Entropy-Guided Reverse Deconvolution of Polyolefin MWDs into Flory Most Probable Distributions

Multiple-site-type (MST) catalysts, such as heterogeneous Ziegler–Natta, Phillips, and some supported metallocenes, make polyolefins with broad molecular weight distributions (MWDs) that cannot be described with a single fundamental equation. Since polyolefin properties depend on their MWDs, accurate mathematical models for these distributions are essential in polymer reaction engineering. A popular modeling approach is to deconvolute their MWDs into several Flory most probable distributions (MPD). All previous deconvolution algorithms start by fitting an experimental MWD with a few Flory MPDs and then increase their number until the deviation between measured and predicted MWDs stops decreasing (forward deconvolution). This article reverses this approach, initially fitting experimental MWDs with multiple Flory MPDs and then decreasing their number with the assistance of a penalty function based on Shannon entropy (reverse deconvolution). The reverse method is easier to implement than the traditional forward methods and simplifies the automation of MWD deconvolution methods.