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

Baxter, Christopher W.; Zhang, Q.; Stanley, Stephen; Shariff, Riyaz; Tupas, R.-R.T.; Stark, Helmut

doi:10.1139/l00-053

Cited by 40 publications

(15 citation statements)

References 11 publications

(10 reference statements)

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“…The temperature of surface water, for instance, varies depending on the season of the year. Therefore the source dataset must encompass at least one full year of measured data (Baxter et al, 2001).…”

Section: Datasetmentioning

confidence: 99%

“…This makes it impossible to develop a useful mechanistic model. Using an ANN model gives the ability to quickly modify process models using full-scale operation data without necessity to understand all micro-scale interactions (Baxter et al, 2001;Maier et al, 2004).…”

Section: Introductionmentioning

confidence: 99%

“…The supremacy of ANN models indicates nonlinear relationships in used datasets. In the water treatment and drinking water production, ANN models have been successfully used to among others modeling and predicting chlorine residual in a water distribution system (Bowden et al, 2006), drinking water quality (Baxter et al, 2001), contaminant removal (Shetty et al, 2003), fouling and backwash efficiency in ultrafiltration (Delgrange-Vincent et al, 2000), optimal aluminum doses (Maier et al, 2004) and residual aluminum (Juntunen et al, 2010) in the water treatment process. Baxter et al (2001) created also a prediction model to provide plant operators with an early warning system (EWS) for raw water quality changes and to improve treatment efficiency.…”

Section: Introductionmentioning

confidence: 99%

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Predicting the residual aluminum level in water treatment process

Tomperi

Pelo²,

Leiviskä

2013

Drink. Water Eng. Sci.

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Abstract. In water treatment processes, aluminum salts are widely used as coagulation chemical. High dose of aluminum has been proved to be at least a minor health risk and some evidence points out that aluminum could increase the risk of Alzheimer's disease. Thus it is important to minimize the amount of residual aluminum in drinking water and water used at food industry. In this study, the data of a water treatment plant (WTP) was analyzed and the residual aluminum in drinking water was predicted using Multiple Linear Regression (MLR) and Artificial Neural Network (ANN) models. The purpose was to find out which variables affect the amount of residual aluminum and create simple and reliable prediction models which can be used in an early warning system (EWS). Accuracy of ANN and MLR models were compared. The new nonlinear scaling method based on generalized norms and skewness was used to scale all measurement variables to range [−2 . . . + 2] before data-analysis and modeling. The effect of data pre-processing was studied by comparing prediction results to ones achieved in an earlier study. Results showed that it is possible to predict the baseline level of residual aluminum in drinking water with a simple model. Variables that affected the most the amount of residual aluminum were among others: raw water temperature, raw water KMnO 4 and PAC/KMnO 4 (Poly-Aluminum Chloride/Potassium permanganate)-ratio. The accuracies of MLR and ANN models were found to be almost the same. Study also showed that data pre-processing affects to the final prediction result.

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Section: Datasetmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Predicting the residual aluminum level in water treatment process

Tomperi

Pelo²,

Leiviskä

2013

Drink. Water Eng. Sci.

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“…Relatively new technique of using ANNs researched for forecasting short-term water demand [20]. ANNs in water quality modeling, as well as for the process and control of treating drinking water used in water distribution systems [21]. Research on the application of ANNs for analysis of data from sensors measuring hydraulic parameters are presented [22].…”

Section: Introductionmentioning

confidence: 99%

Estimation of Non-Revenue Water Ratio for Sustainable Management Using Artificial Neural Network and Z-Score in Incheon, Republic of Korea

Jang

Choi

2017

Sustainability

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Abstract:The non-revenue water (NRW) ratio in a water distribution system is the ratio of the loss due to unbilled authorized consumption, apparent losses and real losses to the overall system input volume (SIV). The method of estimating the NRW ratio by measurement might not work in an area with no district metered areas (DMAs) or with unclear administrative district. Through multiple regression analyses is a statistical analysis method for calculating the NRW ratio using the main parameters of the water distribution system, although its disadvantage is lower accuracy than that of the measured NRW ratio. In this study, an artificial neural network (ANN) was used to estimate the NRW ratio. The results of the study proved that the accuracy of NRW ratio calculated by the ANN model was higher than by multiple regression analysis. The developed ANN model was shown to have an accuracy that varies depending on the number of neurons in the hidden layer. Therefore, when using the ANN model, the optimal number of neurons must be determined. In addition, the accuracy of the outlier removal condition was higher than that of the original data used condition.

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“…Ils expriment tous la dose du coagulant à injecter en fonction des différentes variables descriptives caractérisant l'eau brute à l'entrée de la station de traitement des eaux. Certaines études (BAxTER et al, 2001a;BAxTER et al, 2001b;BAxTER et al, 2002;COx et al, 2003;HEDDAM et al, 2011;LAMRINI et al, 2005) ont montré l'importance des réseaux de neurones comme outil pour l'élaboration des modèles mathématiques à des fins d'automatisation et de supervision des procédés impliqués dans les stations de traitement des eaux.…”

Section: Introductionunclassified

Modélisation de la dose de coagulant par les systèmes à base d’inférence floue (ANFIS) application à la station de traitement des eaux de Boudouaou (Algérie)

Salim

Bermad²,

Dechemi

2012

rseau

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La coagulation est l’une des étapes les plus importantes dans le traitement des eaux. La difficulté principale est de déterminer la dose optimale de coagulant à injecter en fonction des caractéristiques de l’eau brute. Un mauvais contrôle de ce procédé peut entraîner une augmentation importante des coûts de fonctionnement et le non-respect des objectifs de qualité en sortie de la station de traitement. Le sulfate d’aluminium (Al2SO4.18H2O) est le réactif coagulant le plus généralement utilisé. La détermination de la dose de coagulant se fait au moyen de l’essai dit de « Jar Test » conduit en laboratoire. Ce type d’approche a le désavantage d’avoir un temps de retard relativement long et ne permet donc pas un contrôle automatique du procédé de coagulation. Le présent article décrit un modèle neuro flou de type Takagi Sugeno (TK), développé pour la prédiction de la dose de coagulant utilisée lors de la phase de clarification dans la station de traitement des eaux de Boudouaou qui alimente la ville d’Alger en eau potable. Le modèle ANFIS (système d’inférence flou à base de réseaux de neurones adaptatifs), qui combine les techniques floues et neuronales en formant un réseau à apprentissage supervisé, a été appliqué durant la phase de calage et testé en période de validation. Les résultats obtenus par le modèle ANFIS ont été comparés avec ceux obtenus avec un réseau de neurones de type perceptron multicouche (MLP) et un troisième modèle à base de regression linéaire multiple (MLR). Un coefficient de détermination (R2) de l’ordre de 0,92 en période de validation a été obtenu avec le modèle ANFIS, alors que pour le MLP, il est de l’ordre de 0,75, et que pour le modèle MLR, il ne dépasse pas 0,35. Les résultats obtenus sont d’une grande importance pour la gestion de l’installation.

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

Cited by 40 publications

References 11 publications

Predicting the residual aluminum level in water treatment process

Predicting the residual aluminum level in water treatment process

Estimation of Non-Revenue Water Ratio for Sustainable Management Using Artificial Neural Network and Z-Score in Incheon, Republic of Korea

Modélisation de la dose de coagulant par les systèmes à base d’inférence floue (ANFIS) application à la station de traitement des eaux de Boudouaou (Algérie)

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