Performance comparison of GRG algorithm with evolutionary algorithms in an aqueous electrolyte system

Excel solver is a powerful tool for optimization of linear and nonlinear problems. With this unique tool, the user can achieve an optimal value for the desired objective function in Excel cell. This solver acts on a group of cells that are directly or indirectly associated with the function; thus, the user-defined values will be optimized. In the present work, 13 existing species in an electrolyte solution have been considered to predict the activity coefficient of inorganic ions in the electrolyte solution, which includes H2O, CO2(aq), H+, Na+, Ba2+, Ca2+, Sr2+, Mg2+, OH-, Cl-, SO4, CO3, HCO3. In this study, to predict the activity coefficient of species in the system, Extended UNIQUAC activity coefficient model was considered and its parameters optimized using Excel solver tool based on GRG algorithm. Total error for optimization of adjustable parameters of Extended UNIQUAC model for 13 desired ions at three temperatures 298.15 K, 323.15 K and 373.15 K with the Excel solver tool was 0.0087. The results of GRG algorithm were favorable than those of ICA, PSO and ABC algorithms. The results of this optimization are intended to predict mineral deposition. The number of adjustable variables (model parameters for optimization) is over 200, and the number of target functions is 39.