Applied Machine Learning in Chemical Process Engineering

A Practical Approach

Zafar Said, Muhammad Farooq

• Publisher: Elsevier
• Year: 2026
• Topic: Technology & Engineering
• eBook ISBN:
  9780443339448
• Subtopic: Business Intelligence
• Index: Technology & Engineering

As machine learning capabilities and functionality increases, more industry experts and researchers are integrating applied machine learning into their research. Applied Machine Learning in Chemical Process Engineering: A Practical Approach serves as a comprehensive guide to equip the reader with the fundamental theory, practical guidance, methodologies, experimental design and troubleshooting knowledge needed to integrate machine learning into their processes. This book offers a comprehensive overview of all aspects of machine learning, from inception to integration that will allow readers from any scientific discipline to begin to examine the capabilities of machine learning. This book will then build upon this overview to offer worked examples and case studies, alongside practical methods-based guidance to walk the reader through integrating machine learning end-to-end. Finally, this book will offer critical discussion of concepts that are interwoven into the ever-evolving principles of machine learning such as ethics, safety and culpability that are crucial when working with machine learning. Applied Machine Learning in Chemical Process Engineering: A Practical Approach will be an invaluable resource for researchers, professionals in industry and academia, and students at graduate level and above who work in chemical engineering and are looking to automate, optimize or intensify their chemical processes. This book will also help professionals in other disciplines and industries looking into integrate machine learning into their work, such as though looking to scale up their processes to an industrial scale or conduct novel research. - Provides an integrated view of chemical and process engineering basics and machine learning - Provides a complete reference on machine learning foundations and chemical and process engineering applications - Includes real-world worked examples and case studies to show how machine learning techniques are applied in process design, optimization, and control - Evaluates the difficulties, ethical implications, and prospects of chemical industry machine learning integration - Provides troubleshooting and solutions to common problems associated with data collecting, preprocessing, and model deployment in live operations

Table of contents

• Title of Book
• Chapter 1 Introduction to Machine Learning for chemical engineers
• Chapter 2 Data handling and preprocessing in chemical datasets
• Chapter 3 Predictive modeling for chemical processes
• Chapter 4 Partial derivative based sensitivity analysis of NARX model for time-series applications of chemical processes
• Chapter 5 Process optimization and control using Machine Learning
• Chapter 6 Molecular simulations and deep learning
• Chapter 7 Reinforcement learning in process design
• Chapter 8 Challenges and ethical considerations in implementing Machine Learning
• Chapter 9 Case studies: Breakthroughs at the intersection of ML and chemical engineering
• Chapter 10 Physics-informed neural networks in chemical engineering
• Chapter 11 Explainable Artificial Intelligence and sustainable computing in Machine Learning
• Chapter 12 Future of Artificial Intelligence in chemical and process engineering
• Index