Machine Learning in Python for Process Systems Engineering

Machine Learning in Python for Process Systems Engineering provides a practical and application-focused introduction to machine learning methods tailored specifically for process systems engineering (PSE).

This book is designed for students, researchers, and practicing engineers who want to integrate data-driven techniques with traditional process engineering knowledge. It demonstrates how machine learning can be used to model, analyze, and optimize complex process systems using Python, with an emphasis on real industrial and engineering applications.

Readers are guided through essential machine learning concepts—including regression, classification, clustering, neural networks, and data-driven modeling—within the context of process systems engineering. The book highlights time-dependent and process data, system dynamics, and operational decision-making, supported by clear explanations and hands-on Python examples.

By combining fundamental theory with practical implementation, this book enables readers to develop robust machine learning models for process analysis, monitoring, control, and optimization. Python-based workflows and case studies help bridge the gap between classical process modeling and modern machine learning approaches.

Key features include:

• Machine learning methods tailored for process systems engineering

• Python-based examples and practical case studies

• Focus on dynamic, multivariable, and process data

• Suitable for students and professionals with basic Python and engineering background

Whether you are new to machine learning or seeking to enhance process systems engineering applications with Python, this book serves as a comprehensive and accessible resource.

Size: A4(20*28cm)

Printing: 80 gm – color

Cover: Softcover