如何控制和预测孔结构是炭气凝胶研究的重要课题.然而,由于耗时耗财,导致实验方法研究控制和预测孔结构成为难题.本文提出一种基于神经网络的炭气凝胶孔结构的预测与优化模型,并采用遗传算法设计和优化模型,对六种典型训练算法模型性能进行比较分析.利用该模型对孔径和吸附容量进行预测,两者的预测相关系数分别为0.992和0.981,预测均方根误差分别为0.077和0.054.经测试,该模型与实验研究的结果相符,并有效的应用于预测和控制炭气凝胶实验参数.
An intelligent simulation method for predicting and optimizing the pore structure of carbon aerogels is proposed by using an artificial neural network ( ANN) algorithm. The ANN model has been optimized based on an improved genetic algorithm from six typical training algorithms. The volumes and diameters of pores in the simulated samples are predicted by the optimized ANN model, which shows correlation coefficients R2 of 0. 992 and 0. 981 and root-mean-square prediction errors ( RMSPE) of 0. 077 and 0. 054 between the predicted and experimental values for the volumes and diameters of pores, respectively. The proposed model is expected to have practical applications in the pore structure control of carbon aerogels.
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