Dragonfly-Support Vector Machine for Regression Modeling of the Activity Coefficient at Infinite Dilution of Solutes in Imidazolium Ionic Liquids Using σ-Profile Descriptors

Benimam, Hania; Si‐Moussa, Cherif; Hentabli, Mohamed; Hanini, Salah; Laidi, Maamar

doi:10.1021/acs.jced.0c00168

Cited by 37 publications

(38 citation statements)

References 56 publications

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“…The best validation performance is 10 À4 at epoch (iterations) 1,000 for the best network topology with overall MAPE of 2.4535%, % RMSE of 2.54%, values of bias factor B f ¼ 1 and accuracy factor A f ¼ 1, which indicates that the model is valid in the predicting of FR. The values of K and K' indicates that the model is acceptable (Benimam et al 2020).…”

Section: Resultsmentioning

confidence: 98%

Application of artificial neural network (ANN-MLP) for the prediction of fouling resistance in heat exchanger to MgO-water and CuO-water nanofluids

Benyekhlef

Mohammedi

Hassani

et al. 2021

Water Science and Technology

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In this work an artificial neural network model was developed with the aim of predicting fouling resistance for heat exchanger, the network was designed and trained by means of 375 experimental data points that were selected from the literature. This data points contains 6 inputs, including time, volumetric concentration, heat flux, mass flow rate, inlet temperature, thermal conductivity and fouling resistance as an output. The experimental data are used for training, testing and validation the ANN using multiple layer perceptron (MLP). The comparison of statistical criteria of different networks shows that the optimal structure for predicting the fouling resistance of the nanofluid is the MLP network with 20 hidden neurons, which has been trained with Levenberg–Marquardt (LM) algorithm. The accuracy of the model was assessed based on three known statistical metrics including mean square error (MSE), mean absolute percentage error (MAPE) and coefficient of determination (R2). The obtained model was found with the performance of {MSE = 6.5377 × 10−4, MAPE = 2.40% and R2 = 0.99756} for the training stage, {MSE = 3.9629 × 10−4, MAPE = 1.8922% and R2 = 0.99835} for the test stage and {MSE = 5.8303 × 10−4, MAPE = 2.57% and R2 = 0.99812} for the validation stage. In order to control the fouling procedure, and after conducting a sensitivity analysis, it found that all input variables have strong effect on the estimation of the fouling resistance.

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Section: Resultsmentioning

confidence: 98%

Application of artificial neural network (ANN-MLP) for the prediction of fouling resistance in heat exchanger to MgO-water and CuO-water nanofluids

Benyekhlef

Mohammedi

Hassani

et al. 2021

Water Science and Technology

Self Cite

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“…In this work the performance and reliability of the ANN model were evaluated by various statistical criteria including the relative absolute error (RAE%), the mean relative absolute error (MRAE), the mean square error (MSE), the Correlation Coefficient (R), the Coefficient of determination (R 2 ) [22] .…”

Section: Resultsmentioning

confidence: 99%

Using artificial neural network for predicting heat transfer coefficient during flow boiling in an inclined channel

et al. 2021

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The flow and heat transfer characteristics in a nuclear power plant in the event of a serious accident are simulated by boiling water in an inclined rectangular channel. In this study an artificial neural network model was developed with the aim of predicting heat transfer coefficient (HTC) for flow boiling of water in inclined channel, the network was designed and trained by means of 520 experimental data points that were selected from within the literature. orientation ,mass flux, quality and heat flow which were employed to serve as variables of input of multiple layer perceptron (MLP) neural network, whereas the analogous HTC was selected to be its output. Via the method of trial-and-error, MLP network with 30 neurons in the hidden layer was attained as optimal ANN structure. The fact that is was enabled to predict accurately the HTC. For the training set, the mean relative absolute error (MRAE) is about 0.68 % and the correlation coefficient (R) is about 0.9997. As for the testing and validation set they are respectively about 0.60 % and 0.9998 and about 0.79 % and 0.9996. The comparison of the developed ANN model with experimental data and empirical correlations in vertical channel under the low flow rate and low quality shows a good agreement.

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“…23 More details about the SVM model optimisation equation and the kernel functions can be found in ref. [24][25][26] A novel metaheuristic optimisation algorithm named Dragonfly (DA) was developed by S. Mirjalili 27 based on the behaviour of dragonflies. 27 This algorithm was coupled with SVM method to tune its hyper-parameters.…”

Section: Methodsmentioning

confidence: 99%

Critical Properties and Acentric Factors of Pure Compounds Modelling Based on QSPR-SVM with Dragonfly Algorithm

et al. 2021

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This work aimed to model the critical pressure, temperature, volume properties, and acentric factors of 6700 pure compounds based on five relevant descriptors and two thermodynamic properties. To that end, four methods were used, namely, multi-linear regression (MLR), artificial neural networks (ANNs), support vector machines (SVMs) using sequential minimal optimisation (SMO), and hybrid SVM with Dragonfly optimisation algorithm (SVM-DA) to model each property. The results suggested that hybrid SVM-DA had better prediction performance compared to the other models in terms of average absolute relative deviation (AARD%) of {0.7551, 1.962, 1.929, and 2.173} and R2 of {0.9699, 0.9673, 0.9856, and 0.9766} for critical temperature, critical pressure, critical volume, and acentric factor, respectively. The developed models can be used to estimate the property of newly designed compounds only from their molecular structure.

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Dragonfly-Support Vector Machine for Regression Modeling of the Activity Coefficient at Infinite Dilution of Solutes in Imidazolium Ionic Liquids Using σ-Profile Descriptors

Cited by 37 publications

References 56 publications

Application of artificial neural network (ANN-MLP) for the prediction of fouling resistance in heat exchanger to MgO-water and CuO-water nanofluids

Application of artificial neural network (ANN-MLP) for the prediction of fouling resistance in heat exchanger to MgO-water and CuO-water nanofluids

Using artificial neural network for predicting heat transfer coefficient during flow boiling in an inclined channel

Critical Properties and Acentric Factors of Pure Compounds Modelling Based on QSPR-SVM with Dragonfly Algorithm

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