Applied Machine Learning in Chemical Process Engineering: A Practical Approach

Zafar Said · Muhammad Farooq

Jan 2026 · Elsevier

Ebook

350

Pages

Eligible

This book will become available on January 1, 2026. You will not be charged until it is released.

About this ebook

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 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 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

About the author

Dr. Zafar Said is an Associate Professor with the Department of Sustainable Renewable Energy Engineering, University of Sharjah, UAE. He also serves as coordinator of the Functional Nanomaterials Synthesis Lab. Dr. Said completed his Ph.D. from the University of Malaya, Malaysia, and worked as a postdoctoral researcher at the Masdar Institute, UAE, where he has also worked on industrial collaborative projects. Dr. Said works on renewable energy, energy and exergy analysis, solar energy, heat transfer, and nanofluids. He has published over 180 papers, 2 books, 20 book chapters, and 26 conference papers, with more than 15,000 citations, and was also ranked in the World's Top 2% Scientists in 2022, 2021 and 2020 by Elsevier and Stanford University in the field of Energy. He is ranked in the top 100 scientists in the United Arab Emirates and has secured more than 2 million AED in research grants. He has been honoured with several prestigious awards and is also serving as Editorial Board Member for several ISI Journals, as well as Guest Editor for several special issues.

Muhammad Farooq is Associate Professor at the Sultan Qaboos University, Muscat, Oman. He also holds the positions of Associate Professor at the University of Agriculture, Faisalabad, Pakistan, Adjunct Associate Professor at the University of Western Australia, Adjunct Professor at the Dankook University, Korea. He was also Young Affiliate fellow of The World Academy of Sciences (2015-2019) and is member of Global Young Academy and Pakistan Academy of Sciences. He received ‘Best Young Research Scholar Award’ from the Higher Education Commission of Pakistan (2013 and 2014). He was honored with the COMSTECH Award for Excellence in Research (2016) by Organization of Islamic Conference and Gold Medal (2017) by Pakistan Academy of Sciences. He received the Best University Teacher Award (2018) from the Higher Education Commission of Pakistan and was named as Highly Cited Researcher by the Web of Science (2018 and 2019). He received the distinguished researcher award (2020) from the Sultan Qaboos University. He has edited and co-edited eight books, and authored and co-authored more than 380 research articles and 49 book chapters. His citations, on google scholar, exceed 22,000 with h-index of 72.

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