Applied Machine Learning in Chemical Process Engineering

A Practical Approach
Erscheinungsjahr 2026
ISBN: 978-0-443-33943-1
Verlag: Elsevier Science

Buch, Englisch, 350 Seiten, Format (B × H): 191 mm x 235 mm

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.

Autoren/Hrsg.

Said, Zafar

Farooq, Muhammad

Fachgebiete

• Technische Wissenschaften Verfahrenstechnik | Chemieingenieurwesen | Biotechnologie Verfahrenstechnik, Chemieingenieurwesen
• Mathematik | Informatik EDV | Informatik Business Application Unternehmenssoftware
• Wirtschaftswissenschaften Betriebswirtschaft Unternehmensforschung
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz

Inhaltsverzeichnis

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

Über Autor(innen)

Farooq, Muhammad
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.

Said, Zafar
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.