Multi-Objective Optimization of Industrial Power Generation Systems: Emerging Research and Opportunities

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The increased complexity of the economy in recent years has led to the advancement of energy generation systems. Engineers in this industrial sector have been compelled to seek contemporary methods to keep pace with the rapid development of these systems. Computational intelligence has risen as a capable method that can effectively resolve complex scenarios within the power generation sector. In-depth research on the various applications of this technology is lacking, as engineering professionals need up-to-date information on how to successfully utilize computational intelligence in industrial systems.

Multi-Objective Optimization of Industrial Power Generation Systems: Emerging Research and Opportunities provides emerging research exploring the theoretical and practical aspects of the application of intelligent optimization techniques within industrial energy systems. Featuring coverage on a broad range of topics such as swarm intelligence, renewable energy, and predictive modeling, this book is ideally designed for industrialists, engineers, industry professionals, researchers, students, and academics seeking current research on computational intelligence frameworks within the power generation sector.

Author(s): Timothy Ganesan
Series: Advances in Civil and Industrial Engineering
Publisher: IGI Global
Year: 2019

Language: English
Pages: 233
City: Hershey

Cover
Title Page
Copyright Page
Book Series
Table of Contents
Foreword
Preface
Acknowledgment
Chapter 1: Computational Intelligence in Energy Generation
Chapter 2: Optimization of a Solar-powered Irrigation System
Chapter 3: Multiobjective Programming for Waste Heat Recovery of an Industrial Gas Turbine
Chapter 4: Multiobjective Optimization for Waste Heat Recovery of an Industrial Gas Turbine using Extreme Value Stochastic Engines
Chapter 5: Biofuel Supply Chain Optimization Using Lévy-Enhanced Swarm Intelligence
Chapter 6: Biofuel Supply Chain Optimization Using Random Matrix Generators
Conclusion
Related Readings
About the Author
Index