An Integrated PRE Methodology for Capturing the Reaction Performance of Single‐ and Multi‐site Type Catalysts Using Bench‐Scale Polymerization Experiments

Abstract

applications (from building and construction to automotive, packaging, and medical applications). The humble polymer consisting of building blocks of ethylene, propylene and α-olefins is actually far from being simple, able to be formulated under infinite combinations of the inter-and intramolecular distributions that describe it. Polymer reaction engineering (PRE) offers the theoretical background for describing the catalytic olefin polymerization process. The scope is to develop a modeling pathway from polymerization process conditions to polymer microstructure and end-use properties. This enables us not only to better understand the process but also to establish a mathematical tool for predicting the reactor and the polymer performance under different reaction conditions. This PRE pathway consists of concrete and well-defined steps (Figure 1): [3] The scope of polymer reaction engineering (PRE) is to develop a modeling pathway from polymerization process conditions to polymer microstructure and end-use properties. The catalyst is the heart of the low-pressure polymerization and its kinetic parameters constitute the cornerstone of this pathway, without which none of the modeling steps can be established. In this work, an integrated PRE methodology for capturing the reaction performance of single-and multi-site type catalysts is presented. According to the methodology proposed, the catalyst kinetic parameters are estimated based on a series of targeted bench-scale polymerization experiments and characterization combined with polymer reaction engineering modeling.

A combined experimental and molecular simulation study on stress generation phenomena during the Ziegler–Natta polyethylene catalyst fragmentation process

“…35,36 On the other hand, macroscopic modelling approaches focus on the selection of the operation conditions of reactors to perform optimization studies. 5,17–19,37–44 There are two examples of this class of modeling approaches: Polymer Flow (PF) models are a typical example of modeling aimed to predict the final polymer properties like MWD, CCD and reaction rate; 8,45 morphology models (MF) based on experimental information, like porometer data, 17–19 are used to describe how the fragmentation takes place during the polymerization reaction. 17,40,41,45,46 However, though they can well describe the fragmentation mechanism, they are not predictive “ a priori ” because experimental morphology information is needed at different stages of the process.…”