Artificial Intelligence in Chemical Engineering explores the integration of artificial intelligence (AI) into various facets of chemical engineering. The book introduces historical information, highlights current state and trends in AI applications, and discusses challenges and opportunities within the field. Foundational principles of AI and machine learning are thoroughly covered, giving readers a solid understanding of basic AI principles, machine learning algorithms, and the crucial processes of model training and validation. The book then delves into the critical phase of data acquisition and preprocessing for AI models, addressing strategies for data collection, ensuring data quality, and techniques for feature engineering and selection.
Subsequent chapters cover a wide spectrum of AI applications in chemical engineering. From supervised and unsupervised learning for process modeling to the advanced realm of deep learning applications, this book explores neural networks, convolutional and recurrent architectures, and their real-world applications in process optimization and analysis.
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Table of Contents
Section I Foundations and Core Principles
1. Introduction to artificial intelligence in chemical engineering
2. Artificial intelligence in chemical engineering data acquisition and integration
3. Predictive modeling and process optimization in chemical engineering
4. Artificial intelligence in chemical engineering and process technologies
5. Artificial intelligence in chemical reaction engineering
6. Artificial intelligence in chemical kinetics and reactor design
7. Artificial intelligence in heat and mass transfer in chemical engineering processes
8. Artificial intelligence in fluid dynamics and multiphase flow systems
Section II Monitoring, Control, and Safety
9. Artificial intelligence in chemical engineering process monitoring and predictive maintenance
10. Real-time decision support systems in chemical and process engineering
11. Artificial intelligence in quality control and product development
12. Artificial intelligence in process safety and risk management
Section III Sustainable and smart chemical engineering
13. Artificial intelligence in sustainable manufacturing and chemical processes
14. Artificial intelligence in sustainable and smart agrochemicals
15. Artificial intelligence in energy optimization and renewable energy system integration
16. AI-enhanced supply chain management in chemical engineering
Section IV Education, governance and ethics
17. Artificial intelligence in chemical engineering and applied sciences education
18. Artificial intelligence for regulatory compliance in chemical engineering industries
19. Human-AI collaboration in chemical engineering
20. Regulatory compliance and ethical considerations in integration artificial intelligence
Authors
Farooq Sher Assistant Professor, Department of Engineering, Nottingham Trent University, UK.Dr. Farooq Sher is an internationally recognised academic, researcher, and innovator in the fields of chemical and environmental engineering. With over twenty years of experience, he has established a global reputation for pioneering research at the intersection of engineering science, sustainable technologies, and digital transformation, particularly with emerging digital tools applied to modern chemical and process engineering challenges.
Dr. Sher currently serves as an Assistant Professor in the Department of Engineering at Nottingham Trent University, Nottingham, United Kingdom. In addition to his academic responsibilities, he is actively engaged in international collaboration, research capacity-building, and interdisciplinary project leadership across several engineering domains. Dr. Sher holds PhD in Chemical Engineering from the University of Nottingham, United Kingdom, following his MSc in Chemical Engineering from the University of Leeds, United Kingdom and a Bachelor's degree in Chemical Engineering. He is also a Fellow of the Higher Education Academy (FHEA), an acknowledgement of his commitment to excellence in teaching, mentoring, and educational development at both undergraduate and postgraduate levels.
Dr. Sher's research spans a broad range of topics, including AI-driven process optimisation, sustainable manufacturing, catalytic systems, waste-to-energy technologies, renewable energy integration, and net-zero engineering solutions. His multidisciplinary and application-focused research approach bridges academic theory with real-world industrial innovation. Dr. Sher has published extensively, contributing numerous peer-reviewed journal articles, review papers, and book chapters to high-impact international publications. His work has received thousands of citations, and he has been consistently listed among the Top 2% of Scientists in the World by Elsevier and Stanford University, USA recognising his influence and impact in scientific research.
