Applied Machine Learning in Chemical Process Engineering
A Practical Approach
- 1st Edition - January 1, 2026
- Latest edition
- Editors: Zafar Said, Muhammad Farooq
- Language: English
- Paperback ISBN:9 7 8 - 0 - 4 4 3 - 3 3 9 4 3 - 1
- eBook ISBN:9 7 8 - 0 - 4 4 3 - 3 3 9 4 4 - 8
As machine learning capabilities and functionality increases, more industry experts and researchers are integrating applied machine learning into their research. Applied Machine Le… Read more
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
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
1. Introduction to Machine Learning for Chemical Engineers
2. Data Handling and Preprocessing in Chemical Datasets
3. Predictive Modeling for Chemical Processes
4. Unsupervised Learning and Pattern Recognition in Chemical Data
5. Process Optimization and Control using Machine Learning
6. Molecular Simulations and Deep Learning
7. Reinforcement Learning in Process Design
8. Challenges and Ethical Considerations in Implementing ML
9. Case Studies: Breakthroughs at the Intersection of ML and Chemical Engineering
10. Physics-Informed Neural Networks in Chemical Engineering
11. Explainable AI and Sustainable Computing in Machine Learning
12. Future of AI in Chemical and Process Engineering Scope: Future trends and technologies in ML for chemical engineering
2. Data Handling and Preprocessing in Chemical Datasets
3. Predictive Modeling for Chemical Processes
4. Unsupervised Learning and Pattern Recognition in Chemical Data
5. Process Optimization and Control using Machine Learning
6. Molecular Simulations and Deep Learning
7. Reinforcement Learning in Process Design
8. Challenges and Ethical Considerations in Implementing ML
9. Case Studies: Breakthroughs at the Intersection of ML and Chemical Engineering
10. Physics-Informed Neural Networks in Chemical Engineering
11. Explainable AI and Sustainable Computing in Machine Learning
12. Future of AI in Chemical and Process Engineering Scope: Future trends and technologies in ML for chemical engineering
- Edition: 1
- Latest edition
- Published: January 1, 2026
- Language: English
ZS
Zafar Said
Dr. Zafar Said is currently working as a Distinguished Associate Professor in the
Department of Mechanical and Aerospace Engineering at the United Arab Emirates
University, UAE. He received his doctoral degree in Mechanical Engineering from
the University of Malaya, Malaysia, and completed his postdoctoral research at
Khalifa University, UAE. Dr. Said is a recognized leader in energy technology,
nanofluids, and sustainable energy. His major areas of interest include heat
transfer, solar energy systems, and advanced thermofluids. His research focuses on
battery thermal management, enhancement of solar collectors using nanofluids
and turbulators, and the development of stable nanorefrigerants and
nanolubricants. He also applies artificial intelligence and machine learning to
predict thermophysical properties and optimize energy systems. He is the recipient
of several prestigious awards, including the Khalifa Award for Education as
Distinguished University Professor (2025), the Future Pioneer Award in
Sustainability (2025), and Best Academic Research at the 13th Dubai Award for
Sustainable Transport (2024). He has also received the Research and Innovation
Award from the UAE Ministry of Energy and Infrastructure (2022) and First Place in
Scientific Research at the Excellence and Creative Engineering Award (2023) by the
Society of Engineers, UAE. In recognition of his contributions, he has been
consistently ranked among the world’s top 2% of scientists in the field of energy by
Elsevier BV and Stanford University. In addition to his academic duties, he actively
serves in editorial roles for several international journals and is a frequent keynote
speaker at global conferences.
Affiliations and expertise
Distinguished Associate Professor, Mechanical and Aerospace Engineering Department, College of Engineering, United Arab Emirates University, Al Ain, United Arab EmiratesMF
Muhammad Farooq
Professor Muhammad Farooq is an experienced professional with more than 17 years of blended experience in research, teaching, training, industry, and project management across the globe and has visited over 25 countries for various professional activities including USA, UK, European countries, China, Pakistan, Turkey and the Gulf region. He holds PhD in Mechanical Engineering degree from Heriot-Watt University Edinburgh, United Kingdom. Dr. Farooq is the author of over 150 leading international research articles and his work has been cited more than 3,700 times. He is recognized among the top 2% scientists worldwide, according to the list issued by Stanford University, USA, and Elsevier/Scopus. He is a strong advocate for net-zero and carbon-neutral initiatives for sustainable environmental applications through SDGs and collaborating with more than 20 countries for various professional engagements related to research and development.
As an Editor, Dr. Farooq has handled more than 500 research articles for renowned journals and international conferences, including Journal of Carbon Research (Q1), npj Thermal Science and Engineering (Springer Nature), Discover Sustainability (springer nature-Q2), The Journal of King Saud University – Engineering Sciences (Elsevier-Q1), Frontiers in Bioengineering and Biotechnology (Q1), Frontiers in Energy Research Journal (Q2), Sustainability Journal (MDPI-Q1), ChemEngineering Journal (MDPI- Q2), Chemistry Journal (MDPI- Q2), Energies Journal (MDPI-Q1), Processes Journal (MDPI-Q2), Journal of Agriculture (MDPI-Q1), Journal of Energy and Environment (SAGE- Q2), and the Pakistan Journal of Engineering and Applied Sciences. He has served as a volunteer and recognized reviewer for leading international journals and top-notch conferences, having reviewed over 1,000 research papers and received recognition awards from prestigious publishers such as Elsevier, Springer, SAGE Publishing, MDPI, and the Taylor & Francis Group.
Dr. Farooq has received numerous highly competitive international grants and awards including Faculty Research Grant, NRPU HEC Research Project Award, UK Alumni Award by British Council, Best Paper Award by Institute of Engineers Pakistan, British Council Pak-UK Education Gateway Award for faculty exchange, Neilson Research Award, EU-CO2-TRIP Project funded by Marie Curie for Clean Coal Energy Generation, UK ADNET Research Grant, UK BBSRC and Faculty Development Scholarship. He has conducted various international training sessions as a resource person and frequently serves as a chair of technical sessions, conference secretary, member of technical committees, organizer, and invited keynote speaker at world-renowned international conferences, summer schools, and professional meetings related to energy systems.
Affiliations and expertise
Department of Mechanical Engineering, University of Engineering and Technology Lahore, Pakistan. Department of Mechanical Engineering, Prince Mohammad Bin Fahd University, Saudi Arabia