About the Book
This book addresses a critical gap in the current literature by offering a comprehensive and application-focused resource that connects core machine learning (ML) techniques with real-world chemical engineering challenges, including process optimization, control, safety, and environmental sustainability. The motivation behind this book stems from the rapid advancements in data availability (e.g., from IoT, process sensors, and lab systems) and the growing need to extract actionable insights from this data. Traditional modeling approaches, often based on first-principles, can be limited in flexibility and scale. ML provides a complementary paradigm capable of learning patterns, predicting outcomes, and supporting intelligent decision-making in complex, nonlinear systems, making it particularly suitable for modern chemical and environmental process systems. The book aims to equip researchers, practitioners, and graduate students with both theoretical foundations and practical insights into how ML is transforming chemical engineering. The book systematically explores supervised, unsupervised, deep, and reinforcement learning methods, as well as their applications across various domains, including process control, quality assurance, fault detection, environmental monitoring, and sustainability. Notably, dedicated chapters cover environmental chemical engineering topics, including wastewater treatment, air pollution control, and circular economy applications, where ML is increasingly essential. The scope spans introductory to advanced levels, assuming a basic understanding of chemical engineering and statistics. Mathematical formulations are provided where relevant, but the emphasis is on conceptual clarity, interpretability, and industrial relevance. Each chapter includes illustrative case studies, visualizations, and references to real datasets or tools. This book adopts a novel interdisciplinary approach by integrating environmental engineering, digital twin technology, and ethical AI considerations within the context of process industries. A unique strength lies in its balanced coverage of academic and industrial perspectives, bridging the gap between theory and implementation.
About the Author :
Prof. Dr. Shaik Feroz is currently working at Prince Mohammed Bin Fahd University, Kingdom of Saudi Arabia. Dr. Feroz obtained his doctorate in the field of chemical engineering from Andhra University, India, in 2004 and Post Doc Research Fellow from Leibniz University, Germany, in 2015; M.Tech. in chemical engineering from Osmania University, India, in 1998; B.Tech. in chemical engineering from S. V. University, India, in 1992; and post graduate diploma in environmental studies from Andhra University in 2003. Dr. Feroz Shaik is Visiting Professor for School of Renewable Energy, Maejo University, Thailand. Dr. Feroz has expertise in process engineering, plant design and troubleshooting, quality control using advance analytical equipment, wastewater treatment, solar energy systems (PV & CSP) for energy and desalination, hot water systems and water treatment, synthesis of nano photo catalysts, simultaneous treatment of wastewater and production of hydrogen, and environmental impact assessment. Dr. Sani I. Abba is Assistant Researcher Professor in the Department of Civil Engineering at Prince Mohammad Bin Fahd University (PMU), KSA. He holds a B.Sc. degree from Bayero University Kano (BUK), an M.Tech. degree from Sharda University, India, and a Ph.D. from Near East University (NEU), Cyprus. Dr. Abba has over a decade of experience working with Yusuf Maitama Sule University, Baze University, Nigeria, and King Fahd University of Petroleum and Minerals (KFUPM), Saudi Arabia. He has extensive experience in teaching, research, and curriculum development at graduate and undergraduate levels. His research interests span artificial intelligence, water footprint, water security, groundwater, membrane desalination, wastewater, water quality, water resources, public health, pollution control, climate change, sustainable development, hydroclimatology, hydroenvironmental modeling, computational engineering, soft computing, and optimization algorithms. Dr. Jamal F. Nayfeh is Dean of the College of Engineering and Professor of mechanical engineering at Prince Mohammad Bin Fahd University (PMU) since September 2009. Previously, he was Associate Dean for academics, marketing, and outreach in the College of Engineering and Computer Science and Professor of mechanical engineering in the Department of Mechanical, Materials, and Aerospace Engineering (MMAE) at the University of Central Florida (UCF). He received his Ph.D. in engineering mechanics from Virginia Tech in 1990. Dr. Nayfeh is Member of Tau Beta Pi Engineering Honor Society, American Society of Mechanical Engineers, American Institute of Aeronautics and Astronautics, Society of Automotive Engineers, and American Society for Engineering Education.
