基于随机卷积核神经网络数据增强的软测量OA

The by-product 4-CBA of PX oxidation reaction in the production process of purified terephthalic acid(PTA)is difficult to mea-sure online,and only a small amount of samples can be obtained through offline analysis.A dynamic soft sensing model RCKN-XGBoost based on random convolutional kernel neural network data augmentation is proposed to address this issue.The model first uses random convolu-tional kernel neural network(RCKN)for data augmentation,expanding the sample size and improving its diversity;Then,the original sample is linearly combined with the expanded sample to form a new dataset;Finally,XGBoost was used to train and predict the network.On the 4-CBA content dataset of PX oxidation process in a certain chemical plant,the RCKN-XGBoost model was compared with XGBoost,CNN,CNN-XGBoost,and Laplace XGBoost models.It was found that the MRE index of the RCKN-XGBoost model decreased by 1.07%,0.92%,0.80%,and 0.65%,respectively,and the RMSE decreased by 27.9%,18.62%,12.58%,and 8.05%,proving the effectiveness of the model.