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

Shoulong Dong, Hong Quan, Dongfang Zhao, Hansheng Li, Junming Geng, Helei Liu*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

24 Citations (Scopus)

Abstract

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.

Original languageEnglish
Article number118165
JournalChemical Engineering Science
Volume264
DOIs
Publication statusPublished - 31 Dec 2022

Keywords

  • Absorption
  • Artificial intelligence (AI)
  • Back-propagation neural network (BPNN)
  • CO capture
  • Mass transfer

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