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.

• Serves as a comprehensive resource to bridge the gap between theoretical knowledge of neural networks and practical implementation in chemical engineering
• Provides in-depth explanations of neural network concepts, architectures, and algorithms, along with specifics about applications in chemical engineering
• Outlines various types of artificial neural networks, including feed-forward networks and their applications in chemical engineering processes and systems
• Includes practical guidance and case studies that showcase the successful application of neural networks in solving chemical engineering problems
• Presents insights into essential aspects such as data pre-processing techniques, model selection, training strategies, and performance evaluation
• Provides a roadmap for the effective implementation of neural networks in experimental modeling, including code and MATLAB modeling