Machine Learning in Python for Dynamic Process Systems
https://DevCourseWeb.com
English | 2023 | ISBN: NA | 208 Pages | PDF | 10.2 MB
This book provides a comprehensive coverage of ML methods that have proven useful in process industry for dynamic process modeling. Step-by-step instructions, supported with industry-relevant case studies, show how to develop solutions for process modeling, process monitoring, etc., using classical and modern methods
This book is designed to help readers gain a working-level knowledge of machine learning-based dynamic process modeling techniques that have proven useful in process industry. Readers can leverage the concepts learned to build advanced solutions for process monitoring, soft sensing, inferential modeling, predictive maintenance, and process control for dynamic systems. The application-focused approach of the book is reader friendly and easily digestible to the practicing and aspiring process engineers, and data scientists. The authors of this book have drawn from their years of experience in developing data-driven industrial solutions to provide a guided tour along the wide range of available ML methods and declutter the world of machine learning for dynamic process modeling. Applications on time series analysis, process disturbance modeling, system identification, and process fault detection are illustrated with examples. Upon completion, readers will be able to confidently navigate the system identification literature and make judicious selection of modeling approaches suitable for their problems.
