Development of ANFIS technique for estimation of CO2 solubility in amino acids and study on impact of input parameters

Abstract

ANFIS (Adaptive neuro fuzzy inference system) modeling of CO2 capture using chemical absorbent was carried out in this study to correlate the solubility of CO2 to the solvent and operational parameters. In the ANFIS model, the input parameters including temperature, pressure, and physio-chemical properties of the solvent were considered, while the loading of CO2 in the absorbent was considered as the sole target output to be predicted by the model. Indeed, we developed a machine learning based model for predicting the CO2 loading capacity in amino acid salt solutions as the chemical absorbent of carbon dioxide. This model uses a metaheuristic optimized ANFIS based on a wide range of amino acids. This study's novel part is the use of Differential Evolution (DE) and Firefly Algorithm (FA) metaheuristics in order to solve hyper-parameter tuning of ANFIS as an optimization problem based on differential evolution. Accordingly, the optimized ANFIS model has an R2 score of 0.9520 for the test data and a score of 0.9841 for the training data. This indicates that the proposed model is both general and accurate in terms of its predictions for CO2 loading in amino acid salt solutions. The MAPE and RMSE error rates are also 1.17E-01, respectively, while the MAPE error rate is 1.14E-01.