Novel Gray Orthogonal Echo State Network Integrating the Process Mechanism for Dynamic Soft Sensor Development

Polypropylene is an important raw material for producing medical masks. The melt index (MI) is one of the most important quality indexes in the propylene polymerization (PP) production process, but it cannot be physically measured in real time. In consideration of the strong nonlinearity, obvious dynamic characteristics, and complex mechanism of the PP process, the gray soft sensor model, which combines the merits of mechanism-driven modeling and data-driven modeling, has great research value. In this study, we propose a novel gray dynamic soft sensor modeling strategy. The influence factors of the MI are analyzed based on the process mechanism of PP production plants to select appropriate process variables and make necessary mechanism transformation. Then, the kernel principal component analysis and wavelet denoising are used to eliminate the multicollinearity and “noise” interference among process variables. Finally, an improved orthogonal sparse echo state network is used to construct the gray dynamic soft sensor model. The experimental results based on the real field data of the PP production plant show that the orthogonalization and sparseness of the reservoir can effectively enhance the performance of the reservoir and improve the operational efficiency. Meanwhile, the proposed dynamic soft sensing model has better prediction ability than the corresponding methods. Moreover, this study is of great significance to guide and optimize the PP production process.