Computational intelligence techniques for modeling of dynamic adsorption of organic pollutants on activated carbon

Mesellem, Yamin; Hadj, Abdallah Abdallah El; Laidi, Maamar; Hanini, Salah; Hentabli, Mohamed

doi:10.1007/s00521-021-05890-2

Cited by 14 publications

(12 citation statements)

References 46 publications

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“…Dragonfly algorithm and a support vector machine (DA-SVM) technique. [24,29] RMSE, root mean square error; SMVR, support vector machine for regression criteria to assess the predictive power of the DA-SVM model. [30] RMSE…”

Section: Svm Optimization With the Da Techniquementioning

confidence: 99%

Investigation of the candesartan cilexetil antihypertensive drug microencapsulation by PLA‐PVP K30 biodegradable polymers: Experimental optimization and release kinetics modelling

et al. 2023

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Candesartan is an angiotensin receptor blocker (ARB) used to treat hypertension. However, its poor aqueous solubility and oral bioavailability have limited its therapeutic applications. In order to increase bioavailability and control the release of candesartan condensation, microspheres containing biodegradable polymers (polyvinylpyrrolidone [PVP K30] and polylactic acid [PLA]) in different ratios were prepared by the o/o solvent evaporation method using Span 80 as a surfactant. In addition, the impact of encapsulation parameters (i.e., PVP K30 and PLA concentrations) on the encapsulation ratio and release percentage was investigated by the mixed factorial design method. The release kinetics of the microspheres was simulated by combining two methods, the Dragonfly algorithm and a support vector machine (DA‐SVM). The experimental data were in good agreement with the predicted data, with a coefficient of determination close to unity and a mean square error close to zero. Fourier‐transfer infrared spectrometry (FTIR) analysis revealed the presence of condensation in all formulations without reporting distortion in the spectra. Scanning electron microscopy (SEM) confirmed the successful synthesis of microspheres, whose sizes were between 12 and 26 μm. Formulations with a PLA‐drug ratio of 6:1 (N15, N17, and N18) showed the highest encapsulation efficiency (68%, 71%, and 70%, respectively), while formulations that do contain PVP K30, such as N5, N4, and N3, showed a higher release (83%, 84%, and 89%, respectively), indicating that the agent (PVP K30) enhanced the bioavailability and release of candesartan. Overall, N3 showed a higher drug release rate at 12 h and important encapsulation efficiency, making it the optimal formulation obtained in this study.

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Section: Svm Optimization With the Da Techniquementioning

confidence: 99%

Investigation of the candesartan cilexetil antihypertensive drug microencapsulation by PLA‐PVP K30 biodegradable polymers: Experimental optimization and release kinetics modelling

et al. 2023

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“…ψ is equivalent to the function approximation accuracy placed on the training data samples. 15 C is the capacity parameter, ξ i and ξ i * represent the positive slack variables. 15 K is a kernel function defined by an inner product of the nonlinear transfer function; the most used kernel function is the Gaussian Radial Basis function (RBF), which is given by Eq.…”

Section: Svm Modelling Approachmentioning

confidence: 99%

“…10 Due to this complexity, machine learning algorithms have emerged as a powerful tool, compared to other classical methods, to tackle the nonlinear relationships directly from samples with no previous knowledge of the chemical or physical nature that affects the system. [10][11][12][13] Different machine learning algorithms were used in the literature as an advanced mathematical tool to model the adsorption capacity of single and multicomponent adsorption systems, such as: artificial neural network (ANN), 3,7,[11][12][13][14][15] support vector machine (SVM). 12,[15][16][17] The SVM method can overcome some disadvantages of the ANN model, such as robustness, and avoid the result of falling into local optimum.…”

Section: Introductionmentioning

confidence: 99%

“…[10][11][12][13] Different machine learning algorithms were used in the literature as an advanced mathematical tool to model the adsorption capacity of single and multicomponent adsorption systems, such as: artificial neural network (ANN), 3,7,[11][12][13][14][15] support vector machine (SVM). 12,[15][16][17] The SVM method can overcome some disadvantages of the ANN model, such as robustness, and avoid the result of falling into local optimum. 18 However, the SVM's parameters are tuned using a built-in optimisers, which are generally selected by trial and error method.…”

