Optimization of Extended UNIQUAC Model Parameter for Mean Activity Coefficient of Aqueous Chloride Solutions using Genetic+PSO

Abstract

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.

Keywords

• artificial bee colony algorithm
• extended uniquac model
• genetic+pso algorithm
• mineral ions
• optimization