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.
Sher, Farooq: -
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.
