Surface tension of binary mixtures containing environmentally friendly ionic liquids: Insights from artificial intelligence

The surface tension (ST) of ionic liquids (ILs) and their accompanying mixtures allows engineers to accurately arrange new processes on the industrial scale. Without any doubt, experimental methods for specification ST every supposable IL its with other compounds would be an arduous job. Also, measurements are effortful prohibitive; thus, a precise estimation property via dependable method greatly desirable. For doing this task, modeling according artificial neural network (ANN) disciplined by four optimization algorithms, namely teaching–learning-based (TLBO), particle swarm (PSO), genetic algorithm (GA) imperialist competitive (ICA), has been suggested estimate binary ILs mixtures. training testing applied network, set 748 data points systems within temperature range 283.1–348.15 K was utilized. Furthermore, outlier analysis used discover doubtful points. Gained values MSE & R2 were 0.0000007 0.993, 0.0000002 0.998, 0.0000004 0.996 0.0000006 0.994 ICA-ANN, TLBO-ANN, PSO-ANN GA-ANN, respectively. Results demonstrated that predicted TLBO-ANN model such target wholly matched.