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Degradation and mineralization of phenol compounds with goethite catalyst and mineralization prediction using artificial intelligence.

Tisa F, Davoody M, Abdul Raman AA, Daud WM - PLoS ONE (2015)

Bottom Line: The efficiency of phenol degradation via Fenton reaction using mixture of heterogeneous goethite catalyst with homogeneous ferrous ion was analyzed as a function of three independent variables, initial concentration of phenol (60 to 100 mg /L), weight ratio of initial concentration of phenol to that of H2O2 (1: 6 to 1: 14) and, weight ratio of initial concentration of goethite catalyst to that of H2O2 (1: 0.3 to 1: 0.7).Two separate models were developed using artificial neural networks to predict degradation percentage by a combination of Fe3+ and Fe2+ catalyst.Satisfactory agreement was observed between testing data and the predicted values (R2Phenol = 0.9214 and R2TOC= 0.9082).

View Article: PubMed Central - PubMed

Affiliation: Department of Chemical Engineering, Faculty of Engineering, University of Malaya,50603 Kuala Lumpur, Malaysia.

ABSTRACT
The efficiency of phenol degradation via Fenton reaction using mixture of heterogeneous goethite catalyst with homogeneous ferrous ion was analyzed as a function of three independent variables, initial concentration of phenol (60 to 100 mg /L), weight ratio of initial concentration of phenol to that of H2O2 (1: 6 to 1: 14) and, weight ratio of initial concentration of goethite catalyst to that of H2O2 (1: 0.3 to 1: 0.7). More than 90 % of phenol removal and more than 40% of TOC removal were achieved within 60 minutes of reaction. Two separate models were developed using artificial neural networks to predict degradation percentage by a combination of Fe3+ and Fe2+ catalyst. Five operational parameters were employed as inputs while phenol degradation and TOC removal were considered as outputs of the developed models. Satisfactory agreement was observed between testing data and the predicted values (R2Phenol = 0.9214 and R2TOC= 0.9082).

No MeSH data available.


Effect of number of hidden neurons on network performance.
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pone.0119933.g006: Effect of number of hidden neurons on network performance.

Mentions: The network errors are plotted versus number of neurons in hidden layer for Phenol (straight line) and TOC (dotted line) models in Fig. 6. As shown in Fig. 6, both models had the least mean square error values when 10 nodes were used in their hidden layers. Fig. 7 depicts the structure of a three-layer feed-forward back-propagation neural network consisting of ten hidden neurons. The structure was almost the same for both models with the output variable being the only difference. Fig. 7 is followed by Table 3 which lists the specifications of the proposed models.


Degradation and mineralization of phenol compounds with goethite catalyst and mineralization prediction using artificial intelligence.

Tisa F, Davoody M, Abdul Raman AA, Daud WM - PLoS ONE (2015)

Effect of number of hidden neurons on network performance.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4388832&req=5

pone.0119933.g006: Effect of number of hidden neurons on network performance.
Mentions: The network errors are plotted versus number of neurons in hidden layer for Phenol (straight line) and TOC (dotted line) models in Fig. 6. As shown in Fig. 6, both models had the least mean square error values when 10 nodes were used in their hidden layers. Fig. 7 depicts the structure of a three-layer feed-forward back-propagation neural network consisting of ten hidden neurons. The structure was almost the same for both models with the output variable being the only difference. Fig. 7 is followed by Table 3 which lists the specifications of the proposed models.

Bottom Line: The efficiency of phenol degradation via Fenton reaction using mixture of heterogeneous goethite catalyst with homogeneous ferrous ion was analyzed as a function of three independent variables, initial concentration of phenol (60 to 100 mg /L), weight ratio of initial concentration of phenol to that of H2O2 (1: 6 to 1: 14) and, weight ratio of initial concentration of goethite catalyst to that of H2O2 (1: 0.3 to 1: 0.7).Two separate models were developed using artificial neural networks to predict degradation percentage by a combination of Fe3+ and Fe2+ catalyst.Satisfactory agreement was observed between testing data and the predicted values (R2Phenol = 0.9214 and R2TOC= 0.9082).

View Article: PubMed Central - PubMed

Affiliation: Department of Chemical Engineering, Faculty of Engineering, University of Malaya,50603 Kuala Lumpur, Malaysia.

ABSTRACT
The efficiency of phenol degradation via Fenton reaction using mixture of heterogeneous goethite catalyst with homogeneous ferrous ion was analyzed as a function of three independent variables, initial concentration of phenol (60 to 100 mg /L), weight ratio of initial concentration of phenol to that of H2O2 (1: 6 to 1: 14) and, weight ratio of initial concentration of goethite catalyst to that of H2O2 (1: 0.3 to 1: 0.7). More than 90 % of phenol removal and more than 40% of TOC removal were achieved within 60 minutes of reaction. Two separate models were developed using artificial neural networks to predict degradation percentage by a combination of Fe3+ and Fe2+ catalyst. Five operational parameters were employed as inputs while phenol degradation and TOC removal were considered as outputs of the developed models. Satisfactory agreement was observed between testing data and the predicted values (R2Phenol = 0.9214 and R2TOC= 0.9082).

No MeSH data available.