Overview

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.

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9781799817116

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Timothy Ganesan is currently a Senior Analyst at the Royal Bank of Canada specializing in computational intelligence and data analytics. He has experience working as a Principal Researcher for the Fuels and Combustion Section in the research and development arm of the Malaysian power producer - Tenaga Nasional Berhad (TNB). In addition to having degrees in Chemical Engineering and Computational Fluid Dynamics, he holds a Ph.D. in Process Optimization. His research interests include engineering/industrial optimization, multi-objective/multi-level programming, evolutionary algorithms, machine learning, chaos optimization, and swarm-based optimization.

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