Prediction of surface tension of the binary mixtures containing ionic liquid using heuristic approaches; an input parameters investigation

Shojaeian, Abolfazl; Asadizadeh, Mostafa

doi:10.1016/j.molliq.2019.111976

Cited by 21 publications

(10 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The input parameters of the model included temperature, melting temperature, mole fraction, and molecular weight of ionic and non-ILs. Shojaeian and Asadizadeh 54 proposed an ANN model to predict surface tension of binary mixtures containing ILs based on 1537 data points regarding 33 binary mixtures. In their study, various approaches were developed by utilizing physical properties such as temperature, reduced temperature, critical temperature, critical pressure, critical volume, molecular weight, acentric factor, and critical compressibility factor, along with two distinct mixing rules, as input parameters.…”

Section: Introductionmentioning

confidence: 99%

Insights into the estimation of surface tensions of mixtures based on designable green materials using an ensemble learning scheme

Soleimani,

Saeedi Dehaghani

2023

Sci Rep

View full text Add to dashboard Cite

Precise estimation of the physical properties of both ionic liquids (ILs) and their mixtures is crucial for engineers to successfully design new industrial processes. Among these properties, surface tension is especially important. It’s not only necessary to have knowledge of the properties of pure ILs, but also of their mixtures to ensure optimal utilization in a variety of applications. In this regard, this study aimed to evaluate the effectiveness of Stochastic Gradient Boosting (SGB) tree in modeling surface tensions of binary mixtures of various ionic liquids (ILs) using a comprehensive dataset. The dataset comprised 4010 experimental data points from 48 different ILs and 20 non-IL components, covering a surface tension range of 0.0157–0.0727 N m−1 across a temperature range of 278.15–348.15 K. The study found that the estimated values were in good agreement with the reported experimental data, as evidenced by a high correlation coefficient (R) and a low Mean Relative Absolute Error of greater than 0.999 and less than 0.004, respectively. In addition, the results of the used SGB model were compared to the results of SVM, GA-SVM, GA-LSSVM, CSA-LSSVM, GMDH-PNN, three based ANNs, PSO-ANN, GA-ANN, ICA-ANN, TLBO-ANN, ANFIS, ANFIS-ACO, ANFIS-DE, ANFIS-GA, ANFIS-PSO, and MGGP models. In terms of the accuracy, the SGB model is better and provides significantly lower deviations compared to the other techniques. Also, an evaluation was conducted to determine the importance of each variable in predicting surface tension, which revealed that the most influential factor was the mole fraction of IL. In the end, William’s plot was utilized to investigate the model's applicability range. As the majority of data points, i.e. 98.5% of the whole dataset, were well within the safety margin, it was concluded that the proposed model had a high applicability domain and its predictions were valid and reliable.

show abstract

Section: Introductionmentioning

confidence: 99%

Insights into the estimation of surface tensions of mixtures based on designable green materials using an ensemble learning scheme

Soleimani,

Saeedi Dehaghani

2023

Sci Rep

View full text Add to dashboard Cite

show abstract

“…More information on MFO programs and algorithms can be found in the literature (Mirjalili, 2015;[19-23,25]; Shehab et al, 2020; [24]). Artificial neural networks inspired by the human nervous system are widely used today in various fields to predict and solve problems (Amini et al, 2021; [26]; Shakeri et al, 2020; [27]). One of the most practical types of ANN models includes multilayer perceptron networks that have been used since the 1980s.…”

Section: Introductionmentioning

confidence: 99%

Computational Modeling of Desiccation Properties (CW, LS, and VS) of Waste-Based Activated Ash-Treated Black Cotton Soil for Sustainable Subgrade Using Artificial Neural Network, Gray-Wolf, and Moth-Flame Optimization Techniques

