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

Roy Setiawan, Reza Daneshfar (), Omid Rezvanjou, Siavash Ashoori and Maryam Naseri ()
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Roy Setiawan: Universitas Kristen Petra
Reza Daneshfar: Petroleum University of Technology (PUT)
Omid Rezvanjou: Petroleum University of Technology (PUT)
Siavash Ashoori: Petroleum University of Technology (PUT)
Maryam Naseri: Golestan University

Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, 2021, vol. 23, issue 12, No 20, 17606-17627

Abstract: Abstract 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 the specification of the ST of every supposable IL and its mixtures with other compounds would be an arduous job. Also, experimental measurements are effortful and prohibitive; thus, a precise estimation of the property via a dependable method would be greatly desirable. For doing this task, a new modeling method according to artificial neural network (ANN) disciplined by four optimization algorithms, namely teaching–learning-based optimization (TLBO), particle swarm optimization (PSO), genetic algorithm (GA) and imperialist competitive algorithm (ICA), has been suggested to estimate ST of the binary ILs mixtures. For training and testing the applied network, a set of 748 data points of binary ST of IL systems within the temperature range of 283.1–348.15 K was utilized. Furthermore, an outlier analysis was used to discover doubtful data points. Gained values of MSE & R2 were 0.0000007 and 0.993, 0.0000002 and 0.998, 0.0000004 and 0.996 and 0.0000006 and 0.994 for the ICA-ANN, TLBO-ANN, PSO-ANN and GA-ANN, respectively. Results demonstrated that the experimental data and predicted values of the TLBO-ANN model for such target are wholly matched.

Keywords: Surface tension; Ionic liquid; Artificial neural network; Genetic algorithm; Teaching–learning-based optimization; Imperialist competitive algorithm; Particle swarm optimization (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s10668-021-01402-3

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