Machine Learning in Python for Dynamic Process Systems

Machine Learning in Python for Dynamic Process Systems offers a practical and accessible introduction to applying machine learning techniques to dynamic and time-dependent process systems using Python.

Designed for students, researchers, and engineers, this book bridges the gap between traditional process modeling and modern data-driven approaches. It focuses on real-world dynamic systems where variables change over time, such as chemical processes, industrial systems, and complex engineered environments.

Readers will learn how to implement machine learning models—including regression, classification, neural networks, and time-series methods—to analyze, predict, and optimize dynamic processes. The book emphasizes hands-on learning with clear explanations, Python-based examples, and step-by-step workflows that make complex concepts easier to understand.

By combining theoretical foundations with practical Python applications, this book equips readers with the tools needed to model dynamic behavior, improve system performance, and make data-informed decisions in modern process systems.

Key features include:

• Clear explanations of machine learning concepts for dynamic systems

• Practical Python examples and case studies

• Focus on time-dependent and process-based data

• Suitable for beginners with basic Python knowledge as well as advanced practitioners

Whether you are new to machine learning or looking to apply Python-based models to dynamic process systems, this book serves as a valuable guide to mastering data-driven modeling in real-world applications.

Size: A4(20*28cm)

Printing: 80 gm – color

Cover: Softcover