Research on Modeling of Industrial Soft Sensor Based on Ensemble Learning

Soft sensor technology has become one of the effective means to solve the problems that the key variables in complex industrial processes are not easy to measure. Single supervised or unsupervised modeling methods alone cannot sufficiently represent the data, leading to suboptimal results when using the acquired soft sensor model for critical variable prediction. Therefore, we have proposed an ensemble learning soft sensor model (SCAE-ACNN), which integrates stacked convolutional autoencoder (CAE) and attention mechanism. First, the SCAE-based learner constructed by stacking multiple pretrained convolutional autoencoder is called SCAE, in which unsupervised learning of SCAE can extract deep features of data. Second, in order to further enhance the prediction ability, this model uses convolution attention module to remove irrelevant and redundant information from the original features and constructs a convolution attention module base learner called attention convolutional neural network (ACNN). The homogeneous base learners stated above are trained, and the final key variables are obtained through the weighted average combination strategy. The proposed method based on ensemble learning is simulated in the debutanizer of steam thermal power generation and petroleum refining. The results show that the proposed SCAE-ACNN soft sensor model has better prediction performance than the traditional SVM algorithm, multilayer neural network, and advanced methods.