Improved correlations for working fluid properties prediction and their application in performance evaluation of sub-critical Organic Rankine Cycle
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DOI: 10.1016/j.energy.2019.02.124
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Cited by:
- Qiang Liu & Ran Chen & Xinliu Yang & Xiao Xiao, 2023. "Thermodynamic Analyses of Sub- and Supercritical ORCs Using R1234yf, R236ea and Their Mixtures as Working Fluids for Geothermal Power Generation," Energies, MDPI, vol. 16(15), pages 1-22, July.
- Zhan, Taotao & Chen, Yuhang & Dong, Ao & He, Maogang & Zhang, Ying, 2023. "Intrinsic-group-contribution PC-SAFT and its application in performance analysis of high-temperature organic Rankine cycle with siloxanes and alkanes," Energy, Elsevier, vol. 278(PA).
- Ping, Xu & Yang, Fubin & Zhang, Hongguang & Xing, Chengda & Zhang, Wujie & Wang, Yan, 2022. "Evaluation of hybrid forecasting methods for organic Rankine cycle: Unsupervised learning-based outlier removal and partial mutual information-based feature selection," Applied Energy, Elsevier, vol. 311(C).
- Luo, Xianglong & Wei, Youxing & Qiu, Guanfu & Liang, Yingzong & Chen, Jianyong & Yang, Zhi & Wang, Chao & Chen, Ying, 2020. "Simultaneous design and off-design operation optimization of a waste heat-driven organic Rankine cycle using a multi-period mathematical programming method," Energy, Elsevier, vol. 213(C).
- 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).
- Mohan, Sooraj & Dinesha, P. & Campana, Pietro Elia, 2022. "ANN-PSO aided selection of hydrocarbons as working fluid for low-temperature organic Rankine cycle and thermodynamic evaluation of optimal working fluid," Energy, Elsevier, vol. 259(C).
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Keywords
ANN; GCM; CAMD; ORC; Working fluid;All these keywords.
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