Novel CNN-based transformer integrating Boruta algorithm for production prediction modeling and energy saving of industrial processes

Abstract

Industrial process data collected by sensors have characteristics of high dimensionality, non-linearity and dynamics. Consequently, the selection extraction is regarded as a critical part for reducing the dimension of the data and removing irrelevant variables of constructing the production prediction model of industrial processes. Therefore, a novel production prediction model using the Boruta algorithm integrating the convolutional neural network-based Transformer (BCT) is proposed in this paper. Primarily, the Boruta algorithm maps nonlinear high-dimensional data to a low-dimensional space to select features that are meaningful to the yield of the industrial process. Then, the features are extracted adaptively using a convolutional neural network (CNN), which is encoded based on the transformer layer to learn relevant information about the different representation spaces. Furthermore, a linear layer with highway connections is employed to obtain prediction results. Finally, the BCT method is applied to establish a realistic production prediction model of actual liquefied petroleum gas plants for energy saving. Compared with back propagation neural networks, the radial basis function, the extreme learning machine, and the transformer based on the CNN, the BCT method achieves a state-of-the-art level. Furthermore, the BCT method provides the operation guidance on the actual liquefied petroleum gas (LPG) production process with increasing the LPG yield by 17.21%, which can improve production efficiency and reducing energy consumption.