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.
Sprache
Verlagsort
Zielgruppe
Produkt-Hinweis
Broschur/Paperback
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Maße
Höhe: 235 mm
Breite: 191 mm
ISBN-13
978-0-443-34076-5 (9780443340765)
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Schweitzer Klassifikation
Dr. Farooq Sher is an internationally recognised academic, researcher, and innovator in the fields of chemical engineering 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 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.
Herausgeber*in
Assistant Professor, Department of Engineering, Nottingham Trent University, UK
1. Introduction to Artificial Intelligence in Chemical Engineering
2. Fundamentals of Artificial Intelligence and Machine Learning for Chemical Engineers
3. Data Acquisition and Integration in AI Applications
4. Predictive Modeling and Process Optimization
5. Control Systems and Decision Support with AI
6. Real-time Decision Support Systems in Process Industries
7. AI Applications in Chemical Reaction Engineering
8. AI in Process Safety and Risk Management
9. AI in Sustainable and Green Processes
10. Sustainable Manufacturing and Green Chemistry with AI
11. AI for Energy Efficiency and Renewable Integration
12. Smart Manufacturing and Industry 4.0 Integration
13. AI in Quality Control and Product Development
14. Advanced Process Monitoring and Predictive Maintenance with AI
15. Human-AI Collaboration in Chemical Engineering
16. AI in Chemical Education and Training
17. AI for Regulatory Compliance in Chemical Industries
18. Case Studies and Practical Implementations
19. Ethical Considerations and Challenges in AI Integration
20. Future Trends and Innovations in AI and Chemical Engineering
21. Conclusion and Outlook
