Neural computations in modelling of CO2 capture from Gas stream emissions by Sodium Glycinate solution

The present contribution was performed in order to predict CO2 loading capacity in aqueous sodium glycinate as a novel class of green solution under wide operating range using radial basis function artificial neural network (RBFANN). The predicted CO2 loading capacity values were in brilliant agreement with those corresponding experimental values. The estimated values of MSE and R-squared were 0.00045 and 0.997, respectively. Accordingly, statistical and graphical analyses confirm satisfactory prediction of our proposed model.