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Comparison between differential evolution algorithms and response surface methodology in ethylene plant optimization based on an extended combined energy - exergy analysis

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  • Jahromi, Farid Sadeghian
  • Beheshti, Masoud
  • Rajabi, Razieh Fereydon

Abstract

This paper applies an extend energy - exergy analysis as a strategy for evaluating the performance of different types of distillation columns in ethylene production process by using industrial data. Regarding to the limitation and deficiency of energy – exergy combination, a new method named exergy destruction level (EDL) and conceptual diagram based on equipment target value is proposed for process equipment with pressure and chemical composition changes. The effects of different operational parameters on the component separation are evaluated by sensitivity analysis. Eventually response surface methodology (RSM) and artificial intelligence (DE) method are developed for optimization of the chemical plant. Comparing obtained results from ethylene plant optimization by using RSM and DE, it was found that annual profit percentage with DE method is 61.6% more than RSM method. Also, results of optimization showed that the most effective operating parameters consist of feed stream temperature, boil-up ratio, reflux ratio, column pressure and feed stage. It was observed that utilities and refrigeration cycle consumption work have been declined significantly by using DE optimizer and EDL analysis (12.6% and 11.6%) compared to RSM optimizer and exergy analysis (11.9% and 4.8%). It is obvious that, EDL analysis and DE method expedite process optimization and provide more precise analysis than conventional energy-exergy analysis.

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  • Jahromi, Farid Sadeghian & Beheshti, Masoud & Rajabi, Razieh Fereydon, 2018. "Comparison between differential evolution algorithms and response surface methodology in ethylene plant optimization based on an extended combined energy - exergy analysis," Energy, Elsevier, vol. 164(C), pages 1114-1134.
    • Handle: RePEc:eee:energy:v:164:y:2018:i:c:p:1114-1134
    DOI: 10.1016/j.energy.2018.09.059
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    2. Mochen Liao & Kai Lan & Yuan Yao, 2022. "Sustainability implications of artificial intelligence in the chemical industry: A conceptual framework," Journal of Industrial Ecology, Yale University, vol. 26(1), pages 164-182, February.
    3. Song, Dan & Lin, Ling & Wu, Ye, 2019. "Extended exergy accounting for a typical cement industry in China," Energy, Elsevier, vol. 174(C), pages 678-686.
    4. Francisco Amaral & Alex Santos & Ewerton Calixto & Fernando Pessoa & Delano Santana, 2020. "Exergetic Evaluation of an Ethylene Refrigeration Cycle," Energies, MDPI, vol. 13(14), pages 1-21, July.
    5. Xiang, Dong & Huang, Weiqing & Huang, Peng, 2018. "A novel coke-oven gas-to-natural gas and hydrogen process by integrating chemical looping hydrogen with methanation," Energy, Elsevier, vol. 165(PB), pages 1024-1033.

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