Paǔta准则在醚化温度预测建模异常数据剔除中的应用  

The Application of in Paǔta Criterion Eliminating Abnormal Values in Etherification Temperature Forecast Modeling

作　　者：徐前勇[1] 张运陶[1] 

机构地区：[1]西华师范大学应用化学研究所,四川南充637009

出　　处：《西华师范大学学报（自然科学版）》2011年第4期348-352,共5页Journal of China West Normal University(Natural Sciences)

基　　金：四川省重点技术创新项目企业信息化专项(川财建[2010]98号;2010XX026)

摘　　要：以某化工企业MTBE装置的过程参数C4流量F1、CH3OH流量F2、混合原料预热温度T301为变量,用BP神经网络建立了关于醚化塔下部温度T303D的预测模型.经Paǔta准则剔除样本数据异常点后建立的BP模型,训练、验证和预测计算结果的确定系数R2分别为0.8873、0.8873和0.8582,而直接用原始数据建立的BP模型,训练、验证和预测计算结果的R2则分别为0.8361、0.8148和0.7376.研究表明,运用Paǔta准则剔除异常样本数据,可以较大幅度的提高模型的预测准确性.With process parameters of a chemical enterprise MTBE device C4 flow F1, CH3 OH flow F2, mixing the ingredients preheating temperature T301 as variables, we have established an etherification tower temperature BPNN（Back Propagation Neural Network） prediction model about the T303D. The BP model was established after eliminating the abnormal values of the sample data by Pauta Criteria. The correlation coefficients（ R^2） of the training, validation and prediction were 0. 8873,0. 8873 and 0. 8582, respectively. And with the BP model established directly by original sample data, the R^2 calculation results of the training, validation and prediction were 0. 8361, 0. 8148 and 0. 7376, respectively. The research has shown that with the PaSta Criteria to eliminate the abnormal values of the sample data, the established BP model can improve the prediction accuracy significantly.

关 键 词：MTBE 醚化塔 Paǔta准则 BPNN 

分 类 号：TQ018[化学工程]