Operation characteristics and performance prediction of a 3 kW organic Rankine cycle (ORC) with automatic control system based on machine learning methodology
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DOI: 10.1016/j.energy.2022.125857
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Cited by:
- Lu, Pei & Chen, Kaihuang & Luo, Xianglong & Wu, Wei & Liang, Yingzong & Chen, Jianyong & Chen, Ying, 2024. "Experimental and simulation study on a zeotropic ORC system using R1234ze(E)/R245fa as working fluid," Energy, Elsevier, vol. 292(C).
- Davide Di Battista & Roberto Cipollone, 2023. "Waste Energy Recovery and Valorization in Internal Combustion Engines for Transportation," Energies, MDPI, vol. 16(8), pages 1-28, April.
- Attila R. Imre & Sindu Daniarta & Przemysław Błasiak & Piotr Kolasiński, 2023. "Design, Integration, and Control of Organic Rankine Cycles with Thermal Energy Storage and Two-Phase Expansion System Utilizing Intermittent and Fluctuating Heat Sources—A Review," Energies, MDPI, vol. 16(16), pages 1-25, August.
- Zhang, Yuan & Wu, Xiaocheng & Tian, Zhen & Gao, Wenzhong & Peng, Hao & Yang, Ke, 2023. "Comparison of random forest, support vector regression, and long short term memory for performance prediction and optimization of a cryogenic organic rankine cycle (ORC)," Energy, Elsevier, vol. 280(C).
- Tao, Hai & Alawi, Omer A. & Kamar, Haslinda Mohamed & Nafea, Ahmed Adil & AL-Ani, Mohammed M. & Abba, Sani I. & Salami, Babatunde Abiodun & Oudah, Atheer Y. & Mohammed, Mustafa K.A., 2024. "Development of integrative data intelligence models for thermo-economic performances prediction of hybrid organic rankine plants," Energy, Elsevier, vol. 292(C).
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Keywords
Organic Rankine cycle (ORC); Automatic control system; Machine learning methodology; Thermal efficiency; Multi-objective optimization;All these keywords.
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