optimization of extended uniquac model parameter for mean activity coefficient of aqueous chloride solutions using genetic+pso

In the present study, in order to predict the activity coefficient of inorganic ions, 12 cases of aqueous chloride solution were considered (aclx=1,2; a=li, na, k, rb, mg, ca, ba, mn, fe, co, ni). for this study, the uniquac thermodynamic model is desired and its adjustable parameters are optimized with the genetic + pso algorithm. the optimization of the uniquac model with pso+ genetic algorithms has good results. so that the minimum and maximum electrolyte error of the whole system are 0.00044 and 0.0091, respectively. for this study, a temperature of 298.15 and a pressure of 1 is considered. also, in this study for the electrolyte system, the artificial bee colony (abc) algorithm, and imperialist competitive algorithm (ica) has been studied. the results showed that the artificial bee colony algorithm has a lower accuracy than the genetic+ particle swarm optimization (pso) algorithm. the minimum concentration was 0.1 molality and the maximum concentration was 3 molality. based on the results, the activity coefficient of licl, nacl, kcl, rbcl + h2o, mgcl2, cacl2, bacl2, mncl2, fecl2, cocl2 nicl2 depends on the ionic strength of the electrolyte system.