Computational Tools for Sustainable Industrial Transformation

Overview

This book discusses how computational tools are revolutionizing sustainable industrial transformation. By integrating advanced technologies such as big data analytics, machine learning, digital twins, and IoT, this volume provides a comprehensive guide to optimizing industrial processes for enhanced efficiency and reduced environmental impact.

The chapters cover critical topics including the principles of industrial efficiency, the application of digital twins in manufacturing, and the application of machine learning and AI for process optimization and predictive maintenance. Readers will also explore the benefits of big data analytics in monitoring sustainability metrics and the role of IoT in smart sensor networks.

Through real-world case studies and expert contributions, this book offers actionable insights into how computational tools can revolutionize industrial practices. The material presented significantly advances sustainability science by addressing key challenges and opportunities in the transition towards smart and sustainable societies.

Through the integration of computational methods with industrial transformation, the book offers innovative solutions to pressing sustainability issues such as resource depletion, environmental degradation, and social inequality.

Intended Audience

Designed for industrial engineers, managers, and academics across disciplines such as engineering, environmental science, and business management, this book offers practical guidance on implementing computational techniques to optimize processes and reduce environmental impact.

It invites readers to think through critical questions about sustainable practices and provides actionable insights that can be directly applied within industrial settings. By bridging theoretical knowledge with practical application, this book serves as an essential resource for professionals seeking to drive sustainable change in industry.