Modeling and Optimization of Fe(III) Adsorption from Water using Bentonite Clay: Comparison of Central Composite Design and Artificial Neural Network

2012 ◽  
Vol 35 (11) ◽  
pp. 2007-2014 ◽  
Author(s):  
I. M. Savic ◽  
S. T. Stojiljkovic ◽  
I. M. Savic ◽  
S. B. Stojanovic ◽  
K. Moder
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Central Composite Design ◽  
Bentonite Clay ◽  
Modeling And Optimization ◽  
Artificial Neural ◽  
Central Composite

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

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
WesamEldin I. A. Saber ◽  
Noura El-Ahmady El-Naggar ◽  
Mohammed S. El-Hersh ◽  
Ayman Y. El-khateeb ◽  
Ashraf Elsayed ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Central Composite Design ◽  
Immobilized Cells ◽  
Comparative Approach ◽  
Bacterial Cells ◽  
Ann Model ◽  
Biosorption Process ◽  
Artificial Neural ◽  
Central Composite

AbstractHeavy 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.

scholarly journals Use of Central Composite Design and Artificial Neural Network for Predicting the Yield of Biodiesel

Procedia CIRP ◽  
2020 ◽  
Vol 89 ◽  
pp. 59-67
Author(s):  
Ilesanmi Daniyan ◽  
Emmanuel Bello ◽  
Tunde Ogedengbe ◽  
Khumbulani Mpofu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Central Composite Design ◽  
Artificial Neural ◽  
Central Composite
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