BP神经网络在加氢裂化装置航煤性质软测量中的应用

应用Matlab软件构建单隐层BP神经网络,并对中压加氢裂化装置航煤性质进行软测量应用。以700组样本数据作为训练集,对预测航煤闪点、终馏点模型进行训练。结果表明,在152组验证数据集上模型对闪点、终馏点预测分别取得1.57 ℃和2.74 ℃的均方误差(RMSE),随之在80组测试数据集上模型取得的泛化RMSE分别为1.87 ℃和1.98 ℃。以300组样本数据作为训练集,对预测航煤密度的模型进行训练。结果表明,在100组验证集上模型RMSE为2.18 kg·m^-3,随之在70组测试数据集上的泛化RMSE为2.72 kg·m^-3。BP神经网络的泛化RMSE表明,通过合理选择特征变量和设计网络结构,单隐层BP神经网络能够满足航煤性质的工业软测量要求。

Application of BP neural network in soft sensing of kerosene properties in hydrocracking unit

BP neural network with single hidden layer was constructed by using Matlab,and the soft-sensing application of kerosene properties in the medium pressure hydrocracking unit was carried out.The model was trained to predict kerosene flash point and final boiling point(FBP) with a training set of 700 sample data,and respectively a mean square error (RMSE) of 1.57 ℃ and 2.74 ℃for flash point and FBP prediction were obtained by using BP model on a validated set with 152 sample data,furtherly a generalized RMSE of 1.87 ℃and 1.98 ℃on a test set with 80 sample data was achieved. Another model was trained to predict kerosene density with a training set of 300 sample dataand a RMSE of 2.18 kg·m^-3 was obtained by using BP model on a validated set with 100 sample data,furtherly a generalized RMSE of 2.72 kg·m^-3 on a test set with 70 sample data was achieved respectively.The generalized RMSEs demonstrated that the BP neural network with single hidden layer could meet the requirements of industrial soft sensing of kerosene properties by reasonably selecting characteristic variables and designing network architecture.