Dynamic simulation and control of two-series industrial reactors producing linear low-density polyethylene

Kazerooni, Nooshin Moradi; Eslamloueyan, Reza; Biglarkhani, Mehdi

doi:10.1007/s40090-019-0177-4

Cited by 6 publications

(2 citation statements)

References 28 publications

(32 reference statements)

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“…However, while the steady-state defines the baseline operation, [95] in real life a plant alternates between steady-state and unsteady state (start-up, shut-down, feed composition variations, for example). [96][97][98] For this reason, we recommend understanding the dynamics of all the operations in the simulation before integrating energy.…”

Section: Energy Integration Intensificationmentioning

confidence: 99%

Experimental methods in chemical engineering: Process simulation

et al. 2020

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Process simulation software designs equipment, simulates operations, optimizes a plant's configuration (heat exchangers network, for example), estimates operating and capital expenses, and serves as an educational tool. However, mastering the theoretical background minimizes common mistakes such as applying an incorrect thermodynamic method, selecting improper algorithms in the case of tear systems, and setting irrational system specifications. Engineers and researchers will exploit this tool more often in the future as constant advancements in simulation science as well as new models are released continually. Process simulators make it easier to build digital twins and thus will facilitate the implementation of the industry 4.0 guidelines. We highlight the mathematical and technical features of process simulators, as well as the capabilities and the fields of application. A bibliometric map of keywords from articles citing Aspen+, Aspen plus, Hysys, and Pro/II indexed by Web of Science between 2017 and 2020 identified the main research clusters, such as design, optimization, energy or exergy, biomass; H 2 and CO 2 capture, thermodynamics; and separations and techno-economic analysis.

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Section: Energy Integration Intensificationmentioning

confidence: 99%

Experimental methods in chemical engineering: Process simulation

et al. 2020

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“…However, adjusting the hydrogen feeding rate to regulate the chain structure of polyethylene is crucial. Some research has also explored the operation of polymerization to regulate the chain structure of polyethylene [ 31 , 32 , 33 ]. By addressing concerns related to stability, we do not only ensure the consistent production of high-quality polyethylene but also contribute to the economic viability and environmental sustainability of the polymerization process [ 34 , 35 , 36 ].…”

Section: Introductionmentioning

confidence: 99%

Transient Behavior and Control of Polyethylene Production in a Fluidized Bed Reactor Utilizing Population Balance Model

Ghasem

2024

IJMS

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In this study, a fluidized bed reactor for polyethylene production was employed using a dry mode approach, where the recycle stream may contain components of a nature that cannot be condensed through standard cooling. To analyze the behavior of the fluidized bed reactors during the copolymerization of ethylene with butene, a dynamic population balance model was employed. The study includes sensitivity analyses through computer simulations to examine the variations in reactor temperature, molecular weights, catalyst feed rate, and monomer/comonomer concentrations in the fluidized bed reactor. It is noteworthy that the reactor exhibits instability under normal operational conditions and is sensitive to changes in the catalyst feed rate and coolant temperature of the heat exchanger. The findings also highlight challenges such as temperature fluctuations above the polymer melting point. This underscores the importance of implementing a temperature control system to prevent issues like reactor shutdown due to elevated temperatures. Dynamic instabilities were observed under specific circumstances and were successfully controlled using Proportional Integral Derivative (PID) control strategies. The population balance model is essential for understanding the complexity of transient polymerization reactions. It enables researchers to simulate and optimize polymerization processes by utilizing the detailed kinetics of the reaction.

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