Empirical mathematical models and artificial neural networks for the determination of alum doses for treatment of southern Australian surface waters

Abstract

Research Article| May 01 1999 Empirical mathematical models and artificial neural networks for the determination of alum doses for treatment of southern Australian surface waters J. van Leeuwen; J. van Leeuwen Search for other works by this author on: This Site PubMed Google Scholar C. W. K. Chow; C. W. K. Chow Search for other works by this author on: This Site PubMed Google Scholar D. Bursill; D. Bursill Search for other works by this author on: This Site PubMed Google Scholar M. Drikas M. Drikas Search for other works by this author on: This Site PubMed Google Scholar Journal of Water Supply: Research and Technology-Aqua (1999) 48 (3): 115–127. https://doi.org/10.2166/aqua.1999.0012 Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Share Icon Share Twitter LinkedIn Tools Icon Tools Cite Icon Cite Permissions Search Site Search nav search search input Search input auto suggest search filter All ContentAll JournalsThis Journal Search Advanced Search Citation J. van Leeuwen, C. W. K. Chow, D. Bursill, M. Drikas; Empirical mathematical models and artificial neural networks for the determination of alum doses for treatment of southern Australian surface waters. Journal of Water Supply: Research and Technology-Aqua 1 May 1999; 48 (3): 115–127. doi: https://doi.org/10.2166/aqua.1999.0012 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex The potential for predicting alum doses for surface waters from southern Australia based on physico-chemical parameters of the raw waters was studied. These parameters included dissolved organic carbon (DOC), absorbance at 254 nm, turbidity and alkalinity. Procedures used for assessing the predictability of alum dosing were empirical mathematical models and artificial neural networks.Alum doses determined by jar tests were selected on the basis of target values for settled and filtered turbidities, colour and residual aluminium.Regression equations which incorporated the parameters of DOC, UV absorbance (254 nm/cm), turbidity, alkalinity and pH gave correlation coefficients of greater than 0.9. These equations gave a high frequency of prediction within ±10 mg/L alum of actual doses. Similarly, 86% of alum doses predicted by artificial neural networks were within 10 mg/L of the actual doses. Although a good prediction of coagulant dosing was achieved, it is likely that the models generated are specific for the types of waters studied and the criteria for alum dose selection. This content is only available as a PDF. © IWA Publishing 1999 You do not currently have access to this content.