ANN and RSM based modelling for optimization of cell dry mass of <i>Bacillus</i> sp. strain B67 and its antifungal activity against <i>Botrytis cinerea</i>

Shafi, Jamil; Sun, Zhonghua; Ji, Mingshan; Gu, Zheng; Ahmad, Waqas

doi:10.1080/13102818.2017.1379359

Cited by 35 publications

(26 citation statements)

References 37 publications

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“…ANN can be, superly, replace the polynomial regression-based modeling approach, such as RSM, which modeling the complex nonlinear relationships. The ANN model is hypothetically more accurate because it includes all data points of an experiment 18 , 19 . The modeling procedure of ANN includes the choice of network architecture, establishing of the hidden layers and neurons number in each layer, learning, training, and, finally, validation and verification of the model 20 .…”

Section: Introductionmentioning

confidence: 99%

Rotatable central composite design versus artificial neural network for modeling biosorption of Cr6+ by the immobilized Pseudomonas alcaliphila NEWG-2

Saber

El‐Naggar

El-Hersh

et al. 2021

Sci Rep

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Heavy metals, including chromium, are associated with developed industrialization and technological processes, causing imbalanced ecosystems and severe health concerns. The current study is of supreme priority because there is no previous work that dealt with the modeling of the optimization of the biosorption process by the immobilized cells. The significant parameters (immobilized bacterial cells, contact time, and initial Cr6+ concentrations), affecting Cr6+ biosorption by immobilized Pseudomonas alcaliphila, was verified, using the Plackett–Burman matrix. For modeling the maximization of Cr6+ biosorption, a comparative approach was created between rotatable central composite design (RCCD) and artificial neural network (ANN) to choose the most fitted model that accurately predicts Cr6+ removal percent by immobilized cells. Experimental data of RCCD was employed to train a feed-forward multilayered perceptron ANN algorithm. The predictive competence of the ANN model was more precise than RCCD when forecasting the best appropriate wastewater treatment. After the biosorption, a new shiny large particle on the bead surface was noticed by the scanning electron microscopy, and an additional peak of Cr6+ was appeared by the energy dispersive X-ray analysis, confirming the role of the immobilized bacteria in the biosorption of Cr6+ ions.

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Section: Introductionmentioning

confidence: 99%

Rotatable central composite design versus artificial neural network for modeling biosorption of Cr6+ by the immobilized Pseudomonas alcaliphila NEWG-2

Saber

El‐Naggar

El-Hersh

et al. 2021

Sci Rep

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show abstract

“…This is expressed in the very high values of R 2 & F ratio and the extremely low value of error indicators for the ANN results compared to that of RSM ones. This is more pro- to its universal approximation ability for nonlinearity, whereas RSM is only limited to a second-order polynomial regression [46].…”

Section: Resultsmentioning

confidence: 99%

“…The oval shape of the contour plots indicates a significant interaction between the independent variables. The smallest ellipses in the contour plots represent the maximum predicted values [46].…”

Section: The Three-dimensional Concave Curved Response Surfaces In Fimentioning

confidence: 99%

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Comparative Appraisal of Response Surface Methodology and Artificial Neural Network Method for Stabilized Turbulent Confined Jet Diffusion Flames Using Bluff-Body Burners

Gendy¹,

Ghoneim²,

Zakhary³

2020

WJET

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The present study was conducted to present the comparative modeling, predictive and generalization abilities of response surface methodology (RSM) and artificial neural network (ANN) for the thermal structure of stabilized confined jet diffusion flames in the presence of different geometries of bluff-body burners. Two stabilizer disc burners tapered at 30˚ and 60˚ and another frustum cone of 60˚/30˚ inclination angle were employed all having the same diameter of 80 (mm) acting as flame holders. The measured radial mean temperature profiles of the developed stabilized flames at different normalized axial distances () j x d were considered as the model example of the physical process. The RSM and ANN methods analyze the effect of the two operating parameters namely () r , the radial distance from the center line of the flame, and () j x d on the measured temperature of the flames, to find the predicted maximum temperature and the corresponding process variables. A three-layered Feed Forward Neural Network in conjugation with the hyperbolic tangent sigmoid (tansig) as transfer function and the optimized topology of 2:10:1 (input neurons: hidden neurons: output neurons) was developed. Also the ANN method has been employed to illustrate such effects in the three and two dimensions and shows the location of the predicted maximum temperature. The results indicated the superiority of ANN in the prediction capability as the ranges of R 2 and F Ratio are 0.868-0.947 and 231.7-864.1 for RSM method compared to 0.964-0.987 and 2878.8 7580.7 for ANN method beside lower values for error analysis terms.

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“…Patel et al [28] compared RSM and ANN process models to determine the surface roughness in roller burnishing. The optimization of the fermenting medium for the cell dry mass using both RSM and ANN models was discussed by Shafi and co-authors [29]. Kim et al [30] built RSM and ANN models of an NO x removal system in a liquefied natural gas (LNG) terminal.…”

Section: Introductionmentioning

confidence: 99%

Modeling and Optimization of a Jackfruit Seed‐Based Supercapacitor Electrode Using Machine Learning

Mathew¹,

Karandikar²,

Kulkarni³

2020

Chem Eng & Technol

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Supercapacitors can be used for portable energy storage applications. In this study, machine learning techniques are applied to optimize the process of preparation of supercapacitor electrodes from chemically activated carbon made from jackfruit seeds. Experimental trials were carried out using statistical design of experiments. Artificial neural network was employed to generate the process model and a multiobjective optimization was attempted by means of swarm intelligence and the Derringer's desirability function. The optimized electrode demonstrated high capacitance and low resistance making it suitable for supercapacitors. The algorithm developed in the study can be adopted by process engineers for efficient optimization.

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ANN and RSM based modelling for optimization of cell dry mass of Bacillus sp. strain B67 and its antifungal activity against Botrytis cinerea

Abstract: ANN and RSM based modelling for optimization of cell dry mass of Bacillus sp. strain B67 and its antifungal activity against Botrytis cinerea

Cited by 35 publications

References 37 publications

Rotatable central composite design versus artificial neural network for modeling biosorption of Cr6+ by the immobilized Pseudomonas alcaliphila NEWG-2

Rotatable central composite design versus artificial neural network for modeling biosorption of Cr6+ by the immobilized Pseudomonas alcaliphila NEWG-2

Comparative Appraisal of Response Surface Methodology and Artificial Neural Network Method for Stabilized Turbulent Confined Jet Diffusion Flames Using Bluff-Body Burners

Modeling and Optimization of a Jackfruit Seed‐Based Supercapacitor Electrode Using Machine Learning

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