Bayesian Inference of Aqueous Mineral Carbonation Kinetics for Carbon Capture and Utilization
- 주제(기타) Engineering, Chemical
- 설명문(일반) [Na, Jonggeol; Lee, Ung] KIST, Clean Energy Res Ctr, 5 Hwarang Ro 14 Gil, Seoul 02792, South Korea; [Park, Seongeon; Kim, Minjun; Lee, Dongwoo; Lee, Jong Min] Seoul Natl Univ, Sch Chem & Biol Engn, Gwanak Ro 1, Seoul 08826, South Korea; [Bak, Ji Hyun] KIAS, Sch Computat Sci, 85 Hoegi Ro, Seoul 02455, South Korea; [Yoo, Yunsung; Kim, Injun; Park, Jinwon] Yonsei Univ, Dept Chem & Biomol Engn, 50 Yonsei Ro, Seoul, South Korea
- 관리정보기술 faculty
- 등재 SCIE, SCOPUS
- 발행기관 AMER CHEMICAL SOC
- 발행년도 2019
- URI http://www.dcollection.net/handler/ewha/000000171990
- 본문언어 영어
- Published As https://dx.doi.org/10.1021/acs.iecr.9b01062
초록/요약
We develop a rigorous mathematical model of aqueous mineral carbonation kinetics for carbon capture and utilization (CCU) and estimate the parameter posterior distribution using Bayesian parameter estimation framework and lab-scale experiments. We conduct 16 experiments according to the orthogonal array design and an additional one experiment for the model test. The model considers the gas-liquid mass transfer, solid dissolution, ionic reactions, precipitations, and discrete events in the form of differential algebraic equations (DAEs). The Bayesian parameter estimation framework, which we distribute as a toolbox (https://github.com/jihyunbak/BayesChemEng), involves surrogate models, Markov chain Monte Carlo (MCMC) with tempering, global optimization, and various analysis tools. The obtained parameter distributions reflect the uncertain or multimodal natures of the parameters due to the incompleteness of the model and the experiments. They are used to earn stochastic model responses which show good fits with the experimental results. The fitting errors of all the 16 data sets and the unseen test set are measured to be comparable or lower than when deterministic optimization methods are used. The developed model is then applied to find out the operating conditions which increase the duration of high CO, removal rate and the carbonate production rate. They have highly nonlinear relationships with design variables such as the amounts of CaCO3 and NaOH, flue gas flow rate, and CO2 inlet concentration.
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