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Multiple processes modeling and identification for a cleaner supercritical power plant via Grey Wolf Optimizer

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  • Al-Momani, Ahmad
  • Mohamed, Omar
  • Abu Elhaija, Wejdan

Abstract

Validated mathematical modeling of clean coal power generation technologies is an essential subject of research that has a realized importance in the scientific communities working on the energy production and environment protection. This study presents a complete multiple processes modeling and simulation of a practical cleaner coal supercritical power plant (SCPP). The model covers a wider range of operation than ever published models before, which focus either on startup or on once-through operation. The model in this paper rather embeds the whole journey from recirculation mode during startup process up to the maximum produced power, then to emergency shut-down process in the same model. It has been found that slight adaptations in the once-through model parameters are sufficient to switch from one mode of process to another, which is useful in retaining simplified structure of the model. The fixed parameters of the model have been optimized by modern meta-heuristic technique of Grey Wolf Optimizer (GWO) and compared with Genetic Algorithms (GA). It has been proved that GWO has a sort of superiority over the GA for parameter identification of the SCPP model.

Suggested Citation

  • Al-Momani, Ahmad & Mohamed, Omar & Abu Elhaija, Wejdan, 2022. "Multiple processes modeling and identification for a cleaner supercritical power plant via Grey Wolf Optimizer," Energy, Elsevier, vol. 252(C).
    • Handle: RePEc:eee:energy:v:252:y:2022:i:c:s0360544222009938
    DOI: 10.1016/j.energy.2022.124090
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    1. Hou, Guolian & Huang, Ting & Huang, Congzhi, 2023. "Flexibility improvement of 1000 MW ultra-supercritical unit under full operating conditions by error-based ADRC and fast pigeon-inspired optimizer," Energy, Elsevier, vol. 270(C).
    2. Ghandehariun, Samane & Ghandehariun, Amir M. & Ziabari, Nima Bahrami, 2023. "Performance prediction and optimization of a hybrid renewable-energy-based multigeneration system using machine learning," Energy, Elsevier, vol. 282(C).
    3. Andrés Meana-Fernández & Juan M. González-Caballín & Roberto Martínez-Pérez & Francisco J. Rubio-Serrano & Antonio J. Gutiérrez-Trashorras, 2022. "Power Plant Cycles: Evolution towards More Sustainable and Environmentally Friendly Technologies," Energies, MDPI, vol. 15(23), pages 1-27, November.
    4. Mohammad Qasem & Omar Mohamed & Wejdan Abu Elhaija, 2022. "Parameter Identification and Sliding Pressure Control of a Supercritical Power Plant Using Whale Optimizer," Sustainability, MDPI, vol. 14(13), pages 1-25, June.
    5. Opriș, Ioana & Cenușă, Victor-Eduard, 2023. "Parametric and heuristic optimization of multiple schemes with double-reheat ultra-supercritical steam power plants," Energy, Elsevier, vol. 266(C).
    6. Yu, Jianxi & Petersen, Nils & Liu, Pei & Li, Zheng & Wirsum, Manfred, 2022. "Hybrid modelling and simulation of thermal systems of in-service power plants for digital twin development," Energy, Elsevier, vol. 260(C).
    7. Yin, Chen & Mao, Shuhua, 2023. "Fractional multivariate grey Bernoulli model combined with improved grey wolf algorithm: Application in short-term power load forecasting," Energy, Elsevier, vol. 269(C).

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