Generic AI models for mass transfer coefficient prediction in amine-based CO₂ absorber, Part I: BPNN model

Accurate and reliable prediction of mass transfer coefficient is critical to evaluate the mass transfer performance of amine-based carbon capture process. This work aims to establish a generic mass transfer model applicable to CO₂ absorption into amine solutions in absorber columns. A series of back-propagation neural network (BPNN) models were established based on the experimental data of 23 amine-based systems. The BPNN models trained for a specific CO₂ absorption process matched well with the experimental data with all the AAREs of below 4%. Meanwhile, models with better applicability were proposed and established by introducing extra parameters related to amine properties or column characteristics to better describe and predict the mass transfer behavior in different amine-based systems. Particularly, the generic mass transfer coefficient model was developed with the consideration of all influencing factors including operating conditions, amine properties, and packing characteristics, to achieve the accurate prediction of mass transfer coefficient of CO₂ absorption into amine solutions in packed columns. The developed generic model could serve as a fast and efficient tool for preliminary selection and evaluation of potential amines by presenting the mass transfer performance in CO₂ absorber.