摘要
针对煤质元素分析实验耗时长、成本高的问题,利用GA-BP神经网络预测煤质元素分析结果,构建的GA-BP神经网络模型以289组典型动力煤工业分析结果作为已知量预测元素分析,将预测结果与传统的多元回归关联式所得预测结果进行对比,通过决定系数和均方根误差评价预测结果。结果表明,GA-BP神经网络预测模型对碳、氢、氧、氮、硫的决定系数分别为0.935、0.811、0.779、0.430、0.051,均方根误差分别为2.503、0.458、2.551、0.245、0.824,均优于传统多元回归关联式对各元素的预测结果。最后,将该元素分析预测模型应用于锅炉热效率和碳排放量计算,并计算其与元素分析真实数据所得结果的误差,锅炉热效率平均相对误差为0.19%,碳排放量平均相对误差为3.56%。
In response to the problem of long time and high cost in coal element analysis experiments,a GA-BP neural network is used to predict the results of coal element analysis.The GA-BP neural network model is constructed using 289 sets of typical thermal coal industrial analysis results as known quantity prediction element analysis.The prediction results are compared with the prediction results obtained from traditional multiple regression correlations,and the prediction results are evaluated through determination coefficients and root mean square errors.The results show that the determination coefficients of the GA-BP neural network prediction model for carbon,hydrogen,oxygen,nitrogen,and sulfur are 0.935,0.811,0.779,0.430 and 0.051,respectively,with root mean square errors of 2.503,0.458,2.551,0.245 and 0.824,which are better than the prediction results of traditional multiple regression correlations for each element.Finally,the element analysis prediction model is applied to calculate the boiler thermal efficiency and carbon emissions,and the error between it and the actual data obtained from element analysis is calculated.The average relative error of the boiler thermal efficiency is 0.19%,and the average relative error of carbon emissions is 3.56%.
作者
邵志翔
刘柱
亢银虎
卢啸风
王泉海
SHAO Zhixiang;LIU Zhu;KANG Yinhu;LU Xiaofeng;WANG Quanhai(School of Energy and Power Engineering,Chongqing University,Chongqing 400044,China)
出处
《湖南电力》
2023年第6期54-62,共9页
Hunan Electric Power
基金
国家自然科学基金项目(22178032)
四川省科技计划项目(2022YFSY0025)。
