%0 Journal Article
%T Optimization of Extended UNIQUAC Model Parameter for Mean Activity Coefficient of Aqueous Chloride Solutions using Genetic+PSO
%J Journal of Chemical and Petroleum Engineering
%I University of Tehran
%Z 2423-673X
%A Hashemi, Seyed Hossein
%A Dinmohammad, Mahmood
%A Bagheri, Mehrdad
%D 2020
%\ 06/01/2020
%V 54
%N 1
%P 1-12
%! Optimization of Extended UNIQUAC Model Parameter for Mean Activity Coefficient of Aqueous Chloride Solutions using Genetic+PSO
%K Artificial Bee Colony Algorithm
%K Extended UNIQUAC Model
%K Genetic+PSO Algorithm
%K Mineral ions
%K Optimization
%R 10.22059/jchpe.2020.254905.1225
%X 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.
%U https://jchpe.ut.ac.ir/article_76193_4eb5122f7a78acb11c4724c2060fc461.pdf