Combustion Optimization Based on Computational Intelligence

This book presents the latest findings on the subject of combustion optimization based on computational intelligence. It covers a broad range of topics, including the modeling of coal combustion characteristics based on artificial neural networks and support vector machines. It also describes the optimization of combustion parameters using genetic algorithms or ant colony algorithms, an online coal optimization system, etc. Accordingly, the book offers a unique guide for researchers in the areas of combustion optimization, NOx emission control, energy and power engineering, and chemical engineering.

Auteur

Professor Hao Zhou received his Ph.D. degree from Zhejiang University in 2004. He is currently Deputy Director of State Key Laboratory of Clean Energy Utilization at Zhejiang University and Director of the Zhejiang University - University of Leeds joint research center for sustainable energy. His research interests include combustion optimization, low pollutant combustion technology for utility boilers, and neural network and support vector machine modeling methods. He has published over 20 academic papers and filed 7 patents in the areas of combustion pollutants control and combustion optimization since 2000.

Professor Kefa Cen is a member of the Chinese Academy of Engineering. He received his Ph.D. degree from Moscow Industrial Technology University and has expertise in clean coal combustion and gasification, poly-generation and comprehensive utilization of energy resources, as well as biomass gasification and bio-oil. He is currently Director of the Institute for Thermal Power Engineering at Zhejiang University and Chairman of the Chinese Society of Power Engineering's International Cooperation & Exchange Committee. He is also Editor-in-Chief of the Journal of Zhejiang University (Engineering Science) and the Journal of Renewable Energy. He has published over 800 academic papers and 15 books.

Contenu
The influence of combustion parameters on NOx emissions and carbon burnout.- Modeling methods for combustion characteristics.- Neural network modeling of combustion characteristics.- Support vector machine modeling the combustion characteristics.- Combining neural network or support vector machine with optimization algorithms to optimize the combustion.- Online combustion optimization system.