Applying intelligent approaches to estimate the removal efficiency of heat stable salts from lean amine via electrodialysis

Abstract

Intelligent approaches based on radial basis function (RBF) neural networks and genetic programming (GP) were used to establish accurate models for estimating the removal efficiency of heat stable salts from lean amine via electrodialysis. The operating time, current intensity, membrane types, HSS concentration, and kind of concentrated solution were lumped into dimensionless groups. The groups with the most influence were selected based on the Pearson's correlation matrix for the models’ inputs. The RBF model showed an excellent agreement with real data with average absolute relative error (AARE) of 1.90% and R2 of 99.21%. Then, an explicit empirical correlation was developed for the removal efficiency using the GP technique, which yielded AARE and R2 values of 5.74% and 96.35%, respectively. The performance of the GP and RBF models for estimating the removal efficiency of different ions for different types of membranes and operating conditions were assessed and reasonable results were achieved. Finally, to identify the most effective dimensionless groups to describe the removal efficiency, a sensitivity analysis based on the developed GP and RBF models was accomplished.