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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.


Relative importance of input variables on (a) Phenol conversion model, (b) TOC removal model.
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pone.0119933.g009: Relative importance of input variables on (a) Phenol conversion model, (b) TOC removal model.

Mentions: Studies on weight matrix are necessary for evaluating the relative importance of each input variable on output variable. In this regard, the following equation which is based on the partitioning of connecting weights was mainly used [47] in this study:Ig=∑m=1m=Nh((/Wgmih/∑k=1Ni/Wkmih/)×/Wmnho/)∑m=1k=Ni{∑m=1m=Nh((/Wgmih/∑k=1Ni/Wkmih/)×/Wmnho/)}(10)where Ig, Ni, Nh, W, i, h, o, k, m, and n, refer to the relative impact of the g-th input variable on the output variable, number of input neurons, number of hidden neurons, connection weight, input layer, hidden layer, output layer, input neuron number, hidden neuron number, and output neuron number, respectively. Fig. 9 represents the relative importance of the input variables for both 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)

Relative importance of input variables on (a) Phenol conversion model, (b) TOC removal model.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0119933.g009: Relative importance of input variables on (a) Phenol conversion model, (b) TOC removal model.
Mentions: Studies on weight matrix are necessary for evaluating the relative importance of each input variable on output variable. In this regard, the following equation which is based on the partitioning of connecting weights was mainly used [47] in this study:Ig=∑m=1m=Nh((/Wgmih/∑k=1Ni/Wkmih/)×/Wmnho/)∑m=1k=Ni{∑m=1m=Nh((/Wgmih/∑k=1Ni/Wkmih/)×/Wmnho/)}(10)where Ig, Ni, Nh, W, i, h, o, k, m, and n, refer to the relative impact of the g-th input variable on the output variable, number of input neurons, number of hidden neurons, connection weight, input layer, hidden layer, output layer, input neuron number, hidden neuron number, and output neuron number, respectively. Fig. 9 represents the relative importance of the input variables for both 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.