2021
DOI: 10.1016/j.jiec.2020.10.018
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Exergy analysis and multi-objective optimisation for energy system: a case study of a separation process in ethylene manufacturing

Abstract: In chemical industry, most processes face the challenge of high energy consumption.The approach presented in this study can reduce the energy footprint and increase efficiency. The energy system of a separation process in ethylene manufacturing is used to demonstrate the effectiveness of the approach. The chilling train system of the separation process in a typical ethylene plant consumes most cooling and provides appropriate feed for distillation columns. The steady state simulation of system was presented an… Show more

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Cited by 27 publications
(5 citation statements)
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References 51 publications
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“…Xu et al [13] proposed an optimisation model to optimise energy supply and demand strategies of an industrial park, where climate uncertainties and energy systems efficiency and stability have been considered. Shen et al [14] coupled performing exergy analysis and multi-objective optimisation to model a separation process in ethylene manufacturing and address the trade-…”
Section: Relevant Researchmentioning
confidence: 99%
“…Xu et al [13] proposed an optimisation model to optimise energy supply and demand strategies of an industrial park, where climate uncertainties and energy systems efficiency and stability have been considered. Shen et al [14] coupled performing exergy analysis and multi-objective optimisation to model a separation process in ethylene manufacturing and address the trade-…”
Section: Relevant Researchmentioning
confidence: 99%
“…The study revealed an average exergy efficiency of 91.88% and a notable improvement in the sustainability index from 3.45 to 4.45. Furthermore, in a recent study, Shen et al (2021) executed an exergy analysis of a separation process in ethylene production. They found that the heat exchanger component had the highest exergy destruction rate, with a value of 140.28 kW.…”
Section: Introductionmentioning
confidence: 99%
“…However, the solution of deterministic optimization may be infeasible, especially when the real demands are higher than the nominal ones. In this study, typical operational conditions during more than two years were collected from the industrial process historical database (PHD), and the corresponding process mechanical power demands of the three main compressors were calculated by using Aspen Plus ® [28]. Given that uncertain parameters cannot be predefined, a two-stage ARO framework for the operational optimization of the industrial multi-type energy system under demand uncertainty was proposed.…”
Section: Introductionmentioning
confidence: 99%