Study on Auto-Ignition Temperature of Binary Mixed Liquid Under Mixed Neural Network
To solve the time and safety problems faced by the traditional experimental methods for measur-ing the auto-ignition temperature of binary mixed liquids,a method optimizing the quantitative structure-property relationship(QSPR)prediction model by using neural networks is proposed.BP neural network(BPNN)and the one-dimensional convolutional neural network(1DCNN)are used to process the mixed molecular descriptor data,respectively.Then,the molecular structure map data is processed using the convolutional neural network(CNN),and in this way,two prediction models,BPNN+CNN and IDCNN+CNN,are established.After that,the prediction ability,fitting ability and stability of the experimentally designed BPNN+CNN and 1DCNN+CNN models are verified through cross-validation,residual analysis and application domain analysis.Finally,the effects of different optimizers on the model performance are discussed.And the results of two-dimensional and three-dimensional molecular structure diagrams on the model performance are analyzed.The experimental results show that the coefficients of determination of the two models are 0.989 8 and 0.987 1,respectively.The 10-fold cross-validated com-plex correlation coefficients are 0.961 1 and 0.963 3,respectively.And the cross-validated coefficients are 0.982 6 and 0.992 5,respectively.The results indicate that both models can predict the self-ignition temperature of most binary mixed liquids.The BPNN+CNN model has better fitting ability,and the 1DCNN+CNN model has better stability.
国家科技期刊平台
NSTL
万方数据
维普
