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Multi-stage stochastic programming for integrated optimization of ethylene production processes and utility systems under uncertainty

Author

Listed:
  • Zhao, Liang
  • Rong, Jiyun
  • Ma, Guofu
  • Liang, Chen
  • Long, Jian

Abstract

Multiple time-scale uncertainties in equipment efficiency, process demand, and steam prices pose significant challenges to the modeling and optimization of ethylene production processes and utility systems. This paper introduces a novel multi-stage stochastic programming (MSSP) framework that integrates advanced linearization techniques and data augmentation via a Wasserstein generative adversarial network (WGAN), providing a robust solution for improving energy conversion efficiency and optimizing ethylene production processes with utility systems under uncertainty. A three-stage stochastic programming model addresses the impacts of uncertainties across different time scales. Nonlinear terms in the model are linearized using the McCormick envelope method to simplify the solution process, and a WGAN generates simulated data to augment the dataset. The effectiveness of the proposed method was validated in a real-world ethylene plant case study, where optimization results showed a 3.9 % increase in net profit, a 3.5 % increase in ethylene yield, and a 6.5 % decrease in the cost of the utility system compared to traditional methods.

Suggested Citation

  • Zhao, Liang & Rong, Jiyun & Ma, Guofu & Liang, Chen & Long, Jian, 2025. "Multi-stage stochastic programming for integrated optimization of ethylene production processes and utility systems under uncertainty," Energy, Elsevier, vol. 320(C).
    • Handle: RePEc:eee:energy:v:320:y:2025:i:c:s0360544225009375
    DOI: 10.1016/j.energy.2025.135295
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    References listed on IDEAS

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    1. Geng, Zhiqiang & Li, Hongda & Zhu, Qunxiong & Han, Yongming, 2018. "Production prediction and energy-saving model based on Extreme Learning Machine integrated ISM-AHP: Application in complex chemical processes," Energy, Elsevier, vol. 160(C), pages 898-909.
    2. R. T. Rockafellar & Roger J.-B. Wets, 1991. "Scenarios and Policy Aggregation in Optimization Under Uncertainty," Mathematics of Operations Research, INFORMS, vol. 16(1), pages 119-147, February.
    3. Zhao, Liang & You, Fengqi, 2019. "A data-driven approach for industrial utility systems optimization under uncertainty," Energy, Elsevier, vol. 182(C), pages 559-569.
    4. Li, Hanxiu & Zhao, Liang, 2023. "Life cycle assessment and multi-objective optimization for industrial utility systems," Energy, Elsevier, vol. 280(C).
    5. Gong, Shixin & Shao, Cheng & Zhu, Li, 2017. "Energy efficiency evaluation in ethylene production process with respect to operation classification," Energy, Elsevier, vol. 118(C), pages 1370-1379.
    6. Wei Zhang & Kai Wang & Alexandre Jacquillat & Shuaian Wang, 2023. "Optimized Scenario Reduction: Solving Large-Scale Stochastic Programs with Quality Guarantees," INFORMS Journal on Computing, INFORMS, vol. 35(4), pages 886-908, July.
    7. Gong, Shixin, 2023. "Multi-scale energy efficiency recognition and diagnosis scheme for ethylene production based on a hierarchical multi-indicator system," Energy, Elsevier, vol. 267(C).
    8. Lu, Chang & Wu, Yuehui & Yu, Shanchuan, 2022. "A Sample Average Approximation Approach for the Stochastic Dial-A-Ride Problem on a Multigraph with User Satisfaction," European Journal of Operational Research, Elsevier, vol. 302(3), pages 1031-1044.
    9. George B. Dantzig, 1955. "Linear Programming under Uncertainty," Management Science, INFORMS, vol. 1(3-4), pages 197-206, 04-07.
    10. Tore Jonsbråten & Roger Wets & David Woodruff, 1998. "A class of stochastic programs withdecision dependent random elements," Annals of Operations Research, Springer, vol. 82(0), pages 83-106, August.
    11. Kim, Minsoo & Park, Taeseop & Jeong, Jaeik & Kim, Hongseok, 2023. "Stochastic optimization of home energy management system using clustered quantile scenario reduction," Applied Energy, Elsevier, vol. 349(C).
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