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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.


Comparison between experimental and neural networking results for TOC removal % and Phenol degradation % (a.) 20 min time interval, (b.) 40 min time interval and (c.) 60 min time interval.
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pone.0119933.g008: Comparison between experimental and neural networking results for TOC removal % and Phenol degradation % (a.) 20 min time interval, (b.) 40 min time interval and (c.) 60 min time interval.

Mentions: Performance of the proposed models was evaluated by comparing their predicted values with the experimental values. Data of the test group was used for this purpose. Correlation coefficients of R2 = 0.976 and R2 = 0.968 were achieved for Phenol and TOC models, respectively, which showed acceptable agreement between the outputs and the corresponding real values of both model. Fig. 8 presents a graphical comparison between the experimental data obtained after 20 minutes and the corresponding ANN predictions.


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)

Comparison between experimental and neural networking results for TOC removal % and Phenol degradation % (a.) 20 min time interval, (b.) 40 min time interval and (c.) 60 min time interval.
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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC4388832&req=5

pone.0119933.g008: Comparison between experimental and neural networking results for TOC removal % and Phenol degradation % (a.) 20 min time interval, (b.) 40 min time interval and (c.) 60 min time interval.
Mentions: Performance of the proposed models was evaluated by comparing their predicted values with the experimental values. Data of the test group was used for this purpose. Correlation coefficients of R2 = 0.976 and R2 = 0.968 were achieved for Phenol and TOC models, respectively, which showed acceptable agreement between the outputs and the corresponding real values of both model. Fig. 8 presents a graphical comparison between the experimental data obtained after 20 minutes and the corresponding ANN predictions.

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.