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Decision tree‐based modeling of CO 2 equilibrium absorption in different aqueous solutions of absorbents
Author(s) -
Yarveicy Hamidreza,
Saghafi Hamidreza,
Ghiasi Mohammad M.,
Mohammadi Amir H.
Publication year - 2019
Publication title -
environmental progress and sustainable energy
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.495
H-Index - 66
eISSN - 1944-7450
pISSN - 1944-7442
DOI - 10.1002/ep.13128
Subject(s) - artificial neural network , adaptive neuro fuzzy inference system , multilayer perceptron , solubility , radial basis function , aqueous solution , decision tree , approximation error , biological system , support vector machine , least squares support vector machine , mathematics , computer science , fuzzy logic , chemistry , machine learning , artificial intelligence , fuzzy control system , biology , organic chemistry
In absorption‐based CO 2 removal processes, one of the most important parameters that must be determined is the equilibrium solubility of CO 2 in solvents. This study is the first time to employ the extremely randomized trees, Extra Trees, methodology to develop an Extra Trees model for the CO 2 loading capacity of various absorbents. The ability of the proposed decision tree‐based model in estimation of CO 2 solubility values was compared to that of the previously presented models on the basis of the adaptive neuro‐fuzzy inference system (ANFIS), least squares version of the support vector machine (LSSVM), and artificial neural network (ANN), using error analysis. According to the obtained results, the presented Extra Trees model is able to estimate the CO 2 loading capacity of solvents with an absolute relative deviation in percent (AARD%) equal to 0.15. The calculated values of AARD% for the literature models, i.e., LSSVM, radial basis function‐artificial neural network (RBF‐ANN), multilayer perceptron‐ANN (MLP‐ANN), and ANFIS, are 2.00, 10.03, 6.18, and 14.15, respectively. Hence, the developed Extra Trees model provides much higher robustness and accuracy in estimating/representing the solubility of CO 2 in different aqueous solutions of solvents. © 2019 American Institute of Chemical Engineers Environ Prog, 38: S441–S448, 2019