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Estimation of carbon dioxide equilibrium adsorption isotherms using adaptive neuro‐fuzzy inference systems (ANFIS) and regression models
Author(s) -
Saghafi Hamidreza,
Arabloo Milad
Publication year - 2017
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.12581
Subject(s) - microporous material , adaptive neuro fuzzy inference system , adsorption , fuzzy logic , computer science , fuzzy inference system , process engineering , materials science , artificial intelligence , chemistry , fuzzy control system , engineering , composite material
Removal of CO 2 from industrial facilities such as refineries and power plants for emission reduction has attracted considerable interest. Among currently used CO 2 capturing processes, the use of microporous solids is considered to be one of the most promising approaches. A description of the CO 2 adsorption on microporous material focuses on some captivating problems of present adsorption studies. In present study, robust and accurate methods are designed for the estimation of CO 2 adsorption onto microporous solids as a function of CO 2 partial pressure and temperature. The performance and accuracy of the developed models were tested and validated by their ability to predict the literature data. It was concluded that the designed adaptive neuro‐fuzzy inference systems (ANFIS) models and mathematical equations demonstrated a superior predictive performance on estimation of CO 2 adsorption data over well‐known classic adsorption isotherms. Findings of present study indicate that the neuro‐fuzzy modeling approach is a practicable method for analysis and design of CO 2 separation and purification technology. © 2017 American Institute of Chemical Engineers Environ Prog, 36: 1374–1382, 2017