Machine Learning Tools for Chemical Engineering: Methodologies and Applications explores the integration of Machine Learning (ML) techniques within the chemical engineering domain. This book highlights the precision, speed, and flexibility of ML solutions in addressing complex challenges that traditional methods struggle with. It offers both practical tools and a theoretical framework, combining knowledge modeling, representation, and management tailored to the unique needs of chemical engineering. Beyond the introduction of ML, the book delves into philosophies such as knowledge modeling, knowledge representation, search and inference, and knowledge extraction and management.

It is an invaluable resource for graduate students, researchers, educators, and industry professionals aiming to optimize and innovate in chemical processes through ML applications.

• Highlights the importance of correctly applying machine learning tools in data collection, model development, training, testing, and implementing decision support systems
• Presents the precision, speed, and flexibility of ML solutions in addressing complex challenges
• Delves into philosophies such as knowledge modeling, knowledge representation, search and inference, and knowledge extraction and management