广东电力2018，Vol.31Issue(1)：30-35,6.DOI:10.3969/j.issn.1007-290X.2018.001.006
基于元素分析的煤粉工业分析GA-SVM预测模型
GA-SVM Prediction Model for Pulverized Coal Proximate Analysis Based on Ultimate Analysis
摘要
Abstract
Genetic algorithm (GA)and support vector machine (SVM)methods were used to build a rapid prediction method for pulverized coal proximate analysis based on ultimate analysis.According to 6 029 groups of the U.S.pulverized coal da-ta,this model took ultimate analysis on pulverized coal including C,H,O,N and S as inputs and proximate analysis inclu-ding volatile and fixed carbon as outputs.Another 74 groups of Chinese pulverized coal data were obtained through standard experiments for model verification.Corresponding results indicate average relative errors of prediction on volatile and fixed carbon of the U.S.pulverized coal are 4.60% and 3.22%,and average relative errors of prediction on volatile and fixed carbon of the Chinese pulverized coal are 9.16% and 3.55%.It is proved this model can well make use of ultimate analysis data to predict fixed carbon and volatile and has small prediction errors.关键词
煤粉/元素分析/工业分析/支持向量机/遗传算法/预测Key words
pulverized coal/ultimate analysis/proximate analysis/support vector machine/genetic algorithm/prediction分类
能源科技引用本文复制引用
陈红,黎盛鸣,耿向瑾,赵明,谭鹏,方庆艳,张成..基于元素分析的煤粉工业分析GA-SVM预测模型[J].广东电力,2018,31(1):30-35,6.基金项目
国家自然科学基金(51676076) （51676076）
