Recent advances in artificial intelligence and machine learning for nonlinear relationship analysis and process control in drinking water treatment: A review

2021 ◽  
Vol 405 ◽  
pp. 126673 ◽  
Author(s):  
Lei Li ◽  
Shuming Rong ◽  
Rui Wang ◽  
Shuili Yu
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Drinking Water ◽  
Water Treatment ◽  
Process Control ◽  
Drinking Water Treatment ◽  
Nonlinear Relationship ◽  
Relationship Analysis ◽  
Recent Advances

Drinking water quality and treatment: the use of artificial neural networks

2001 ◽  
Vol 28 (S1) ◽  
pp. 26-35 ◽  
Author(s):  
C W Baxter ◽  
Q Zhang ◽  
S J Stanley ◽  
R Shariff ◽  
R -RT Tupas ◽  
...  
Keyword(s):  
Neural Networks ◽  
Water Quality ◽  
Drinking Water ◽  
Artificial Neural Networks ◽  
Water Treatment ◽  
Process Control ◽  
Treatment Process ◽  
Drinking Water Treatment ◽  
Water Treatment Process ◽  
Artificial Neural

To improve drinking water quality while reducing operating costs, many drinking water utilities are investing in advanced process control and automation technologies. The use of artificial intelligence technologies, specifically artificial neural networks, is increasing in the drinking water treatment industry as they allow for the development of robust nonlinear models of complex unit processes. This paper highlights the utility of artificial neural networks in water quality modelling as well as drinking water treatment process modelling and control through the presentation of several case studies at two large-scale water treatment plants in Edmonton, Alberta.Key words: artificial neural networks, water treatment process control, water treatment modelling.

scholarly journals Integrating water quality and streamflow into prediction of chemical dosage in a drinking water treatment plant using machine learning algorithms

2021 ◽  
Author(s):  
Hui Wang ◽  
Tirusew Asefa ◽  
Jack Thornburgh
Keyword(s):  
Machine Learning ◽  
Water Quality ◽  
Drinking Water ◽  
Water Treatment ◽  
Mean Square Error ◽  
Learning Algorithms ◽  
Drinking Water Treatment ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Mean Square

Abstract Understanding the relationship between raw water quality and chemical dosage is especially important for drinking water treatment plants (DWTP) that have multiple water sources where the ratio of different supply sources could change with seasons or in a matter of weeks in response to changing hydrologic conditions. In this study, the potential for deploying machine learning algorithms, including principal component regression (PCR), support vector regression (SVR) and long short-term memory (LSTM) neural network, are tested to build predictive models. These tools were used to estimate chemical dosage at daily time scale. Influent water quality such as pH, color, turbidity, and alkalinity, as well as chemical dosage including sulfuric acid, ferric sulfate and liquid oxygen were used to build and test these models. An 80/20 percent data split was used for training and testing model performance using correlation coefficients, relative mean square error, relative root mean square error and Nash-Sutcliffe efficiency. Results indicate, compared to PCR, both SVR and LSTM, were able to capture the nonlinear relationship between chemical dose and source water quality changes and displayed higher predictive skills. These types of models have application in real-time operational support without requiring computationally expensive physics-based models.

ASSESSMENT OF ELECTROCOAGULATION PROCESS FOR DRINKING WATER TREATMENT

2015 ◽  
Vol 14 (6) ◽  
pp. 1347-1354 ◽  
Author(s):  
Florica Manea ◽  
Anamaria Baciu ◽  
Aniela Pop ◽  
Katalin Bodor ◽  
Ilie Vlaicu
Keyword(s):  
Drinking Water ◽  
Water Treatment ◽  
Drinking Water Treatment ◽  
Electrocoagulation Process

Drinking Water Surveillance Program in the St. Clair-Detroit River Area 1985/86

1986 ◽  
Vol 21 (3) ◽  
pp. 447-459 ◽  
Author(s):  
K.J. Roberts ◽  
R.B. Hunsinger ◽  
A.H. Vajdic
Keyword(s):  
Drinking Water ◽  
Water Treatment ◽  
Drinking Water Treatment ◽  
Surveillance Program ◽  
Sampling Protocol ◽  
Detroit River ◽  
Water Treatment Plants

Abstract The Drinking Water Surveillance Program (DWSP), developed by the Ontario Ministry of the Environment, is an assessment project based on standardized analytical and sampling protocol. This program was recently instituted in response to a series of contaminant occurrences in the St. Clair-Detroit River area of Southwestern Ontario. This paper outlines the details and goals of the program and provides information concerning micro-contaminants in drinking water at seven drinking water treatment plants in Southwestern Ontario.

Cost and Benefits of Drinking Water Treatment

1984 ◽  
Vol 14 (1) ◽  
pp. 1-30 ◽  
Author(s):  
Robert M. Clark ◽  
James A. Goodrich ◽  
John C. Ireland
Keyword(s):  
Drinking Water ◽  
Water Treatment ◽  
Drinking Water Treatment
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