Development of a framework for sequential Bayesian design of experiments: Application to a pilot-scale solvent-based CO2 capture process
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DOI: 10.1016/j.apenergy.2020.114533
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References listed on IDEAS
- Rubén M. Montañés & Nina E. Flø & Lars O. Nord, 2017. "Dynamic Process Model Validation and Control of the Amine Plant at CO 2 Technology Centre Mongstad," Energies, MDPI, vol. 10(10), pages 1-36, October.
- Sipöcz, Nikolett & Tobiesen, Finn Andrew & Assadi, Mohsen, 2011. "The use of Artificial Neural Network models for CO2 capture plants," Applied Energy, Elsevier, vol. 88(7), pages 2368-2376, July.
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- Ryan, Elizabeth G. & Drovandi, Christopher C. & Pettitt, Anthony N., 2015. "Simulation-based fully Bayesian experimental design for mixed effects models," Computational Statistics & Data Analysis, Elsevier, vol. 92(C), pages 26-39.
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Cited by:
- Kim, Jeongnam & Na, Jonggeol & Kim, Kyeongsu & Bak, Ji Hyun & Lee, Hyunjoo & Lee, Ung, 2021. "Learning the properties of a water-lean amine solvent from carbon capture pilot experiments," Applied Energy, Elsevier, vol. 283(C).
- Yang, Qiulian & Li, Haitao & Wang, Dong & Zhang, Xiaochun & Guo, Xiangqian & Pu, Shaochen & Guo, Ruixin & Chen, Jianqiu, 2020. "Utilization of chemical wastewater for CO2 emission reduction: Purified terephthalic acid (PTA) wastewater-mediated culture of microalgae for CO2 bio-capture," Applied Energy, Elsevier, vol. 276(C).
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Keywords
Design of experiment; Bayesian; Sequential; Pilot plant; CO2 capture; MEA;All these keywords.
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