Modelling chlorine residuals in drinking water: a review

Onyutha, Charles; Kwio-Tamale, J. C.

doi:10.1007/s13762-022-03924-3

Cited by 22 publications

(18 citation statements)

References 80 publications

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“…Besides, as a lethal contaminant, cyanide is a critical criterion in maintaining water safety [25]. Affordability, ease of access, immediate impact, effectiveness even at low concentrations ([CN − ] > 70 µg L −1 ), no physical traces (like color, odor, or turbidity), and the possibility of contamination by industries, such as metal plating, makes cyanide a potential terroristic threat [26].…”

Section: Millionsmentioning

confidence: 99%

“…The outcomes of data availability in the form of a heat map are demonstrated in Figure 9 for initial CN -, ammonia, injected Cl2, RCN -(Figure 9a), T, pH (Figure 9b), and VDS (Figure 9c). Per a literature review [26,52], it can be found that all VDS (mgL −1 ), ammonia, and water temperatures affect chlorine decay directly, and increasing them, CN anions can release greater water bulk. Therefore, if the concentrations of ammonia and VDS are increased, chlorine should be injected in a relative amount based on specific logic.…”

Section: Ai and Soft-sensor Designmentioning

confidence: 99%

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A Sustainable Decision Support System for Drinking Water Systems: Resiliency Improvement against Cyanide Contamination

et al. 2022

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Maintaining drinking water quality is considered important in building sustainable cities and societies. On the other hand, water insecurity is an obstacle to achieving sustainable development goals based on the issues of threatening human health and well-being and global peace. One of the dangers threatening water sources is cyanide contamination due to industrial wastewater leakage or sabotage. The present study investigates and provides potential strategies to remove cyanide contamination by chlorination. In this regard, the main novelty is to propose a sustainable decision support system for the dirking water system in a case study in Iran. First, three scenarios have been defined with low ([CN−] = 2.5 mg L−1), medium ([CN−] = 5 mg L−1), and high ([CN−] = 7.5 mg L−1) levels of contamination. Then, the optimal chlorine dosage has been suggested as 2.9 mg L−1, 4.7 mg L−1, and 6.1 mg L−1, respectively, for these three scenarios. In the next step, the residual cyanide was modelled with mathematical approaches, which revealed that the Gaussian distribution has the best performance accordingly. The main methodology was developing a hybrid approach based on the Gaussian model and the genetic algorithm. The outcomes of statistical evaluations illustrated that both injected chlorine and initial cyanide load have the greatest effects on residual cyanide ions. Finally, the proposed hybrid algorithm is characterized by the multilayer perceptron algorithm, which can forecast residual cyanide anion with a regression coefficient greater than 0.99 as a soft sensor. The output can demonstrate a strong positive relationship between residual cyanide- (RCN−) and injected chlorine. The main finding is that the proposed sustainable decision support system with our hybrid algorithm improves the resiliency levels of the considered drinking water system against cyanide treatments.

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Section: Millionsmentioning

confidence: 99%

Section: Ai and Soft-sensor Designmentioning

confidence: 99%

A Sustainable Decision Support System for Drinking Water Systems: Resiliency Improvement against Cyanide Contamination

et al. 2022

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“…The ability of ANNs to incorporate routine water quality variables other than just upstream residual chlorine is an advantage ANNs possess over process-based models of FRC decay [37,38]. In humanitarian response, water quality data may be limited by constraints on data collection, limited water quality analysis capacities, or lack of reagents for field monitoring [58,59].…”

Section: Model Descriptionmentioning

confidence: 99%

“…Recent research has used data-driven modelling for a complex range of tasks, e.g., controlling dosing of chlorine [29] and other oxidants [30], predicting disinfection by-product formation [31], optimizing cyanide removal [32], and detecting bacterial growth in water samples using image analysis [33]. These models have been used for over two decades to model chlorine residuals in distribution systems, either as standalone models [34][35][36][37][38] or as part of a hybrid data-driven and process-based modelling system [19]. One of the most common and effective branches of data-driven models used in drinking water-especially for modelling chlorine residuals-are artificial neural networks (ANNs) [27,30,[34][35][36]38], though none of these previous studies have modelled chlorine residuals in the post-distribution period.…”

Section: Introductionmentioning

confidence: 99%

“…These models have been used for over two decades to model chlorine residuals in distribution systems, either as standalone models [34][35][36][37][38] or as part of a hybrid data-driven and process-based modelling system [19]. One of the most common and effective branches of data-driven models used in drinking water-especially for modelling chlorine residuals-are artificial neural networks (ANNs) [27,30,[34][35][36]38], though none of these previous studies have modelled chlorine residuals in the post-distribution period. ANNs have been used for probabilistic modelling in an EFS [21,22], though we are not aware of any ANN-EFS being used in drinking water quality modelling, beyond our previous work which used an ANN-EFS to generate risk-based FRC targets by predicting the probability of water users having insufficient point-of-consumption FRC [18].…”

Section: Introductionmentioning

confidence: 99%

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Modelling point-of-consumption residual chlorine in humanitarian response: Can cost-sensitive learning improve probabilistic forecasts?

et al. 2022

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Ensuring sufficient free residual chlorine (FRC) up to the time and place water is consumed in refugee settlements is essential for preventing the spread of waterborne illnesses. Water system operators need accurate forecasts of FRC during the household storage period. However, factors that drive FRC decay after water leaves the piped distribution system vary substantially, introducing significant uncertainty when modelling point-of-consumption FRC. Artificial neural network (ANN) ensemble forecasting systems (EFS) can account for this uncertainty by generating probabilistic forecasts of point-of-consumption FRC. ANNs are typically trained using symmetrical error metrics like mean squared error (MSE), but this leads to forecast underdispersion forecasts (the spread of the forecast is smaller than the spread of the observations). This study proposes to solve forecast underdispersion by training an ANN-EFS using cost functions that combine alternative metrics (Nash-Sutcliffe efficiency, Kling Gupta Efficiency, Index of Agreement) with cost-sensitive learning (inverse FRC weighting, class-based FRC weighting, inverse frequency weighting). The ANN-EFS trained with each cost function was evaluated using water quality data from refugee settlements in Bangladesh and Tanzania by comparing the percent capture, confidence interval reliability diagrams, rank histograms, and the continuous ranked probability. Training the ANN-EFS using the cost functions developed in this study produced up to a 70% improvement in forecast reliability and dispersion compared to the baseline cost function (MSE), with the best performance typically obtained by training the model using Kling-Gupta Efficiency and inverse frequency weighting. Our findings demonstrate that training the ANN-EFS using alternative metrics and cost-sensitive learning can improve the quality of forecasts of point-of-consumption FRC and better account for uncertainty in post-distribution chlorine decay. These techniques can enable humanitarian responders to ensure sufficient FRC more reliably at the point-of-consumption, thereby preventing the spread of waterborne illnesses.

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Development of gradient boosting-assisted machine learning data-driven model for free chlorine residual prediction

Helm,

Zhong,

Reid

et al. 2023

Front. Environ. Sci. Eng.

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Modelling chlorine residuals in drinking water: a review

Cited by 22 publications

References 80 publications

A Sustainable Decision Support System for Drinking Water Systems: Resiliency Improvement against Cyanide Contamination

A Sustainable Decision Support System for Drinking Water Systems: Resiliency Improvement against Cyanide Contamination

Modelling point-of-consumption residual chlorine in humanitarian response: Can cost-sensitive learning improve probabilistic forecasts?

Development of gradient boosting-assisted machine learning data-driven model for free chlorine residual prediction

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