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