Ai in Chemical Engineering - Unlocking the Power Within Data

This book explains machine learning and its implementation in the chemical and process industries. It explores the evolution of traditional plant operation into an integrated and smart operational environment and provides readers with the basis for understanding the use of tools to collect and analyze data for insight and application.

List of contents

1. Smart Manufacturing and Machine Learning. 2. Data and Data Pretreatment. 3. Dimensionality Reduction (DR). 4. Clustering. 5. Unsupervised Learning Case Study. 6. Concepts and Definitions. 7. Predictive Models. 8. Supervised Learning Case Studies. 9. Deep Learning. 10. Deep Learning Case Studies. 11. Reinforcement Learning. 12. Reinforcement Learning Case Studies. 13. Generative AI. Appendix A. FASTMAN-JMP Tool Architecture. Appendix B. Tennessee Eastman Process (TEP). Appendix C. High-Temperature PEM Fuel Cell Modelling. Appendix D. Distance Metrics for Clustering.

About the author

Jose A. Romagnoli is the Gordon & Mary Cain Endowed Chair Professor of Process Systems Engineering, Department of Chemical Engineering, Louisiana State University. He received his Ph.D. from University of Minnesota.
Luis A. Briceno-Mena works at Dow on their Machine Learning Optimization and Statistics team. He received his Ph.D. in Chemical Engineering from Louisiana State University.
Vidhyadhar Manee is a Senior Scientist in Process Research at Boehringer Ingelheim Pharmaceuticals Inc. He received his Ph.D. in Chemical Engineering from Louisiana State University.

Summary

This book explains machine learning and its implementation in the chemical and process industries. It explores the evolution of traditional plant operation into an integrated and smart operational environment and provides readers with the basis for understanding the use of tools to collect and analyze data for insight and application.

Product details

• Authors: Luis Briceño-Mena, Vidhyadhar Manee, José A. Romagnoli, Jose A. Briceno-Mena Romagnoli
• Publisher: Taylor & Francis Ltd.
• Languages: English
• Product format: Hardback
• Released: 31.12.2024
• EAN: 9781032597003
• ISBN: 978-1-0-3259700-3
• No. of pages: 286
• Subjects: Natural sciences, medicine, IT, technology > IT, data processing > IT; Data Mining, Artificial Intelligence, SCIENCE / Chemistry / Industrial & Technical, TECHNOLOGY & ENGINEERING / Automation, TECHNOLOGY & ENGINEERING / Chemical & Biochemical, Artificial Intelligence (AI), Chemical Engineering, Industrial Chemistry, Automatic control engineering, COMPUTERS / Artificial Intelligence / General, Biochemical Engineering, COMPUTERS / Data Science / Data Analytics, Industrial chemistry and chemical engineering