Generic AI models for mass transfer coefficient prediction in amine‐based CO2 absorber, Part II: RBFNN and RF model

• Published in: AIChE journal Vol. 69; no. 1
• Main Authors: Quan, Hong, Dong, Shoulong, Zhao, Dongfang, Li, Hansheng, Geng, Junming, Liu, Helei
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 01.01.2023; American Institute of Chemical Engineers
• Subjects: Absorbers; Absorption; Algorithms; Amines; Artificial neural networks; Back propagation; Carbon dioxide; Carbon sequestration; CO2 capture; Coefficients; machine learning (ML); Mass transfer; Mathematical models; Neural networks; Parameters; Radial basis function; radial basis function neural network (RBFNN); random forest (RF)
• ISSN: 0001-1541; 1547-5905
• DOI: 10.1002/aic.17904

In this work, the radial basis function neural network (RBFNN) and random forest (RF) algorithms were employed to develop generic AI models predicting mass transfer coefficient in amine‐based CO2 absorber. The models with operating parameters as input gave quite different prediction performance in different CO2 absorption systems. To secure better applicability, extra parameters related to amine type and packing characteristics were introduced to reasonably describe mass transfer behaviors, respectively. Moreover, the generic models were proposed by considering all influencing factors of mass transfer in CO2 absorber column. Furthermore, the performance of BPNN, RBFNN, and RF models was completely compared and fully discussed in terms of AARE. All three generic models could predict mass transfer coefficient of CO2 absorber very well. It was found that the BPNN models provide the best predication with AAREs of below 5%. The developed generic model could serve as a fast and efficient tool for preliminary selection and evaluation of potential amines for CO2 absorption. The framework of generic ML model development was also clearly presented, which could provide theoretical basis and practical guidance for the implementation and application of ML models in the carbon capture field.