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In this qualitative paper, a method for technical and operational hazard identification of a water treatment plant is described. Here, fault tree analysis is applied to a physicochemical ultrafiltration (UF) membrane train, with the objectives of developing a systematic approach for organizing and improving our understanding of the hazards at the treatment plant operational level that affect the risk of infection from the pathogen Cryptosporidium parvum. The approach was successful in identifying many technical and operational hazards. The fault tree shows that water treatment plant operation is a complex task where many factors must be taken into account. Regarding the removal of C. parvum oocysts, most initiating events relate to the filtration step in the UF system. In the future, quantification of the probability of fault events may help to prioritize interventions at the operational level. Cet article qualitatif décrit une méthode d’identification des risques techniques et opérationnels d’une usine de traitement des eaux. L’analyse par arbre des causes est utilisée pour un train de membranes d’ultrafiltration physicochimique avec, comme objectifs, le développement d’une approche systématique pour l’organisation et l’amélioration de notre compréhension des dangers au niveau opérationnel de l’usine de traitement qui affectent le risque d’infection par l’agent pathogène Cryptosporidium parvum. L’approche a réussi à identifier plusieurs dangers techniques et opérationnels. L’arbre des causes montre que le fonctionnement d’une usine de traitement des eaux est une tâche complexe dans laquelle il faut tenir compte de plusieurs facteurs. Quant à l’élimination des oocystes C. parvum, la plupart des événements déclencheurs ont trait à l’étape de filtration du système d’ultrafiltration. À l’avenir, la quantification de la probabilité des causes pourrait aider à déterminer la priorité des interventions opérationnelles. [ABSTRACT FROM AUTHOR]

This paper presented a simplified ecological risk assessment of the toxicity of alum residuals from water treatment plants to surface water that is based on the framework recommended by United States Environmental Protection Agency (USEPA). Though few studies have investigated the potential for increased aluminum toxicity with discharge of alum residual streams to the aquatic environment, none have explored the use of ecological risk assessment methodologies to gain additional insight into the potential risk. This systematic approach has been used to elucidate the process of aluminum toxicity from oven-dried alum residuals on aquatic habitats. A laboratory experiment was performed to assess the leaching of dried alum residuals to five lake water samples. The tests were also done to evaluate the effect of pH levels (4, 5.5, and 7) and drying mechanism of alum residual (oven, air or freeze-thaw). Total inorganic aluminum leaching from laboratory analysis was used along with toxicity reference values to determine a risk quotient (RQ) for assessment of risk. Results revealed that alum residuals in surface water could reduce aluminum concentration or potential risk (RQ) for fish in some lake waters at natural pH levels. Surface water pH and drying mechanism of alum residuals did not have considerable effect on leachability of aluminum. Lake waters with and without the addition of alum residual showed a potential risk for chronic sub-lethal toxicity for trout species. Both chronic and acute lethal toxicity was observed in some tests depending on the initial aluminum concentrations in the lake water. A detailed review of toxicological effects of aluminum, its exposure and bioaccumulation was studied for appropriate risk assessment. [ABSTRACT FROM AUTHOR]

