Multi-objective optimization of an industrial slurry phase ethylene polymerization reactor-Reference-Cited by-同舟云学术

Multi-objective optimization of an industrial slurry phase ethylene polymerization reactor

Published:2021-11-30 Issue:0 Volume:0 Page:
ISSN:1542-6580
Container-title:International Journal of Chemical Reactor Engineering
language:en
Short-container-title:

Author:

Thakur Amit K.¹^ORCID,Gupta Santosh K.¹,Kumar Rahul¹,Banerjee Nilanjana¹^ORCID,Chaudhari Pranava¹

Affiliation:

1. Department of Chemical Engineering , University of Petroleum and Energy Studies , Dehradun , India

Abstract

Abstract Slurry polymerization processes using Zeigler–Natta catalysts are most widely used for the production of polyethylene due to their several advantages over other processes. Optimal operating conditions are required to obtain the maximum productivity of the polymer at minimal cost while ensuring operational safety in the slurry phase ethylene polymerization reactors. The main focus of this multi-objective optimization study is to obtain the optimal operating conditions corresponding to the maximization of productivity and yield at a minimal operating cost. The tuned reactor model has been optimized. The single objective optimization (SOO) and multi-objective optimization (MOO) problems are solved using non-dominating sorting genetic algorithm-II (NSGA-II). A complete range of Pareto optimal solutions are obtained to obtain the maximum productivity and polymer yield at different input costs.

Publisher

Walter de Gruyter GmbH

Subject

General Chemical Engineering

Link

https://www.degruyter.com/document/doi/10.1515/ijcre-2021-0196/pdf

Reference34 articles.

1. Abbasi, M. R., A. Shamiri, and M. A. Hussain. 2019. “A Review on Modeling and Control of Olefin Polymerization in Fluidized-Bed Reactors.” Reviews in Chemical Engineering 35: 311–33. https://doi.org/10.1515/revce-2017-0040.

2. Agrawal, N., G. P. Rangaiah, A. K. Ray, and S. K. Gupta. 2007. “Design Stage Optimization of an Industrial Low-Density Polyethylene Tubular Reactor for Multiple Objectives Using NSGA-II and its Jumping Gene Adaptations.” Chemical Engineering Science 62: 2346–65. https://doi.org/10.1016/j.ces.2007.01.030.

3. Agrawal, N., G. P. Rangaiah, A. K. Ray, and S. K. Gupta. 2006. “Multi-objective Optimization of the Operation of an Industrial Low-Density Polyethylene Tubular Reactor Using Genetic Algorithm and its Jumping Gene Adaptations.” Industrial & Engineering Chemistry Research 45 (9): 3182–99. https://doi.org/10.1021/ie050977i.

4. Atan, M. F., M. A. Hussain, M. R. Abbasi, M. J. H. Khan, and M. F. A. Patah. 2019. “Advances in Mathematical Modeling of Gas-Phase Olefin Polymerization.” Processes 7: 1–33. https://doi.org/10.3390/pr7020067.

5. Casalini, T., F. Visscher, E. Janssen, F. Bertola, G. Storti, and M. Morbidelli. 2017. “Modeling of Polyolefin Polymerization in Semibatch Slurry Reactors: Experiments and Simulations.” Macromolecular Reaction Engineering 11: 1–19. https://doi.org/10.1002/mren.201600036.

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