stacked neural network approach for yield prediction of propylene polymerization

Prediction of reaction yield as the most important characteristic process of a slurry polymerization industrial process of propylene has been carried out. Stacked neural network as an effective method for modeling of inherently complex and nonlinear systems-especially a system with a limited number of experimental data points-was chosen for yield prediction. Also, effect of operational parameters on propylene polymerization yield was modeled by the use of this method. The catalyst system was Mg(OEt)₂/DIBP/TiCl₄/PTES/AlEt₃, where Mg(OEt)₂, DIBP (diisobutyl phthalate), TiCl₄, PTES (phenyl triethoxy silane), and triethyl aluminum (AlEt₃) (TEAl) were employed as support, internal electron donor (ID), catalyst precursor, external electron donor (ED), and co-catalyst, respectively. The experimental results confirmed the validity of the proposed model.