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Artificial neural networks, optimization and kinetic modeling of amoxicillin degradation in photo-fenton process using aluminum pillared montmorillonite-supported ferrioxalate catalyst / O. B. Ayodele in Industrial & engineering chemistry research, Vol. 51 N° 50 (Décembre 2012) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 51 N° 50 (Décembre 2012) . - pp. 16311-16319 Titre : Artificial neural networks, optimization and kinetic modeling of amoxicillin degradation in photo-fenton process using aluminum pillared montmorillonite-supported ferrioxalate catalyst Type de document : texte imprimé Auteurs : O. B. Ayodele, Auteur ; H. S. Auta, Auteur ; N. Md Nor, Auteur Année de publication : 2013 Article en page(s) : pp. 16311-16319 Note générale : Industrial chemistry Langues : Anglais (eng) Mots-clés : Modeling  Catalyst  Montmorillonite  Fenton  reaction  PhotooxidationKinetic  model  Optimization  Neural  network Résumé : An artificial neural network (ANN) was applied to study the hierarchy of significance of process variables affecting the degradation of amoxicillin (AMX) in a heterogeneous photo-Fenton process. Catalyst and H2O2 dosages were found to be the most significant variables followed by degradation time and concentration of AMX. The significant variables were optimized and the optimum condition to achieve degradation of 97.87% of 40 ppm AMX was 21.54% excess H2O2 dosage, 2.24 g of catalyst in 10 min. A mathematical model (MM) for the degradation of AMX was developed on the basis of the combined results of the ANN and the optimization studies. The MM result showed that increases in both catalyst and H2O2 dosage enhanced the rate of AMX degradation as shown by the rate constants evaluated from the model. The highest rate constant at the optimum conditions was 122 M―1 S―1. These results provided invaluable insights into the catalytic degradation of AMX in photo-Fenton process. ISSN : 0888-5885 En ligne : http://cat.inist.fr/?aModele=afficheN&cpsidt=26732157 [article] Artificial neural networks, optimization and kinetic modeling of amoxicillin degradation in photo-fenton process using aluminum pillared montmorillonite-supported ferrioxalate catalyst [texte imprimé] / O. B. Ayodele, Auteur ; H. S. Auta, Auteur ; N. Md Nor, Auteur . - 2013 . - pp. 16311-16319. Industrial chemistry Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 51 N° 50 (Décembre 2012) . - pp. 16311-16319 Mots-clés : Modeling  Catalyst  Montmorillonite  Fenton  reaction  PhotooxidationKinetic  model  Optimization  Neural  network Résumé : An artificial neural network (ANN) was applied to study the hierarchy of significance of process variables affecting the degradation of amoxicillin (AMX) in a heterogeneous photo-Fenton process. Catalyst and H2O2 dosages were found to be the most significant variables followed by degradation time and concentration of AMX. The significant variables were optimized and the optimum condition to achieve degradation of 97.87% of 40 ppm AMX was 21.54% excess H2O2 dosage, 2.24 g of catalyst in 10 min. A mathematical model (MM) for the degradation of AMX was developed on the basis of the combined results of the ANN and the optimization studies. The MM result showed that increases in both catalyst and H2O2 dosage enhanced the rate of AMX degradation as shown by the rate constants evaluated from the model. The highest rate constant at the optimum conditions was 122 M―1 S―1. These results provided invaluable insights into the catalytic degradation of AMX in photo-Fenton process. ISSN : 0888-5885 En ligne : http://cat.inist.fr/?aModele=afficheN&cpsidt=26732157