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Fast robust optimization of ORC based on an artificial neural network for waste heat recovery

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  • Wu, Xialai
  • Lin, Ling
  • Xie, Lei
  • Chen, Junghui
  • Shan, Lu

Abstract

Uncertainty in the organic Rankine cycle (ORC) system and the highly complex ORC models pose challenges to optimal operation. A data-driven robust parametric optimization for the ORC system is proposed to ensure high and stable performance under uncertainty. The uncertainty of the cyclic variables is estimated by their distribution parameters, and the average (expected) thermodynamic performance (the net output power of the ORC) is maximized as the optimization objective while minimizing its variance. An artificial neural network with the rectified linear unit is used as a surrogate model for optimization. Then the robust parametric optimization problem can be transformed into a mixed-integer linear optimization problem with a chance-constrained form, and the robust optimal solution can be solved quickly. Case studies show that the solution time of the robust optimization problem using the proposed method is about 1.4 s, which is much less than obtaining the optimal solution based on ORC mechanism models. Meanwhile, the optimal operating condition derived by the proposed robust optimization approach outperforms that obtained by the traditional deterministic strategy. The proposed approach not only improves the robustness of the system but also demonstrates the importance of considering uncertainty in parametric optimization.

Suggested Citation

  • Wu, Xialai & Lin, Ling & Xie, Lei & Chen, Junghui & Shan, Lu, 2024. "Fast robust optimization of ORC based on an artificial neural network for waste heat recovery," Energy, Elsevier, vol. 301(C).
  • Handle: RePEc:eee:energy:v:301:y:2024:i:c:s0360544224014257
    DOI: 10.1016/j.energy.2024.131652
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    References listed on IDEAS

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    1. Osuolale, Funmilayo N. & Zhang, Jie, 2016. "Energy efficiency optimisation for distillation column using artificial neural network models," Energy, Elsevier, vol. 106(C), pages 562-578.
    2. Zhang, Tao & Ma, Junhua & Zhou, Yanglin & Wang, Yongzhen & Chen, Qifang & Li, Xiaoping & Liu, Liuchen, 2021. "Thermo-economic analysis and optimization of ICE-ORC systems based on a splitter regulation," Energy, Elsevier, vol. 226(C).
    3. Wu, Xialai & Chen, Junghui & Xie, Lei, 2018. "Integrated operation design and control of Organic Rankine Cycle systems with disturbances," Energy, Elsevier, vol. 163(C), pages 115-129.
    4. Bo Pang & Erik Nijkamp & Ying Nian Wu, 2020. "Deep Learning With TensorFlow: A Review," Journal of Educational and Behavioral Statistics, , vol. 45(2), pages 227-248, April.
    5. Feng, Yongqiang & Hung, TzuChen & Zhang, Yaning & Li, Bingxi & Yang, Jinfu & Shi, Yang, 2015. "Performance comparison of low-grade ORCs (organic Rankine cycles) using R245fa, pentane and their mixtures based on the thermoeconomic multi-objective optimization and decision makings," Energy, Elsevier, vol. 93(P2), pages 2018-2029.
    6. Ping, Xu & Yang, Fubin & Zhang, Hongguang & Zhang, Jian & Zhang, Wujie & Song, Gege, 2021. "Introducing machine learning and hybrid algorithm for prediction and optimization of multistage centrifugal pump in an ORC system," Energy, Elsevier, vol. 222(C).
    7. Serafino, Aldo & Obert, Benoit & Vergé, Léa & Cinnella, Paola, 2020. "Robust optimization of an organic Rankine cycle for geothermal application," Renewable Energy, Elsevier, vol. 161(C), pages 1120-1129.
    8. Ping, Xu & Yang, Fubin & Zhang, Hongguang & Xing, Chengda & Yao, Baofeng & Wang, Yan, 2022. "An outlier removal and feature dimensionality reduction framework with unsupervised learning and information theory intervention for organic Rankine cycle (ORC)," Energy, Elsevier, vol. 254(PB).
    9. Witanowski, Łukasz & Klonowicz, Piotr & Lampart, Piotr & Ziółkowski, Paweł, 2023. "Multi-objective optimization of the ORC axial turbine for a waste heat recovery system working in two modes: cogeneration and condensation," Energy, Elsevier, vol. 264(C).
    10. Xu, Bin & Li, Xiaoya, 2021. "A Q-learning based transient power optimization method for organic Rankine cycle waste heat recovery system in heavy duty diesel engine applications," Applied Energy, Elsevier, vol. 286(C).
    11. Yang, Min-Hsiung, 2016. "Optimizations of the waste heat recovery system for a large marine diesel engine based on transcritical Rankine cycle," Energy, Elsevier, vol. 113(C), pages 1109-1124.
    12. Zhou, Jianzhao & Chu, Yin Ting & Ren, Jingzheng & Shen, Weifeng & He, Chang, 2023. "Integrating machine learning and mathematical programming for efficient optimization of operating conditions in organic Rankine cycle (ORC) based combined systems," Energy, Elsevier, vol. 281(C).
    13. Palagi, Laura & Sciubba, Enrico & Tocci, Lorenzo, 2019. "A neural network approach to the combined multi-objective optimization of the thermodynamic cycle and the radial inflow turbine for Organic Rankine cycle applications," Applied Energy, Elsevier, vol. 237(C), pages 210-226.
    Full references (including those not matched with items on IDEAS)

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