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: 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

About the author

Dr. Zafar Said is currently working as a Distinguished Associate Professor in theDepartment of Mechanical and Aerospace Engineering at the United Arab EmiratesUniversity, UAE. He received his doctoral degree in Mechanical Engineering fromthe University of Malaya, Malaysia, and completed his postdoctoral research atKhalifa University, UAE. Dr. Said is a recognized leader in energy technology,nanofluids, and sustainable energy. His major areas of interest include heattransfer, solar energy systems, and advanced thermofluids. His research focuses onbattery thermal management, enhancement of solar collectors using nanofluidsand turbulators, and the development of stable nanorefrigerants andnanolubricants. He also applies artificial intelligence and machine learning topredict thermophysical properties and optimize energy systems. He is the recipientof several prestigious awards, including the Khalifa Award for Education asDistinguished University Professor (2025), the Future Pioneer Award inSustainability (2025), and Best Academic Research at the 13th Dubai Award forSustainable Transport (2024). He has also received the Research and InnovationAward from the UAE Ministry of Energy and Infrastructure (2022) and First Place inScientific Research at the Excellence and Creative Engineering Award (2023) by theSociety of Engineers, UAE. In recognition of his contributions, he has beenconsistently ranked among the world’s top 2% of scientists in the field of energy byElsevier BV and Stanford University. In addition to his academic duties, he activelyserves in editorial roles for several international journals and is a frequent keynotespeaker at global conferences.

Professor Muhammad Farooq is a distinguished academician currently serving as Professor and Head of the Department of Plant Sciences at Sultan Qaboos University in Muscat, Oman. He also holds concurrent positions as an Adjunct Professor at the University of Western Australia (since 2011) and a Distinguished Visiting Professor at Dankook University, South Korea (since 2013). His research, on crop water relations and adaptation to dryland environments, has encompassed providing fundamental understanding of the response of crops to abiotic stresses.

Reading information

Smartphones and tablets

Install the Google Play Books app for Android and iPad/iPhone. It syncs automatically with your account and allows you to read online or offline wherever you are.

Laptops and computers

You can listen to audiobooks purchased on Google Play using your computer's web browser.

eReaders and other devices

To read on e-ink devices like Kobo eReaders, you'll need to download a file and transfer it to your device. Follow the detailed Help Center instructions to transfer the files to supported eReaders.

Report illegal content