Integrating multi-objective optimization with computational fluid dynamics to optimize boiler combustion process of a coal fired power plant

Liu, Xingrang and Bansal, R. C. (2014) Integrating multi-objective optimization with computational fluid dynamics to optimize boiler combustion process of a coal fired power plant. Applied Energy, 130 658-669. doi:10.1016/j.apenergy.2014.02.069

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Author Liu, Xingrang
Bansal, R. C.
Title Integrating multi-objective optimization with computational fluid dynamics to optimize boiler combustion process of a coal fired power plant
Journal name Applied Energy   Check publisher's open access policy
ISSN 0306-2619
1872-9118
Publication date 2014-03-20
Sub-type Article (original research)
DOI 10.1016/j.apenergy.2014.02.069
Open Access Status Not Open Access
Volume 130
Start page 658
End page 669
Total pages 12
Place of publication Kidlington, Oxford, United Kingdom
Publisher Pergamon
Language eng
Formatted abstract
Highlights
• A coal fired power plant boiler combustion process model based on real data.
• We propose multi-objective optimization with CFD to optimize boiler combustion.
• The proposed method uses software CORBA C++ and ANSYS Fluent 14.5 with AI.
• It optimizes heat flux transfers and maintains temperature to avoid ash melt.

The dominant role of electricity generation and environment consideration have placed strong requirements on coal fired power plants, requiring them to improve boiler combustion efficiency and decrease carbon emission. Although neural network based optimization strategies are often applied to improve the coal fired power plant boiler efficiency, they are limited by some combustion related problems such as slagging. Slagging can seriously influence heat transfer rate and decrease the boiler efficiency. In addition, it is difficult to measure slag build-up. The lack of measurement for slagging can restrict conventional neural network based coal fired boiler optimization, because no data can be used to train the neural network. This paper proposes a novel method of integrating non-dominated sorting genetic algorithm (NSGA II) based multi-objective optimization with computational fluid dynamics (CFD) to decrease or even avoid slagging inside a coal fired boiler furnace and improve boiler combustion efficiency. Compared with conventional neural network based boiler optimization methods, the method developed in the work can control and optimize the fields of flue gas properties such as temperature field inside a boiler by adjusting the temperature and velocity of primary and secondary air in coal fired power plant boiler control systems. The temperature in the vicinity of water wall tubes of a boiler can be maintained within the ash melting temperature limit. The incoming ash particles cannot melt and bond to surface of heat transfer equipment of a boiler. So the trend of slagging inside furnace is controlled. Furthermore, the optimized boiler combustion can keep higher heat transfer efficiency than that of the non-optimized boiler combustion. The software is developed to realize the proposed method and obtain the encouraging results through combining ANSYS 14.5, ANSYS Fluent 14.5 and CORBA C++.
Keyword Multi-objective optimization
Carbon emission
Coal fired power plant combustion optimization
Slagging and fouling
ANSYS Fluent
CORBA
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ
Additional Notes Available online 20 March 2014

Document type: Journal Article
Sub-type: Article (original research)
Collections: Official 2015 Collection
School of Information Technology and Electrical Engineering Publications
 
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Citation counts: TR Web of Science Citation Count  Cited 17 times in Thomson Reuters Web of Science Article | Citations
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Created: Mon, 12 May 2014, 19:51:43 EST by Xingrang Liu on behalf of School of Information Technol and Elec Engineering