Lime softening clarifier modeling with artificial neural networks. (1st April 2004)

• Record Type: Journal Article
• Authors: Shariff, Riyaz; Cudrak, Audrey; Stanley, Stephen J
• Is Part Of: Journal of environmental engineering and science. Volume 3(2004)Supplement 1
• Page Start: S69
• Page End: S80
• Publication Date: 2004-04-01
• Subjects: environment; 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/s03-065
• Languages: English
• ISSNs: 1496-2551

Abstract

Abstract : This paper examines the application of the artificial neural network (ANN) modeling technique to model a lime softening process at a full-scale drinking water treatment facility. The modeling was done for the Rossdale Water Treatment Plant (WTP) operated by EPCOR Water Services Inc. in Edmonton, Alberta. It was determined that ANN can model a lime clarifier accurately and with superior performance to other modeling methods. During the development stage, a prediction of alum clarifier pH also becomes necessary, and a very accurate inferential (virtual) sensor for pH was developed using ANN. The ANN models were also integrated with the Supervisory Control and Data Acquisition (SCADA) system of the plant so that real-time predictions of lime doses and effluent total hardness could be monitored. It was shown that the performance of the ANN models that were developed using average daily values for the parameters also work well when they are executed in real time. Key words: artificial neural networks, water softening, hardness, virtual analyzer, forward and inverse models.