An Approach for Coagulant Dosage Optimization Using Soft Jar Test: A Case Study of Bangkhen Water Treatment Plant
The most important process of the water treatment plant process is coagulation, which uses alum and poly aluminum chloride (PACL). Therefore, determining the dosage of alum and PACL is the most important factor to be prescribed. This research applies an artificial neural network (ANN), which uses the Levenberg–Marquardt algorithm to create a mathematical model (Soft Jar Test) for chemical dose prediction, as used for coagulation, such as alum and PACL, with input data consisting of turbidity, pH, alkalinity, conductivity, and, oxygen consumption (OC) of the Bangkhen Water Treatment Plant (BKWTP), under the authority of the Metropolitan Waterworks Authority of Thailand. The data were collected from 1 January 2019 to 31 December 2019 in order to cover the changing seasons of Thailand. The input data of ANN are divided into three groups: training set, test set, and validation set. The coefficient of determination and the mean absolute errors of the alum model are 0.73, 3.18 and the PACL model are 0.59, 3.21, respectively.Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 539
 S. Sasananan, "Water Treatment Plant Clarifier Control: An Artificial Intelligence Approach," Doctoral dissertation, University of Tasmania, Australia, 2009.
 E. E. Arasmith, Jar Test. Operational Control Tests for Wastewater Treatment Facilities. Instructor's Manual (and) Student Workbook. Linn-Benton Community College, 1981.
 M. Negnevitsky, Artificial Intelligence: A Guide to Intelligent Systems, 3rd ed. New York: Addison Wesley, 2011.pp. 205-215
 O. Degremont, Water Treatment Handbook, 6th ed. France, 1991.
 H. Maier, N. Morgan, and C. Chow, "Use of artificial neural networks for predicting optimal alum doses and treated water quality parameters," Environmental Modelling and Software, vol. 19, pp. 485-494, 05/01 2004, doi: 10.1016/S1364-8152(03)00163-4.
 Mark Hudson Beale, Martin T. Hagan, and H. B. Demuth, Deep Learning Toolbox™ User's Guide. 2020.
 W. Kuanthong, W. Liamlaem, and S. Sasananan, "Clarifier Models using Artificial Neural Networks - Case Study: Bangkhen Water Treatment Plant," SWU Engineering Journal, vol. 10, no. 1, pp. 32-44, 2015.