Onyelowe

Shakeri

Amini-Khoshalan

et al. 2022

Advances in Materials Science and Engineering

View full text Add to dashboard Cite

Artificial neural network (ANN), gray-wolf, and moth-flame optimization (GWO and MFO) techniques have been used in this research work to predict the effect of activated sawdust ash (ASDA) on the crack width (CW), linear shrinkage (LS), and volumetric shrinkage (VS) of a black cotton soil utilized as a subgrade material. Problematic soils or black cotton soils are not good pavement foundation materials except that they are pretreated in order to meet the basic strength characteristics required for roads in Nigeria. Due to this reason, there has been ongoing research to evaluate the best practices in which black cotton soils can be favorably utilized in earthwork construction. On the other hand, there is a huge concern on the solid waste management system in the wood processing environment and the recycling of sawdust into ash and its reuse as an alternative binder has offered a sustainable disposal system. The work tries to use AI-based techniques to predict the crack and shrinkage behaviors of BCS treated with saw dust ash activated with alkali materials. There was appreciable improvement in the shrinkage and crack parameters over the 30-day drying period due to the addition of ASDA. The intelligent model results showed that the three techniques successfully predicted the CW, LS, and VS with a performance accuracy above 90%, while ANN produced the minimal error in performance outperforming the other techniques. Sensitivity study showed that the drying time (T) was the most influential of the studied parameter. Hence, soil stabilization has shown its potential system of waste management in the wood processing industry.

show abstract

“…They are a new class of liquids, considered as green solvents, that retain their liquid state over wide temperature ranges [1, 3], with high solvation properties, negligible vapor pressure, and high thermal, chemical, and electrochemical stability [4]. Potential applications of ILs require knowledge of physicochemical properties such as density, viscosity, melting point, solvent properties, vapor pressure and surface tension for pure ILs and their mixtures with other solvents [5].…”

Section: Introductionmentioning

confidence: 99%

Machine learning approach for the prediction of surface tension of binary mixtures containing ionic liquids using σ‐profile descriptors

Benmouloud

Si‐Moussa

Benkortbi

2022

Int J of Quantum Chemistry

View full text Add to dashboard Cite

Ionic liquids (IL) are a new class of liquids considered as green solvents; less toxic, less flammable, and less polluting which retain their liquid state over wide temperature ranges and are considered alternatives to volatile organic solvents. The surface tension of IL-organic solvent mixtures plays an important role in the design and development of many industrial processes. This work investigated the capability and feasibility of four ANN model topologies ("trainbr, logsig"; "trainbr, tansig"; "trainlm, logsig"; "trainlm, tansig"), a PSO-SVM model, and an LSSVM model to predict the surface tension of binary systems containing IL. For this purpose, 1623 data points corresponding to the experimental surface tension values of binary mixtures containing IL were collected from the literature. The surface tension values were between 18.9 and 72.7 mN m À1 . The temperature, the composition in mole fraction of IL (X IL ), descriptors based on the sigma profiles, relating to the H-bond donor and to the H-bond acceptor character, the anion, the cation and the solvent were used as input variables of the model in order to differentiate the different compounds involved in the binary systems. A comparison of the experimental and the predicted values in terms of several statistical metrics showed good agreement, however, the prediction (trainbr, logsig) was better than the other approaches with an overall average absolute relative deviation of .8466% and a mean square error of .4952. These results are very encouraging for future projects modeling other physical and chemical properties of ILs.

show abstract

Prediction of surface tension of the binary mixtures containing ionic liquid using heuristic approaches; an input parameters investigation

Cited by 21 publications

References 43 publications

Insights into the estimation of surface tensions of mixtures based on designable green materials using an ensemble learning scheme

Insights into the estimation of surface tensions of mixtures based on designable green materials using an ensemble learning scheme

Computational Modeling of Desiccation Properties (CW, LS, and VS) of Waste-Based Activated Ash-Treated Black Cotton Soil for Sustainable Subgrade Using Artificial Neural Network, Gray-Wolf, and Moth-Flame Optimization Techniques

Machine learning approach for the prediction of surface tension of binary mixtures containing ionic liquids using σ‐profile descriptors

Contact Info

Product

Resources

About