Section: Introductionmentioning

confidence: 99%

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Multicomponent Adsorption Capacity Forecasting Based on Support Vector Machine with Dragonfly Algorithm

Moumen

Laidi

Hanini

et al. 2023

Kemija U Industriji

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The predictability of the adsorption capacity of the multicomponent adsorption system was modelled using Support Vector Machine (SVM). Two SVM models were built and compared. In the first model, the SVM method was used with an already built-in optimisation algorithm. However, in the second model, the SVM method was used by means of a very recent and efficient optimisation algorithm: the Dragonfly Algorithm (DA). The models' accuracy was evaluated by three well-established statistical metrics (root mean squared error RMSE, determination coefficient R 2 , and correlation coefficient R). The used data were collected from previous experimental papers published in literature containing all kinds of pollutants, such as heavy metal ions, dyes, and organic compounds, and different natural/ synthetic adsorbents. The dataset contained five important variables with 1023 points; the variables were divided into four inputs (molecular weight, equilibrium concentrations of adsorbate, specific area of adsorbent, and temperature), and one output (adsorption capacity at equilibrium). The data were divided using the holdout function into two subsets (80 % for training set, and 20 % for test set). The programming stage was carried out using MATLAB software. The results showed that the optimised DA-SVM model with RBF-Gaussian kernel function had good ability for global search combined with high prediction accuracy, with R 2 = 0.997, R = 0.998, and RMSE = 2.539. The obtained model can be used to predict the efficiency of the adsorption system, and provides a tool for process optimisation responding to changes in operating conditions. A new graphical user interface (GUI) was developed with MATLAB GUI to estimate accurately the desired responses by using the best DA-SVM model.

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“…The objective of this model is to construct a mathematical model that can be utilised to predict the dependent variable based on the inputs of independent variables or the predictors. 8,22 Multiple linear regression (MLR)model has been used to obtain the significant relationship as well as correlation between the input variable and the output.…”

Section: Multiple Linear Regressionmentioning

confidence: 99%

Cumulative Drug Release Modelling of PCL-PVP Encapsulated Tramadol by DA-SVM, MLR, PLS, and OLS Regression Techniques

Chabane

Bouchal

Hentabli

et al. 2022

Kemija U Industriji

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This work aimed to model the kinetics of cumulative drug release from formulations based on encapsulation by biodegradable polycaprolactone and polyvinylpyrrolidone polymers. Different ratios of the polymerswere prepared by a solvent evaporation method using Span 20 and Span 80 as surfactants. The cumulative drug release was estimated depending on the formulation component and time. Four models: hybrid model of support vector machine and dragonfly algorithm (DA-SVM), partial least squares (PLS) model, multiple linear regression (MLR) model, and ordinary least squared (OLS) model, were developed and compared. The statistical analysis proved there were no issues in variable inputs. The results showed that the DA-SVM model gave a better result where a determination coefficient was close to one and RMSE error close to zero. A graphical interface was built to calculate the cumulative drug release.

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Computational intelligence techniques for modeling of dynamic adsorption of organic pollutants on activated carbon

Cited by 14 publications

References 46 publications

Investigation of the candesartan cilexetil antihypertensive drug microencapsulation by PLA‐PVP K30 biodegradable polymers: Experimental optimization and release kinetics modelling

Investigation of the candesartan cilexetil antihypertensive drug microencapsulation by PLA‐PVP K30 biodegradable polymers: Experimental optimization and release kinetics modelling

Multicomponent Adsorption Capacity Forecasting Based on Support Vector Machine with Dragonfly Algorithm

Cumulative Drug Release Modelling of PCL-PVP Encapsulated Tramadol by DA-SVM, MLR, PLS, and OLS Regression Techniques

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