Plant-level dynamic optimization of Cryogenic Carbon Capture with conventional and renewable power sources
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DOI: 10.1016/j.apenergy.2015.03.100
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- Song, Chunfeng & Liu, Qingling & Ji, Na & Deng, Shuai & Zhao, Jun & Li, Yang & Kitamura, Yutaka, 2017. "Reducing the energy consumption of membrane-cryogenic hybrid CO2 capture by process optimization," Energy, Elsevier, vol. 124(C), pages 29-39.
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- Song, Chunfeng & Liu, Qingling & Deng, Shuai & Li, Hailong & Kitamura, Yutaka, 2019. "Cryogenic-based CO2 capture technologies: State-of-the-art developments and current challenges," Renewable and Sustainable Energy Reviews, Elsevier, vol. 101(C), pages 265-278.
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- Kang, Charles A. & Brandt, Adam R. & Durlofsky, Louis J. & Jayaweera, Indira, 2016. "Assessment of advanced solvent-based post-combustion CO2 capture processes using a bi-objective optimization technique," Applied Energy, Elsevier, vol. 179(C), pages 1209-1219.
- Shuyu Dai & Dongxiao Niu & Yaru Han, 2018. "Forecasting of Energy-Related CO 2 Emissions in China Based on GM(1,1) and Least Squares Support Vector Machine Optimized by Modified Shuffled Frog Leaping Algorithm for Sustainability," Sustainability, MDPI, vol. 10(4), pages 1-17, March.
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- Menglu Li & Wei Wang & Gejirifu De & Xionghua Ji & Zhongfu Tan, 2018. "Forecasting Carbon Emissions Related to Energy Consumption in Beijing-Tianjin-Hebei Region Based on Grey Prediction Theory and Extreme Learning Machine Optimized by Support Vector Machine Algorithm," Energies, MDPI, vol. 11(9), pages 1-15, September.
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Keywords
Cryogenic Carbon Capture; Fossil-fueled power production; Renewable energy generation; Dynamic optimization;All these keywords.
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