has led several industry-collaborative research projects during his career. He is Professional Engineer and serves on the Technical Energy Committee and EENP Service Awards Committee of Institute of Engineers Singapore.

Lakshminarayanan Samavedham is Associate Professor with the Department of Chemical and Biomolecular Engineering, National University of Singapore. In the period between 2008 and 2014, he was involved in a large international research project on the modeling and control of water infrastructure systems in Singapore under the Singapore-Delft Water Alliance (SDWA) where he and his research team made contributions to the field of distributed model predictive control of water reservoirs. A winner of the topmost teaching award of NUS (2006) and Public Administration Medal (Silver) (2020) from the Government of Singapore for his contributions to higher education, Laksh has also been Director of the NUS Centre for Development of Teaching and Learning, Inaugural Master of a NUS Residential College that was themed on Systems Thinking and System Dynamics Modeling, and Director of the NUS Applied Learning Sciences and Educational Technology Research Institute.?

Raja Banerjee is Professor in the Department of Mechanical and Aerospace Engineering at IIT Hyderabad, also affiliated with the Greenko School of Sustainability. With over twenty-five years of combined academic and industry experience, he works at the intersection of spray and atomization, turbulent spray combustion, multiphase flows, and high-performance computational modeling. His expertise spans GPU parallelized CFD and DEM solvers, and he has developed several in house codes, including a Coupled Level Set–VOF solver, a two-phase Lattice Boltzmann solver, and GPU accelerated multiphase solvers. He has co-authored nearly 100 research articles, holds two patents, and is Fellow of the International Society for Energy, Environment, and Sustainability. He has been involved in the National Supercomputing initiative in India that includes commissioning Param Seva, the 800 TFlop supercomputing facility at IIT Hyderabad. He also played a foundational role in establishing the Centre of Computational Engineering.