2022
DOI: 10.1039/d2em00253a
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Prediction of biphasic separation in CO2 absorption using a molecular surface information-based machine learning model

Abstract: Carbon dioxide capture technologies have been focused to overcome global warming. A biphasic absorbent is one of the promising approaches for energy-saving CO2 capture process. This biphasic absorbent is mainly...

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“…The results showed that the accuracy of phase behavior prediction exceeded 80%. Kataoka et.al used a machine learning model based on molecular surface information to predict the phase change behavior of mixed solvents composed of alkylamine and ethylene glycol ether or alcohol before and after CO 2 absorption. In a data set containing 61 mixed solvents with alkylamine/ethylene glycol ether or alcohol, the accuracy of phase behavior prediction exceeded 90%. The above research still contributes to the development of SO 2 phase change absorbents.…”
Section: Absorbent Evaluationmentioning
confidence: 99%
“…The results showed that the accuracy of phase behavior prediction exceeded 80%. Kataoka et.al used a machine learning model based on molecular surface information to predict the phase change behavior of mixed solvents composed of alkylamine and ethylene glycol ether or alcohol before and after CO 2 absorption. In a data set containing 61 mixed solvents with alkylamine/ethylene glycol ether or alcohol, the accuracy of phase behavior prediction exceeded 90%. The above research still contributes to the development of SO 2 phase change absorbents.…”
Section: Absorbent Evaluationmentioning
confidence: 99%