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A GA - based neural fuzzy system for modeling a paper mill wastewater treatment process / Mingzhi Huang in Industrial & engineering chemistry research, Vol. 50 N° 23 (Décembre 2011) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 50 N° 23 (Décembre 2011) . - pp. 13500–13507 Titre : A GA - based neural fuzzy system for modeling a paper mill wastewater treatment process Type de document : texte imprimé Auteurs : Mingzhi Huang, Auteur ; Jinquan Wan, Auteur ; Yongwen Ma, Auteur Année de publication : 2012 Article en page(s) : pp. 13500–13507 Note générale : Chimie industrielle Langues : Anglais (eng) Mots-clés : Neural  fuzzy  system  Wastewater  treatment Résumé : A genetic algorithm-based neural fuzzy system (GA-NFS) was presented for studying the coagulation process of wastewater treatment in a paper mill. In order to adapt the system to a variety of operating conditions and acquire a more flexible learning ability, the GA-NFS was employed to model the nonlinear relationships between the effluent concentration of pollutants and the chemical dosages, and a hybrid learning algorithm divided into two stages was proposed for parameters learning. During the first learning stage, a genetic algorithm was used to optimize the structure of GA-NFS and the membership function of each fuzzy term due to its capability of parallel and global search. On the basis of an optimized training stage, the back-propagation algorithm (BP algorithm) was chosen to update the parameters of GA-NFS to improve the system precision. The GA-NFS proves to be very effective in modeling coagulation perform and performs better than adaptive-network-based fuzzy inference system (ANFIS). RMSE, MAPE, and R between the predicted and observed values for GA-NFS were only 0.01099, 2.3337, and 0.9375, respectively. DEWEY : 660 ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie201296p [article] A GA - based neural fuzzy system for modeling a paper mill wastewater treatment process [texte imprimé] / Mingzhi Huang, Auteur ; Jinquan Wan, Auteur ; Yongwen Ma, Auteur . - 2012 . - pp. 13500–13507. Chimie industrielle Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 50 N° 23 (Décembre 2011) . - pp. 13500–13507 Mots-clés : Neural  fuzzy  system  Wastewater  treatment Résumé : A genetic algorithm-based neural fuzzy system (GA-NFS) was presented for studying the coagulation process of wastewater treatment in a paper mill. In order to adapt the system to a variety of operating conditions and acquire a more flexible learning ability, the GA-NFS was employed to model the nonlinear relationships between the effluent concentration of pollutants and the chemical dosages, and a hybrid learning algorithm divided into two stages was proposed for parameters learning. During the first learning stage, a genetic algorithm was used to optimize the structure of GA-NFS and the membership function of each fuzzy term due to its capability of parallel and global search. On the basis of an optimized training stage, the back-propagation algorithm (BP algorithm) was chosen to update the parameters of GA-NFS to improve the system precision. The GA-NFS proves to be very effective in modeling coagulation perform and performs better than adaptive-network-based fuzzy inference system (ANFIS). RMSE, MAPE, and R between the predicted and observed values for GA-NFS were only 0.01099, 2.3337, and 0.9375, respectively. DEWEY : 660 ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie201296p

Modeling a paper - making wastewater treatment process by means of an adaptive network - based fuzzy inference system and principal component analysis / Mingzhi Huang in Industrial & engineering chemistry research, Vol. 51 N° 17 (Mai 2012) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 51 N° 17 (Mai 2012) . - pp. 6166–6174 Titre : Modeling a paper - making wastewater treatment process by means of an adaptive network - based fuzzy inference system and principal component analysis Type de document : texte imprimé Auteurs : Mingzhi Huang, Auteur ; Yongwen Ma, Auteur ; Jinquan Wan, Auteur Année de publication : 2012 Article en page(s) : pp. 6166–6174 Note générale : Industrial chemistry Langues : Anglais (eng) Mots-clés : Paper  making  wastewater  treatment  process Résumé : In this paper, a predictive control system based on an adaptive network-based fuzzy inference system (ANFIS) was employed to develop models for predicting and controlling the performance of a paper-making wastewater treatment process. The system includes an ANFIS predictive model and an ANFIS controller. In order to improve the network performance, fuzzy subtractive clustering, euclidean distance clustering, and principal component analysis (PCA) were used to identify model architecture and extract and optimize the fuzzy rule of the model. For the developed predictive model, when predicting, mean absolute percentage error (MAPE) lay 6.06% adopting ANFIS, root mean square normalized error (RMSE) was 24.4485 and R was 0.9731. The control model, taking into account the difference between the predicted value of chemical oxygen demand (COD) and the set point, can effectively change the additive dosages. In order to verify the developed predictive control model, a paper-making wastewater treatment process was picked up to support the operational performance. When the influent COD value or inflow flow rate was changed, the dosage could be accurately adjusted to make the effluent COD remain at the set point, and its MAPE was only 5.19%. The results indicated that reasonable forecasting and controlling performances had been achieved through the developed system. ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie203049r [article] Modeling a paper - making wastewater treatment process by means of an adaptive network - based fuzzy inference system and principal component analysis [texte imprimé] / Mingzhi Huang, Auteur ; Yongwen Ma, Auteur ; Jinquan Wan, Auteur . - 2012 . - pp. 6166–6174. Industrial chemistry Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 51 N° 17 (Mai 2012) . - pp. 6166–6174 Mots-clés : Paper  making  wastewater  treatment  process Résumé : In this paper, a predictive control system based on an adaptive network-based fuzzy inference system (ANFIS) was employed to develop models for predicting and controlling the performance of a paper-making wastewater treatment process. The system includes an ANFIS predictive model and an ANFIS controller. In order to improve the network performance, fuzzy subtractive clustering, euclidean distance clustering, and principal component analysis (PCA) were used to identify model architecture and extract and optimize the fuzzy rule of the model. For the developed predictive model, when predicting, mean absolute percentage error (MAPE) lay 6.06% adopting ANFIS, root mean square normalized error (RMSE) was 24.4485 and R was 0.9731. The control model, taking into account the difference between the predicted value of chemical oxygen demand (COD) and the set point, can effectively change the additive dosages. In order to verify the developed predictive control model, a paper-making wastewater treatment process was picked up to support the operational performance. When the influent COD value or inflow flow rate was changed, the dosage could be accurately adjusted to make the effluent COD remain at the set point, and its MAPE was only 5.19%. The results indicated that reasonable forecasting and controlling performances had been achieved through the developed system. ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie203049r