Artificial neural networks applied for simultaneous analysis of mixtures of nitrophenols by conductometric acid — base titration / Gholamhossein Rounaghi in Industrial & engineering chemistry research, Vol. 50 N° 19 (Octobre 2011)  

Public ISBD [article]  inIndustrial & engineering chemistry research > Vol. 50 N° 19 (Octobre 2011) . - pp. 11375-11381 Titre : Artificial neural networks applied for simultaneous analysis of mixtures of nitrophenols by conductometric acid — base titration Type de document : texte imprimé Auteurs : Gholamhossein Rounaghi, Auteur ; Roya Mohammad Zadeh Kakhki, Auteur ; Tahereh Heidari, Auteur Année de publication : 2011 Article en page(s) : pp. 11375-11381 Note générale : Chimie industrielle Langues : Anglais (eng) Mots-clés : Neural network Résumé : In this study, the simultaneous conductometric titration method for determination of mixtures of 4-nitrophenol, 2,4-dinitrophenol, and 2,4,6- trinitrophenol based on principal component artificial neural network (ANN) calibration model was proposed. The three-layered feed-forward ANN trained by back-propagation learning was used to model the complex nonlinear relationship between the concentration of 4-nitrophenol, 2,4-dinitrophenol, and 2,4,6-trinitrophenol in their ternary mixtures and the conductance of the solutions at different volumes of titrant. The principal components of the conductance matrix were used as the input of the network. The network architecture and parameters were optimized to give low prediction error. The optimized networks predicted the concentrations of nitrophenols in synthetic mixtures. The results showed that the usedANN can proceed the titration data with low relative prediction errors (5.53%, 4.03%, and 4.71% for 4-nitrophenol, 2,4-dinitrophenol, and 2,4,6-trinitrophenol, respectively) and satisfactory recoveries. DEWEY : 660 ISSN : 0888-5885 En ligne : http://cat.inist.fr/?aModele=afficheN&cpsidt=24573334 [article] Artificial neural networks applied for simultaneous analysis of mixtures of nitrophenols by conductometric acid — base titration [texte imprimé] / Gholamhossein Rounaghi, Auteur ; Roya Mohammad Zadeh Kakhki, Auteur ; Tahereh Heidari, Auteur . - 2011 . - pp. 11375-11381. Chimie industrielle Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 50 N° 19 (Octobre 2011) . - pp. 11375-11381 Mots-clés : Neural network Résumé : In this study, the simultaneous conductometric titration method for determination of mixtures of 4-nitrophenol, 2,4-dinitrophenol, and 2,4,6- trinitrophenol based on principal component artificial neural network (ANN) calibration model was proposed. The three-layered feed-forward ANN trained by back-propagation learning was used to model the complex nonlinear relationship between the concentration of 4-nitrophenol, 2,4-dinitrophenol, and 2,4,6-trinitrophenol in their ternary mixtures and the conductance of the solutions at different volumes of titrant. The principal components of the conductance matrix were used as the input of the network. The network architecture and parameters were optimized to give low prediction error. The optimized networks predicted the concentrations of nitrophenols in synthetic mixtures. The results showed that the usedANN can proceed the titration data with low relative prediction errors (5.53%, 4.03%, and 4.71% for 4-nitrophenol, 2,4-dinitrophenol, and 2,4,6-trinitrophenol, respectively) and satisfactory recoveries. DEWEY : 660 ISSN : 0888-5885 En ligne : http://cat.inist.fr/?aModele=afficheN&cpsidt=24573334

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