Neural Network Soft-sensor Modeling of PVC Polymerization Process Based on Data Dimensionality Reduction Strategy

For predicting the conversion rate of vinyl chloride monomer (VCM) in the polyvinyl chloride (PVC) production process, a neural network soft-sensor model based on data dimensionality reduction strategies was proposed. In order to solve the problem of complex neural network topology and long training time caused by the excessive input vector dimension, seven kinds of data dimensionality reduction methods, such as principal component analysis (PCA), locality preserving projection (LPP), kernel principal component analysis (KPCA), expectation max principal component analysis (EMPCA), local tangent space alignment (LTSA), T-distributed stochastic neighbor embedding (TSNE) and neighboring preserving embedding (NPE), are used to reduce the dimension of the high-dimensional input data used in the neural network soft-sensor model. Then the radial basis function (RBF) neural network based on the gradient learning, orthogonal least squares and clustering learning methods and the dynamic fuzzy neural network (D-FNN) were utilized to realize the prediction on the VCM conversion rate. Simulation results show that the proposed neural network soft-sensor models based on seven data dimensionality reduction strategies can effectively predict the key economic and technical indicators of PVC polymerization process and meet the real-time control requirements of PVC production process.