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Modeling and optimization of a utility system containing multiple extractions steam turbines

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  • Luo, Xianglong
  • Zhang, Bingjian
  • Chen, Ying
  • Mo, Songping

Abstract

Complex turbines with multiple controlled and/or uncontrolled extractions are popularly used in the processing industry and cogeneration plants to provide steam of different levels, electric power, and driving power. To characterize thermodynamic behavior under varying conditions, nonlinear mathematical models are developed based on energy balance, thermodynamic principles, and semi-empirical equations. First, the complex turbine is decomposed into several simple turbines from the controlled extraction stages and modeled in series. THM (The turbine hardware model) developing concept is applied to predict the isentropic efficiency of the decomposed simple turbines. Stodola’s formulation is also used to simulate the uncontrolled extraction steam parameters. The thermodynamic properties of steam and water are regressed through linearization or piece-wise linearization. Second, comparison between the simulated results using the proposed model and the data in the working condition diagram provided by the manufacturer is conducted over a wide range of operations. The simulation results yield small deviation from the data in the working condition diagram where the maximum modeling error is 0.87% among the compared seven operation conditions. Last, the optimization model of a utility system containing multiple extraction turbines is established and a detailed case is analyzed. Compared with the conventional operation strategy, a maximum of 5.47% of the total operation cost is saved using the proposed optimization model.

Suggested Citation

  • Luo, Xianglong & Zhang, Bingjian & Chen, Ying & Mo, Songping, 2011. "Modeling and optimization of a utility system containing multiple extractions steam turbines," Energy, Elsevier, vol. 36(5), pages 3501-3512.
    • Handle: RePEc:eee:energy:v:36:y:2011:i:5:p:3501-3512
    DOI: 10.1016/j.energy.2011.03.056
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    2. Zhu, Qiannan & Luo, Xianglong & Zhang, Bingjian & Chen, Ying & Mo, Songping, 2016. "Mathematical modeling, validation, and operation optimization of an industrial complex steam turbine network-methodology and application," Energy, Elsevier, vol. 97(C), pages 191-213.
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    4. Luo, Xianglong & Huang, Xiaojian & El-Halwagi, Mahmoud M. & Ponce-Ortega, José María & Chen, Ying, 2016. "Simultaneous synthesis of utility system and heat exchanger network incorporating steam condensate and boiler feedwater," Energy, Elsevier, vol. 113(C), pages 875-893.
    5. Zhang, B.J. & Liu, K. & Luo, X.L. & Chen, Q.L. & Li, W.K., 2015. "A multi-period mathematical model for simultaneous optimization of materials and energy on the refining site scale," Applied Energy, Elsevier, vol. 143(C), pages 238-250.
    6. Huang, Xiaojian & Luo, Xianglong & Chen, Jianyong & Yang, Zhi & Chen, Ying & María Ponce-Ortega, José & El-Halwagi, Mahmoud M., 2018. "Synthesis and dual-objective optimization of industrial combined heat and power plants compromising the water–energy nexus," Applied Energy, Elsevier, vol. 224(C), pages 448-468.
    7. Luo, Xianglong & Zhang, Bingjian & Chen, Ying & Mo, Songping, 2012. "Operational planning optimization of multiple interconnected steam power plants considering environmental costs," Energy, Elsevier, vol. 37(1), pages 549-561.
    8. Yu Huang & Weizhen Hou & Yiran Huang & Jiayu Li & Qixian Li & Dongfeng Wang & Yan Zhang, 2020. "Multi-Objective Optimal Operation for Steam Power Scheduling Based on Economic and Exergetic Analysis," Energies, MDPI, vol. 13(8), pages 1-18, April.
    9. Dong-Jin Cho & Eul-Bum Lee & Jae-Min Cho & Douglas Alleman, 2019. "Reducing the Superheating of Extraction Stream on Advanced-Ultra Super Critical Power Plants with Regenerative Turbines in South Korea: An Economic Analysis," Energies, MDPI, vol. 12(9), pages 1-22, May.
    10. Shen, Feifei & Zhao, Liang & Wang, Meihong & Du, Wenli & Qian, Feng, 2022. "Data-driven adaptive robust optimization for energy systems in ethylene plant under demand uncertainty," Applied Energy, Elsevier, vol. 307(C).
    11. Luo, Xianglong & Hu, Jiahao & Zhao, Jun & Zhang, Bingjian & Chen, Ying & Mo, Songping, 2014. "Multi-objective optimization for the design and synthesis of utility systems with emission abatement technology concerns," Applied Energy, Elsevier, vol. 136(C), pages 1110-1131.
    12. Voll, Philip & Jennings, Mark & Hennen, Maike & Shah, Nilay & Bardow, André, 2015. "The optimum is not enough: A near-optimal solution paradigm for energy systems synthesis," Energy, Elsevier, vol. 82(C), pages 446-456.
    13. Kostelac, Matija & Pavić, Ivan & Zhang, Ning & Capuder, Tomislav, 2022. "Uncertainty modelling of an industry facility as a multi-energy demand response provider," Applied Energy, Elsevier, vol. 307(C).
    14. Mardan, Nawzad & Klahr, Roger, 2012. "Combining optimisation and simulation in an energy systems analysis of a Swedish iron foundry," Energy, Elsevier, vol. 44(1), pages 410-419.
    15. Luo, Xianglong & Zhang, Bingjian & Chen, Ying & Mo, Songping, 2013. "Operational planning optimization of steam power plants considering equipment failure in petrochemical complex," Applied Energy, Elsevier, vol. 112(C), pages 1247-1264.
    16. Ligang Wang & Zhiping Yang & Shivom Sharma & Alberto Mian & Tzu-En Lin & George Tsatsaronis & François Maréchal & Yongping Yang, 2018. "A Review of Evaluation, Optimization and Synthesis of Energy Systems: Methodology and Application to Thermal Power Plants," Energies, MDPI, vol. 12(1), pages 1-53, December.
    17. Wang, Ligang & Voll, Philip & Lampe, Matthias & Yang, Yongping & Bardow, André, 2015. "Superstructure-free synthesis and optimization of thermal power plants," Energy, Elsevier, vol. 91(C), pages 700-711.
    18. Zhao, Tian & Chen, Xi & He, Ke-Lun & Chen, Qun, 2021. "A standardized modeling strategy for heat current method-based analysis and simulation of thermal systems," Energy, Elsevier, vol. 217(C).
    19. Han, Yulin & Zheng, Jingyuan & Luo, Xiaoyan & Qian, Yu & Yang, Siyu, 2023. "Multi-scenario data-driven robust optimisation for industrial steam power systems under uncertainty," Energy, Elsevier, vol. 263(PD).

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