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.