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Full Description
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.
Contents
1. Introduction to Machine Learning in Chemical and Process Engineering.- 2. Data Acquisition and Preprocessing in Industrial Systems.- 3. Supervised Learning in Process Optimization and Quality Control.- 4. Unsupervised Learning for Process Understanding.- 5. Deep Learning for Complex Process Systems.- 6. Reinforcement Learning for Process Control.- 7. ML Applications in Environmental Chemical Engineering.- 8. Wastewater Treatment and Water Quality Prediction using Machine Learning.- 9. Machine Learning for Air Pollution and Emissions Control.- 10. Circular Economy and Sustainable Process Design.- 11. Digital Twins and Predictive Maintenance in Process Plants.- 12. Safety, Risk Assessment, and Ethical Considerations in ML Deployment.
