#6496. Artificial neural network grey-box model for design and optimization of 50 MWe-scale combined supercritical CO2 Brayton cycle-ORC coal-fired power plant

October 2026publication date
Proposal available till 10-05-2025
4 total number of authors per manuscript0 $

The title of the journal is available only for the authors who have already paid for
Journal’s subject area:
Nuclear Energy and Engineering;
Energy Engineering and Power Technology;
Fuel Technology;
Renewable Energy, Sustainability and the Environment;
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Abstract:
Supercritical CO2 (sCO2) Brayton cycle is a promising technology for coal-fired power generation with high efficiency and compact equipment size. However, its sophisticated construct and high-temperature waste heat rejection require a systematic design to maximize its performance. Herein, we develop a combined sCO2 Brayton cycle-organic Rankine cycle (ORC) design for coal-fired power plant. A novel glass-box model that considers the specific designs of sCO2 boiler, recuperators, coolers, and turbomachinery is formulated to optimize the power plant. A high-accuracy artificial neural network model is also developed to estimate the systems pressure drop to reduce model complexity. As a result, the glass-box model is reformulated into a grey-box model. The model is applied to three different combined cycles’ design problem to evaluate their performance.
Keywords:
Artificial neural network; Coal-fired power generation; Organic Rankine cycle; sCO2 Brayton cycle

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