Modeling and simulation of polypropylene particle size distribution in industrial horizontal stirred bed reactors

Abstract

This work aims at developing a steadystate particle size distribution (PSD) model for predicting the size distribution of polypropylene particles in the outflow streams of propylene gas-phase horizontal stirred bed reactors (HSBR), on the one hand and investigating the effect of the catalyst residence time distribution (RTD) on the polymer PSD, on the other hand. The polymer multilayer model (PMLM) is used to describe the growth of a single particle. Knowing the PSD and RTD of a Ziegler-Natta type of catalyst and polymerization kinetics, this model allows calculating the polymer PSD of propylene polymerization in the HSBRs. The calculated polypropylene PSDs agree well with those obtained from the indus-trial reactors. The results reveal that both the PSD and the RTD of the catalyst affect the polymer PSD but in different manners. The effect of RTD on the PSD is less significant in the case of a nonuniform size catalyst feed. This model also allows investigating the effects of other process parameters on the polymer PSD under steady-state conditions, including intraparticle mass-and heat-transfer limitations, initial catalyst size, and polymer crystallinity.

The Effect of Residence Time Distribution on the Slurry‐Phase Catalytic Ethylene Polymerization: An Experimental and Computational Study

“…Many efforts have been devoted in literature for explicitly including the residence time distribution and its effects on the final particle size distribution (PSD) in a modeling framework . Indeed, the knowledge of the main factors which govern the PSD is fundamental, since it determines the design of the units for the recovery, treatment, and the processing of the polymer.…”

Variation of Comonomer Content in LLDPE Particles with Different Sizes from an Industrial Fluidized Bed Reactor

“…PSD is one of the morphological distributed properties that plays a major role in the product final properties. Particle growth, average particle size, and particle size distribution in fluidized-bed polymerization reactors have also been the focus of several works. − They have pointed out that PSD is affected by the following parameters: catalyst PSD, catalyst residence time in prepolymerization stage, particle fragmentation and growth pattern, particle attrition and elutriation, and reaction conditions.…”

Multiscale modelling of multizone gas phase propylene (co)polymerization reactors—A comprehensive review

“…Santos et al [ 14,15 ] were the first to employ a fairly complete multiscale framework (including a kinetic model, a single particle model, and a reactor model) to the MZCR for propylene homo‐ and copolymerization. Later, Tian et al [ 43 ] applied the multiscale approach to predict the polymer PSD of propylene polymerization in the HSBRs. However, in these models, thermodynamic modelling was quite simple, as Henry's law was used to predict the concentrations of monomer and comonomer at the active sites.…”

“…[9][10][11][12] A number of authors have reported the implementation of the multiscale framework in gas phase polyolefin polymerization to better understand the polymerization process behaviors. [13][14][15][16] The practical applications of the multiscale modelling in the manufacturing processes are, for instance, product quality monitoring and control, existing process improvement and optimization, process safety, process scale-up, and new product-process development. [9][10][11]13,14,[17][18][19][20][21] The purpose of the current work is to review and discuss the application of a multiscale modeling framework to gas phase propylene (co)polymerization reactors and highlight key issues and challenges.…”

“…[ 9–12 ] A number of authors have reported the implementation of the multiscale framework in gas phase polyolefin polymerization to better understand the polymerization process behaviour. [ 13–16 ] The practical applications of multiscale modelling in the manufacturing processes are, for instance, product quality monitoring and control, existing process improvement and optimization, process safety, process scale‐up, and new product‐process development. [ 9–11,13–15,17–20 ]…”