Toward an intelligent approach for predicting surface tension of binary mixtures containing ionic liquids

Soleimani, Reza; Dehaghani, Amir Hossein Saeedi; Shoushtari, Navid Alavi; Yaghoubi, Pedram; Bahadori, Alireza

doi:10.1007/s11814-017-0326-4

Cited by 22 publications

(23 citation statements)

References 75 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the last few years, various innovative methods have been used to predict the surface tension of pure ILs (Atashrouz et al 2017;Lazzús, 2017). According to the latest study in literature, few works have predicted the surface tension of IL binary systems using various artificial intelligence methods which are described below (Atashrouz, 2017;Hashemkhani, 2015;Lashkarbolooki, 2017;Shojaeian & Asadizadeh, 2020;Soleimani, 2018).…”

Section: Introductionmentioning

confidence: 99%

“…This model was able to predict the output data with R 2 = 0.9948 and MSE = 6.67 E-7. In 2018, Soleimani and his colleagues used a model based on an artificial neural network to predict the surface tension of mixtures containing ionic liquid (Soleimani, 2018). They used 748 data and were able to accurately predict the output parameter by considering the input parameters of their model including temperature, ionic liquid properties (mole fraction and molecular weight), and non-ionic liquid properties (boiling temperature and molecular weight).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Setiawan

Daneshfar

Rezvanjou

et al. 2021

Environ Dev Sustain

View full text Add to dashboard Cite

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 & R 2 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.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Setiawan

Daneshfar

Rezvanjou

et al. 2021

Environ Dev Sustain

View full text Add to dashboard Cite

show abstract

“…Various ANN models have already been used for the surface tension of pure liquids of different kinds [ 16 , 21 , 30 , 31 , 34 , 35 , 36 , 37 ] and mixtures [ 31 , 39 , 40 ]. In particular, the proposals for pure liquids, published from 2008 to 2017, were summarized by Mulero et al [ 34 ].…”

Section: Introductionmentioning

confidence: 99%

Surface Tension of Liquid Organic Acids: An Artificial Neural Network Model

2021

View full text Add to dashboard Cite

An artificial neural network model is proposed for the surface tension of liquid organic fatty acids covering a wide temperature range. A set of 2051 data collected for 98 acids (including carboxylic, aliphatic, and polyfunctional) was considered for the training, testing, and prediction of the resulting network model. Different architectures were explored, with the final choice giving the best results, in which the input layer has the reduced temperature (temperature divided by the critical point temperature), boiling temperature, and acentric factor as an independent variable, a 41-neuron hidden layer, and an output layer consisting of one neuron. The overall absolute percentage deviation is 1.33%, and the maximum percentage deviation is 14.53%. These results constitute a major improvement over the accuracy obtained using corresponding-states correlations from the literature.

show abstract

“…Based on these facts, it is highly desirable to apply alternative advanced soft computing methods to address problems associated with traditional methods in calculating the gas solubility in polymers. Successful application of soft computing techniques, such as artificial neural networks (ANN), genetic programming (GP), support vector machines (SVM), decision tree (DT)‐based algorithms, and the adaptive neuro fuzzy inference system (ANFIS), in various areas have been reported in the literature .…”

Section: Introductionmentioning

confidence: 99%

Evolving an Accurate Decision Tree‐Based Model for Predicting Carbon Dioxide Solubility in Polymers

et al. 2020

Self Cite

View full text Add to dashboard Cite

Solubility is one of the most indispensable physicochemical properties determining the compatibility of components of a blending system. Research has been focused on the solubility of carbon dioxide in polymers as a significant application of green chemistry. To replace costly and time‐consuming experiments, a novel solubility prediction model based on a decision tree, called the stochastic gradient boosting algorithm, was proposed to predict CO2 solubility in 13 different polymers, based on 515 published experimental data lines. The results indicate that the proposed ensemble model is an effective method for predicting the CO2 solubility in various polymers, with highly satisfactory performance and high efficiency. It produces more accurate outputs than other methods such as machine learning schemes and an equation of state approach.

show abstract

Toward an intelligent approach for predicting surface tension of binary mixtures containing ionic liquids

Cited by 22 publications

References 75 publications

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

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

Surface Tension of Liquid Organic Acids: An Artificial Neural Network Model

Evolving an Accurate Decision Tree‐Based Model for Predicting Carbon Dioxide Solubility in Polymers

Contact Info

Product

Resources

About