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

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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.

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Abbreviations

IL:

Ionic liquid

ANN:

Artificial neural network

MRE:

Mean relative error

ICA:

Imperialist competitive algorithm

R2 :

Coefficient of determination

STD:

Standard deviation

MLP:

Multi-layer perceptron

SVM:

Support vector machine

ST:

Surface tension

MSE:

Mean squared error

BP:

Back-propagation

PSO:

Particle swarm optimization

EC:

Evolutionary computation

ARD:

Average relative deviation

GA:

Genetic algorithm

TLBO:

Teaching–learning-based optimization

RMSE:

Root-mean-squared error

DT:

Decision tree

FS:

Fuzzy system

GEP:

Gene expression programming

RBF:

Radial basis function

BP:

Backpropagation

AI:

Artificial intelligence

r:

Relevancy factor

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Authors and Affiliations

  1. Universitas Kristen Petra, Surabaya, Indonesia

    Roy Setiawan

  2. Department of Petroleum Engineering, Ahwaz Faculty of Petroleum Engineering, Petroleum University of Technology (PUT), Ahwaz, Iran

    Reza Daneshfar, Omid Rezvanjou & Siavash Ashoori

  3. Department of Chemical Engineering, Faculty of Engineering, Golestan University, Aliabad Katoul, Iran

    Maryam Naseri

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  1. Roy Setiawan
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Setiawan, R., Daneshfar, R., Rezvanjou, O. et al. Surface tension of binary mixtures containing environmentally friendly ionic liquids: Insights from artificial intelligence. Environ Dev Sustain 23, 17606–17627 (2021). https://doi.org/10.1007/s10668-021-01402-3

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  • DOI: https://doi.org/10.1007/s10668-021-01402-3

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