Artificial Neural Networks in Chemical Engineering Processes: From Theory to Applications

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Ahad Ghaemi (Associate Professor, School of Chemical, Petroleum and Gas Engineering, Iran University of Science and Technology, Tehran, Iran) Zohreh Khoshraftar (Iran University of Science and Technology (IUST), Iran)

Artificial Neural Networks in Chemical Engineering Processes

Name: Artificial Neural Networks in Chemical Engineering Processes
Price: 438.95 AUD
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ISBN: 9780443328329

From Theory to Applications

Ahad Ghaemi (Associate Professor, School of Chemical, Petroleum and Gas Engineering, Iran University of Science and Technology, Tehran, Iran), Zohreh Khoshraftar (Iran University of Science and Technology (IUST), Iran)

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English

Elsevier - Health Sciences Division
19 December 2025

Chemical engineering

Artificial Neural Networks in Chemical Engineering Processes: From Theory to Applications serves as a comprehensive resource on artificial neural networks within chemical engineering, including understanding the fundamental principles, learning about relevant algorithms and architectures, and exploring practical case studies. This book covers theoretical principles, relevant algorithms, and practical case studies, this book covers artificial neural network concepts, architectures, and algorithms, with a focus on applications in chemical engineering processes. This book also addressed common challenges by providing practical guidance through successful case studies, offering insights on data pre-processing, model selection, training strategies, and performance evaluation. The book serves as a valuable tool for bridging the gap between neural networks and their practical implementation in chemical engineering.

This book will be an invaluable resource for chemical Engineers, particularly researchers and industry professionals working in Machine Learning and Artificial Intelligence. It will also be a very useful guide for Graduate and Postgraduate Students in Chemical Engineering and machine learning. Artificial Neural Networks in Chemical Engineering will also be a valuable resource for anyone working with artificial neural networks in other industries, particularly data scientists and analysts.

Edited by: Ahad Ghaemi (Associate Professor School of Chemical Petroleum and Gas Engineering Iran University of Science and Technology Tehran Iran), Zohreh Khoshraftar (Iran University of Science and Technology (IUST), Iran)
Imprint: Elsevier - Health Sciences Division
Country of Publication: United States
Dimensions: Height: 235mm, Width: 191mm,
Weight: 450g
ISBN: 9780443328329
ISBN 10: 0443328323
Pages: 462
Publication Date: 19 December 2025
Audience: Professional and scholarly , Undergraduate
Format: Paperback
Publisher's Status: Active

1. Artificial Neural Networks 2. MATLAB and Python functions of neural networks 3. Modelling of Absorption Processes using ANNs 4. Modelling of Adsorption Processes using ANNs 5. Modelling of Extraction Processes using ANNs 6. Modelling of Distillation Processes using ANNs 7. Modelling of Drying Processes using ANNs 8. Modelling of Leaching processes using ANNs 9. Modelling of Thermodynamic Properties using ANNs 10. Modelling of Vapour Liquid Equilibria systems using ANNs ] 11. Modelling of chemical reactors and reactions using ANNs 12. Modelling of pharmaceutical processing using ANNs

Ahad Ghaemi is a Professor in the School of Chemical, Petroleum and Gas Engineering, Iran University of Science and Technology. His research interests include process design, modeling and simulation, gas and petroleum industries, computational fluid dynamics, artificial intelligence, artificial neural networks, separation and purification processes, synthesis methods, environmental engineering, nanotechnology, and nano-adsorbents. Zohreh Khoshraftar is a Post-Doctoral Researcher in the School of Chemical, Petroleum and Gas Engineering, Iran University of Science and Technology. Her research fields encompass artificial neural networks in chemical engineering, design-expert, materials synthesis, pesticides, nanotechnology, and separation processes.