A stacked neural network approach for yield prediction of propylene polymerization

Abstract

AbstractPrediction 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. © 2009 Wiley Periodicals, Inc. J Appl Polym Sci, 2010