Successful prediction for coagulant dosage and effluent turbidity of a coagulation process in a drinking water treatment plant based on the Elman neural network and random forest models-Reference-Cited by-同舟云学术

Successful prediction for coagulant dosage and effluent turbidity of a coagulation process in a drinking water treatment plant based on the Elman neural network and random forest models

Published:2023 Issue:9 Volume:9 Page:2263-2274
ISSN:2053-1400
Container-title:Environmental Science: Water Research & Technology
language:en
Short-container-title:Environ. Sci.: Water Res. Technol.

Author:

Wang Dongsheng¹,Chen Le¹,Li Taiyang¹,Chang Xiao¹,Ma Kaiwei¹²,You Weihong³,Tan Chaoqun³^ORCID

Affiliation:

1. College of Automation & College of Artificial Intelligence, Nanjing University of Posts and Telecommunications, Nanjing 210023, China

2. Key Laboratory of Measurement and Control of Complex Systems of Engineering, Ministry of Education, Southeast University, Nanjing 210096, China

3. School of Civil Engineering, Southeast University, Nanjing 210096, China

Abstract

The uncertainty of the changes in the quality of raw water, and the long lag in the process of coagulation introduce significant difficulties in eliminating turbidity during the treatment of drinking water.

Funder

National Natural Science Foundation of China

Key Laboratory of Measurement and Control of Complex Engineering Systems

Nanjing University of Posts and Telecommunications

Publisher

Royal Society of Chemistry (RSC)

Subject

Water Science and Technology,Environmental Engineering

Link

http://pubs.rsc.org/en/content/articlepdf/2023/EW/D3EW00181D

Reference56 articles.

1. Removal of micron-sized microplastic particles from simulated drinking water via alum coagulation