Mathematical modeling of the molecular weight distribution of polypropylene produced in a loop reactor

Abstract

The model of the molecular weight distribution (MWD) of polypropylene produced in a loop reactor is established. The simulated MWD data of the polymers produced in steady-state polymerizations agree with the actual data collected from certain plant. The simulated weight-average molecular weight data also agree with the plant data in start-up processes. Furthermore, the model can be used to simulate the influence of the operation parameters on the MWD of the polymers produced in the steady-state polymerizations as well as the dynamic polymerizations. The simulated results of the steady state polymerizations show the MWD width of polypropylene decreases with the increase of both the propylene flow and the hydrogen flow, but increases with the increase of the catalyst flow. We also find the weight fraction of the polymers with short chains increases with the increase of both the propylene flow and the hydrogen flow, meanwhile, a small shift of the MWD curve to long chains can also be found as the catalyst flow increases. In the dynamic polymerizations, simulations indicate the MWD width and the weight fraction of the polymers with long chains all decrease in both of the start-up process and the end-up process of the polymerizations

Steady-state particle size distribution modeling of polypropylene produced in tubular loop reactors

“…For more information, readers are encouraged to refer Refs. [1,12,28]. According to the external module, we can easily predict both the polymer PSD and properties, and discuss the effects of the operational parameters on the polymer PSD.…”

“…The last one is certainly the most important at present [1]. In the tubular loop reactor, small catalyst particles (e.g.…”

Steady State Simulation of Ethylene Polymerization Using Multiple‐Site Coordination Catalysts

“…There are several simulation techniques to obtain the molecular weight distribution of a polyolefin, like Monte-Carlo [1] and Galerkin methods, [2] and also instantaneous distributions with uncertain distributions [3] instead of the Flory distribution used in this work. Monte-Carlo is a very powerful tool that gives us detailed information of the polymer microstructure, but needs higher computation effort and validation of the results of the statistical model.…”

Multiple active site Monte Carlo model for heterogeneous Ziegler‐Natta propylene polymerization

“…Corresponding MSmodel program builds a polymer yield-time curve with given rate constants and above weight fraction constants. The optimal rate constants are obtained through the comparison of the experimental and simulated data with an error expression that arrives a given minimum criterion, 7,11,25,31,34 Error…”

“…11,12,17,[26][27][28] The polymerization kinetic scheme comprises a series of elementary reactions, namely, chain initiation, chain propagation, chain transfer and chain termination reactions. In addition, the following kinetic modeling assumptions are made in this article:…”