Application of artificial neural networks in wastewater treatment. (1st April 2004)

• Record Type: Journal Article
• Authors: El-Din, Ahmed Gamal; Smith, Daniel W; El-Din, Mohamed Gamal
• Is Part Of: Journal of environmental engineering and science. Volume 3(2004)Supplement 1
• Page Start: S81
• Page End: S95
• 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/s03-067
• Languages: English
• ISSNs: 1496-2551

Abstract

Abstract : In the past few years, artificial neural networks (ANNs) have been used in describing and modelling wastewater treatment processes. Artificial neural network models can be identified without a detailed knowledge of the kinetics of the system to be modelled. Also, ANN models can potentially contain a great deal of information about the system itself, including the same type of information contained in conventional deterministic models. The fact that these models can be continuously updated with minimal resource requirements makes them very attractive for application in a real-time control scenario. In the current paper, applications of ANNs in the field of wastewater treatment performance prediction are reviewed. In addition, this paper presents a case study that reports some comprehensive modelling work to develop nonlinear neural network prediction models for the Gold Bar Wastewater Treatment Plant (GBWWTP), the largest sewage treatment facility in Edmonton, Alberta. Key words: wastewater treatment, artificial neural networks, flow prediction, primary sedimentation, activated sludge.