화학공학소재연구정보센터
학회 한국공업화학회
학술대회 2019년 봄 (05/01 ~ 05/03, 부산 벡스코(BEXCO))
권호 23권 1호
발표분야 (화학공정) 4차산업 혁명 시대의 공정시스템기술 적용
제목 Gaussian process bayesian optimization and parameter estimation of water lean CO2 capture pilot plant
초록 Herein, we present a surogate model based bayesian optimization and parameter estimation for a CO2 capture process with a new water lean solvent. The water lean solvent, K2Sol, is a sterically hindered diamine and the hindered amine site makes K2Sol easily forms bicarbonate resulting the high absorption capacity. The surogate model based optimization and parameter estimation are carried out according to input and output relationship of experiments, thus expensive optimization model construction and property estimation can be avoided. According to the pilot plant experiment, the optimum regeneration energy of Monoethanolamine (MEA) and K2Sol respectively shows 4.3 and 2.8 GJ/tCO2 indicating that K2Sol requires only 65% of regeneration energy of MEA.
저자 이웅, 나종걸, 김정남, 이희원, 이현주
소속 한국과학기술(연)
키워드 CO2 capture; Water lean amine solvent; Gaussian process; Optimization; Parameter Estimation
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