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《Computers and Applied Chemistry》 2011-11
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Prediction of montmorillonite suspensions rheological parameters based on neural network

Tao Huaizhi,Pu Xiaolin~*,Wang Gui,Pan Lijuan and Xiang Chaogang (State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation,Southwest Petroleum University,Chengdu,610500, Sichuan,China)  
A model was used to research four kinds of additives(PAC-HV,CMC,SMP and SPNH) effecting on apparent viscosity(AV) and plastic viscosity(PV) of montmorillonite suspensions,which was based on a backpropagation(BP) neural network.The methods of resilient propagation(RP) algorithm and optimal stopping rule were used to avoid overfitting and improve training speed as well as generalization ability.A test on real data showed the maximum absolute error of AV was 8.75%,the average absolute error of AV was 1,12%;the maximum absolute error of PV was 8.82%, the average absolute error of PV was 1.42%.At last,the model was used to analysis the single additive effecting on rheological parameters.
【Fund】: 国家自然科学基金资助项目(50774065)
【CateGory Index】: TE254.4;TP183
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