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《Computers and Applied Chemistry》 2014-06
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Prediction of oil-base drilling fluid emulsification stability based on BP neural network

Chen Fu;Xu Xin;Chen Junbin;Tao Huaizhi;College of Chemistry and Chemical Engineering,Southwest Petroleum University;Drilling Fluid Technology Service Company,Drilling & Production Engineering Research Institute of CCDC;  
Through the experiment,the calcium dodecyl benzene sulfomc acid calcium,calcium naphthenic acid,oleic acid and n-butyl alcohol four treatment effect on the properties of oil-based drilling fluid emulsification stability,and combined with the experimental data,the application of BP neural network to predict In the model training,the integrated neural network is adopted to improve the generalization ability of the BP neural network.The test result of the neural network ensemble is superior to a single subnet,the aging before the average error rate is7.85%,the biggest error rate is 22.34%;Aging after the average error rate is 5.75%,the maximum error of 14.53%.
【CateGory Index】: TE254
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