Neural network models of Cryptosporidium parvum inactivation by chlorine dioxide and ozone. Issue 5 (2nd August 2007)

• Record Type: Journal Article
• Authors: Janes, Kevin R.; Musilek, Petr
• Is Part Of: Journal of environmental engineering and science. Volume 6:Issue 5(2007)
• Page Start: 477
• Page End: 482
• Publication Date: 2007-08-02
• Subjects: disinfection model -- neural network -- cryptosporidium parvum -- ozone -- chlorine dioxide; modèle de désinfection -- réseau neuronal -- Cryptosporidium parvum -- ozone -- dioxyde de chlore; Environmental engineering -- Periodicals; Sanitary engineering -- Periodicals; Environmental engineering -- Canada -- Periodicals; Environnement, Technique de l' -- Périodiques; Technique sanitaire -- Périodiques; Environnement, Technique de l' -- Canada -- Périodiques; Environmental engineering; Sanitary engineering; Canada; Electronic journals; Computer network resources; Periodicals; 628.05
• DOI: 10.1139/S07-012
• Languages: English
• ISSNs: 1496-2551

Abstract

Abstract : Several neural network disinfection models for the inactivation ofCryptosporidium parvum by chlorine dioxide and ozone were developed and compared against existing temperature-corrected Chick-Watson models. In this study a back propagation based network-pruning algorithm called structural learning with forgetting has been used to train all neural network models. The neural network disinfection models performed well relative to the competing Chick-Watson models and learned several basic input variable trends established in earlier disinfection studies. Water temperature was found to be a significant process variable and pH less influential for all neural network models. The final disinfectant residual was included as an input variable to incorporate residual decay, and was found to be more relevant for disinfectants with less stable residuals.