Improved Pearson Correlation Coefficient-Based Graph Neural Network for Dynamic Soft Sensor of Polypropylene Industries

Abstract

Polypropylene is an important product in the chemical industry and also a raw material for packaging bags, masks, and building boards. The melt index (MI) is a key indicator for evaluating the quality and efficiency of the polypropylene production process. Accurate measurement of the MI is beneficial to increase the polypropylene yield and save energy. The polypropylene production process is characterized by strong nonlinearity, obvious dynamic features, and complex structure, so the current soft sensor methods cannot carry out real-time and accurate soft sensor of the MI. In order to fully mine the complex relationship between variables of temporal data and extract the characteristics of time-series space in chemical production processes, this paper proposes a novel dynamic soft sensor method using an improved Pearson correlation coefficient-based graph neural network (GNN) (Pearson-GNN) method. The adjacency matrix is updated through the correlation coefficient of the data, which is integrated into the graph convolution and time sequence convolution modules of GNN to improve the accuracy of the soft sensing. Finally, the performance of the proposed Pearson-GNN is verified in time-series data soft-sensing task on the public air quality data set and actual polypropylene production processes. Compared with the diffusion concurrent recurrent neural networks (DCRNN), the multivariate time-series forecasting with graph neural networks (MTGNN), the spatiotemporal graph convolutional networks (STGCN), and the GNN based on a fully dynamic adjacency matrix without Pearson correlation updates (Fully GNN), the experimental results show that proposed Pearson-GNN is superior to other methods in terms of the mean absolute percentage error, the root-mean-square error, and mean absolute error.