CAJ | 학술논문

建立了正庚烷-甲苯-环丁砜液液平衡体系的神经网络模型．首先以303．15 K和313．15 K下的数据作为样本来训练神经网络，获得了优化的模型参数，而后用323．15 K下的数据来检验其预测能力．结果表明：在所研究的温度范围内，对相平衡数据能够给出较好的描述，3个温度下的平均绝对偏差分别为0．87％、0．71％和1．59％．
건립료정경완-갑분-배정풍액액평형체계적신경망락모형．수선이303．15 K화313．15 K하적수거작위양본래훈련신경망락，획득료우화적모형삼수，이후용323．15 K하적수거래검험기예측능력．결과표명：재소연구적온도범위내，대상평형수거능구급출교호적묘술，3개온도하적평균절대편차분별위0．87％、0．71％화1．59％．
A back propagation neural network model with one hidden layer was established to predict the liquid-liquid equilibrium data of n-heptane + toluene + ( sulfolane and N-methylpyrrolidone ) system under atmospheric pressure. The equilibrium data of 303. 15 K and 313. 15 K were employed to train the network, and then the optimized model parameters were obtained. The equilibrium data of 323. 15 K were used to validate its predicting capability. The results showed that average absolute deviations of the correlated ( training ) mole fractions from experimental data (303. 15 K and 313. 15 K) were 0. 87% and 0. 71%,respectively, and that of the predicted mole fractions from exper-imental data (323. 15K) was 1. 59%.