ANFIS modeling for prediction of CO2 solubility in potassium and sodium based amino acid Salt solutions

By E. Soroush, M. Mesbah, N. Hajilary, Mashallah Rezakazemi. Journal of Environmental Chemical Engineering, 2019

62 citations recorded by the source corpus.

Abstract Amino acids are considered as a new class of solvents, which can be used in CO2 capture processes. Designing such processes requires simple and precise models for predicting thermodynamic parameters. In this study, Adaptive Neuro-Fuzzy Inference System (ANFIS) was employed to develop a precise temperature dependent intelligent model for correlating the CO2 loading capacity of amino acid salt solutions for a wide range of amino acids. The validity, precision, and generality of the proposed model were verified by both graphical and statistical measures. Through outlier diagnostics, the statistical validity of the data set and applicability of the developed model was confirmed. At the end, a sensitivity analysis was performed to ensure that, the proposed ANFIS model mimics the sensitivity of the experimental data toward input variables. The results were satisfactory and proved that the developed ANFIS structure can be used for the precise estimation of CO2 loading capacities of amino acid salt solutions.

Subfields: Biomedical Engineering, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/3cc0a5c3754a7a0398b433e05d8643030d350523