Raw water quality variation has a great effect on drinking water treatment. To improve the adaptivity of drinking water treatment and stabilize the quality of treated water, a raw water quality assessment method, which is based upon the support vector machine (SVM), is developed in this study. Compared to existing raw water quality assessment methods, the assessment method studied herein is oriented to drinking water treatment and can directly be used for the control of the chemical (alum and ozone) dosing process. To this end, based upon the productive experiences and the analysis of the operating data of water supply, a raw water quality assessment standard oriented to drinking water treatment is proposed. A raw water quality model is set up to assess the raw water quality based upon the SVM technique. Based upon the raw water quality assessment results, a feedforward–feedback control scheme has been designed for the chemical dosing process control of drinking water treatment. Thus, the chemical dosage can be adjusted in time to cope with raw water quality variations and hence, the quality of the treated water is stabilized. Experimental results demonstrate the improved effectiveness of the proposed method of raw water quality assessment and the feedforward–feedback control scheme.
Skip Nav Destination
Article navigation
Research Article|
December 29 2015
Research on raw water quality assessment oriented to drinking water treatment based on the SVM model
Dongsheng Wang
1School of Automation, Nanjing University of Posts and Telecommunication, Nanjing 210003, China
E-mail: [email protected]
Search for other works by this author on:
Water Supply (2016) 16 (3): 746–755.
Article history
Received:
May 20 2015
Accepted:
December 14 2015
Citation
Dongsheng Wang; Research on raw water quality assessment oriented to drinking water treatment based on the SVM model. Water Supply 1 June 2016; 16 (3): 746–755. doi: https://doi.org/10.2166/ws.2015.186
Download citation file:
Sign in
Don't already have an account? Register
Client Account
You could not be signed in. Please check your email address / username and password and try again.
Could not validate captcha. Please try again.
eBook
Pay-Per-View Access
$38.00