Drinking water filtration systems should be as robust as possible to consistently provide finished water of high quality to protect public health. The quantification of robustness makes it possible to evaluate the status of a given system. This paper introduces a newly developed turbidity robustness index, which differentially weights average performance and deviations from average. A second new robustness evaluation method - the probability method - was also developed. This method provides the basis to implement almost real-time online monitoring of filtration robustness as the filter cycle develops. This may be particularly useful in smaller systems, which usually have less direct operator oversight. Both of the newly developed robustness approaches were applied to assess filtration turbidity performance at selected full-scale water treatment plants. Les systèmes de filtration d’eau potable devraient être aussi robustes que possible afin de fournir constamment de l’eau prête au débit de haute qualité dans le but de protéger la santé du public. La quantification de la robustesse rend possible l’évaluation de l’état d’un système donné. Le présent article introduit un indice de robustesse développé récemment qui pondère de manière différentielle le rendement moyen et les déviations de la moyenne. Une deuxième méthode d’évaluation de la robustesse - la méthode probabiliste - a également été développée. Cette dernière méthode fournit la base pour implanter un suivi en temps quasi réel en ligne de la robustesse de filtration lors du développement du cycle de filtration. Cela peut être particulièrement utile pour les systèmes plus petits que les opérateurs surveillent moins directement. Les deux nouvelles approches de la robustesse ont été appliquées à l’évaluation du rendement de la filtration de la turbidité dans des usines sélectionnées de traitement des eaux pleine échelle. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This paper examines the application of the artificial neural network (ANN) modeling technique to model a lime softening process at a full-scale drinking water treatment facility. The modeling was done for the Rossdale Water Treatment Plant (WTP) operated by EPCOR Water Services Inc. in Edmonton, Alberta. It was determined that ANN can model a lime clarifier accurately and with superior performance to other modeling methods. During the development stage, a prediction of alum clarifier pH also becomes necessary, and a very accurate inferential (virtual) sensor for pH was developed using ANN. The ANN models were also integrated with the Supervisory Control and Data Acquisition (SCADA) system of the plant so that real-time predictions of lime doses and effluent total hardness could be monitored. It was shown that the performance of the ANN models that were developed using average daily values for the parameters also work well when they are executed in real time. [ABSTRACT FROM AUTHOR]

Artificial neural network (ANN) technology has evolved from the experimental stage into actual industrial applications. To achieve this significant transition, careful planning and adjustment are required. This paper illustrates such an example in the water treatment industry. The project objective is to upgrade the ANN models from a previous research project and install the system on-line in the Rossdale Water Treatment Plant in Edmonton, Alberta, Canada, to forecast raw water colour one day ahead. The article discusses the important issues and techniques to upgrade the neural network model to the actual application. Furthermore, sufficient communication is also required between the designers and the users to address the applicability and user friendly issues in model implementation. Failure in communication can render the whole process ineffective. Possible improvements are also recommended for the future on-line applications. [ABSTRACT FROM AUTHOR]

This paper reports on the application of artificial neural network (ANN) techniques for predicting the concentration of trihalomethanes (THMs) in finished water at the E.L. Smith Water Treatment Plant (WTP) in Edmonton, Alberta, Canada. The formation of THMs in finished water involves many complex chemical reactions and interactions that are difficult to model using conventional methods. The formation of THMs has been found to be correlated to raw and treated water quality characteristics such as colour, pH, and temperature and chemical addition such as chlorine, alum, and powder activated carbon (PAC). Three models were derived using raw water, post clarification water, and a combination of raw and post clarification water parameter inputs. The model that most successfully predicted the concentration of THMs in finished water is the model that uses clarifier effluent parameter inputs. This model can be used at the E.L. Smith WTP for early detection of potentially high THM concentrations in finished water and gives plant operators enough advanced warning to reduce THM precursors. With an adequate understanding of water treatment plant processes and THM formation potential it will be fairly easy for any water treatment facility, which has a few years of historical plant data, to develop its own ANN model for predicting the formation of THM in finished water. [ABSTRACT FROM AUTHOR]

Virtually all water utilities are looking at improving the operation of their plants to keep control of costs and to meet stringent water quality regulations. Better process control and automation of the plants can help achieve these goals. However, traditional control techniques such as proportional–integral–derivative (PID) can be inadequate when automating certain water treatment processes such as turbidity, organics, or hardness removal in a clarification process. Advanced process control techniques are alternatives to mitigate this impediment. At the cornerstone of many advanced process control techniques is a model of the process being controlled, which can be developed using artificial neural networks (ANNs). This paper describes various advanced process control techniques, the potentially large role of ANN models in implementing these techniques, and issues and solutions when using ANN in a real-time control system. [ABSTRACT FROM AUTHOR]