Neural Network Construction and Its Application in Coal-fired Power Plants for Coal-Blending Optimization
In this paper, the prediction effects of different BP neural network model were analyzed and the main factors affecting the prediction effects were also studied. These factors included network structure, learning sample quantities, hidden nodes, learning accuracy and etc. Based on the above analyses and studies, BP neural networks were built to predict characteristics, such as low heat and others, of blended coals, and the prediction accuracy is extremely high. The errors of the prediction cases were all less than 0.12％, and most of them were around 0.04％. In addition, the optimization of coal blending for a 125MW unit at a coal-fired power station was conducted with exhaustive method, and it is very directive to the practical coal blending. The characteristics of neural network are with close relation with the data extension of the input and output samples. The smaller the input and output data extension, the better the convergence of the neural network and the error of the network will be smaller. The structure of the neural network constructed in this paper is decided by structure of the input and output data, so it is universal and has strong expansion.
neural network construction coal-blending optimization coal-fired power plants
WU Jiang ZHANG Yanping YE Chenfei SHEN Minqiang REN Jianxing HE Ping DAI Zuoying HUANG Jinjie LI Xiongying HUANG Ning GAO Jing
School of Energy and Environmental Eng., Shanghai Univ. of Electric Power, Shanghai 200090, P.R. Chi Dept. of Operation, Xuzhou Hnarun Power Plant Co.Ltd., No. 1 Huarun Rd., Xuzhou, Jiangsu, 221142, P. Huaneng Shantou Power Plant in Guangdong Branch of the operation of Shantou, 515071, P.R. China
国际会议
The 6th International Symposium of Asia Institute of Urban Environment(亚洲城市环境学会第六届国际会议)
长春
英文
641-645
2009-09-20（万方平台首次上网日期，不代表论文的发表时间）