Enhanced combination modeling method for combustion efficiency in coal-fired boilers

Authors:
Guoqiang Li;Peifeng Niu;Chao Liu;Weiping Zhang
Affiliations:
Key Lab of Industrial Computer Control Engineering of Hebei Province, Yanshan University, Qinhuangdao 066004, China and National Engineering Research Center for Equipment and Technology of Cold St ...;Key Lab of Industrial Computer Control Engineering of Hebei Province, Yanshan University, Qinhuangdao 066004, China and National Engineering Research Center for Equipment and Technology of Cold St ...;Key Lab of Industrial Computer Control Engineering of Hebei Province, Yanshan University, Qinhuangdao 066004, China;Key Lab of Industrial Computer Control Engineering of Hebei Province, Yanshan University, Qinhuangdao 066004, China and Qinhuangdao Institute of Technology, Qinhuangdao 066100, China
Venue:
Applied Soft Computing
Year:
2012

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Abstract

In this paper, we propose a new combination modeling method whose structure consists of three components: extreme learning machine (ELM), adaptive neuro-fuzzy inference system (ANFIS) and PS-ABC which is a modified hybrid artificial bee colony algorithm. The combination modeling method has been proposed in an attempt to obtain good approximations and generalization performances. In the whole model, ELM is used to build a global model, and ANFIS is applied to compensate the output errors of ELM model to improve the overall performance. In order to obtain a better generalization ability and stability model, PS-ABC is adopted to optimize input weights and biases of ELM. For stating the proposed model validity, it is applied to set up the mapping relation between the boiler efficiency and operational conditions of a 300WM coal-fired boiler. Compared with other combination models, the proposed model shows better approximations and generalization performances.