Combustion Optimization Based on Computational Intelligence

• Main Authors: Zhou, Hao (Author, http://id.loc.gov/vocabulary/relators/aut), Cen, Kefa (http://id.loc.gov/vocabulary/relators/aut)
• Corporate Author: SpringerLink (Online service)
• Format: Electronic eBook
• Language: English
• Published: Singapore : Springer Singapore : Imprint: Springer, 2018.
• Edition: 1st ed. 2018.
• Series: Advanced Topics in Science and Technology in China,
• Subjects: Energy efficiency.; Thermodynamics.; Heat engineering.; Heat transfer.; Mass transfer.; Energy systems.; Chemical engineering.; Computational intelligence.; Energy Efficiency.; Engineering Thermodynamics, Heat and Mass Transfer.; Energy Systems.; Industrial Chemistry/Chemical Engineering.; Computational Intelligence.
• Physical Description: XXVI, 270 p. 229 illus., 129 illus. in color. online resource.
• ISBN: 9789811078750
• ISSN: 1995-6819
• DOI: 10.1007/978-981-10-7875-0

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.