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Effect of Thermodynamic Parameters on Modeling Industrial Slurry Reactors for Catalytic Olefins Polymerization

Effect of Thermodynamic Parameters on Modeling Industrial Slurry Reactors for Catalytic Olefins Polymerization

Authors: 
Tamaddoni, M. - Presenter, SABIC (Saudi Basic Industries Corporation)
Milosevic, M. - Presenter, SABIC (Saudi Basic Industries Corporation)

Effect of Thermodynamic Parameters on Modeling Industrial Slurry Reactors for Catalytic Olefins Polymerization

Maryam Tamaddoni1, Francesco Bertola1, Miran Milosevic1

1SABIC Technology Center, Urmonderbaan 22, 6167 RD Geleen, The Netherlands

Keywords: Polyolefins; Thermodynamics; Slurry reactor

Corresponding author: Maryam Tamaddoni, Maryam.Tamaddoni@SABIC.com

Abstract

The various chemical and physical phenomena occurring in a polymerization reactor can be classified into the three modeling levels: Micro-scale/kinetic modeling, Meso-scale/thermodynamic modeling and Macro-scale/reactor modeling [1]. To develop an integrated model for industrial slurry reactors, it is essential to include all these sub-models. Thermodynamics of the polyolefin slurry reactor including gaseous monomers/co-monomers, liquid diluent and polymer phase has a direct effect on the rate of polymerization. Therefore, the prediction of thermodynamic properties is a requirement of the integrated process model.

In the present work, the PC-SAFT Equation of State is employed to describe the equilibrium between different components in the multiphase reactor. Using PC-SAFT EoS, the description of components is provided by three pure-component parameters and one binary interaction parameter [2]. These parameters are available in literature and in commercial process simulation softwares for most of the components present in polyolefin slurry reactors.

In this study, a sensitivity analysis is carried out to understand the effect of the thermodynamic parameters on the predictive capability of the model developed for the slurry catalytic olefin polymerization process. The results of the model are compared with the available data from the industrial plant to evaluate the dependence of predictive capabilities of the model on the parameters used.

Acknowledgments

The authors are grateful to Prof. Gabriele Sadowski, Technical University of Dortmund, for PC-SAFT EoS parameters.

References

[1] V. Touloupidis, Macromol. React. Eng. 2014, 8, 508–527

[2] J. Gross, G. Sadowski, Ind. Eng. Chem.Res. 40, 2001, 1244–1342.