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Potable water treatment is one of the most challenging and complex systems that municipalities need to deal with considering limited resources. A study from mid-90s showed that the continuously deteriorating Canadian water supply system would require $3.1 billion to bring the system at satisfactory level. Drinking water treatment plants (WTP) include several components, such as tanks, basin, and pumps. Operators are able to spend a small portion of the available resources or their plant’s infrastructure and equipment compared to water quality and day-to-day operational activities. The research presented in this technical paper aims at developing condition assessment model(s) for the WTP components. Essential condition parameters of WTP include technical, physical, environmental, and operational aspects. To determine the condition index of a WTP component, value additive multi-attribute theory has been used where average weights and scores are considered for the model parameters. Data on WTP conditions are collected from experts and consultants across Canada and the United States. It is concluded from the model results that the average condition index for settling basins, ranges from 9.6 (best scenarios) to 1.9 (worst scenarios) and from 9.6 to 3.4 for pumps. Analysis reveals that, for tank and basins, design and construction parameter is the most important parameter for WTP condition, while the operational parameter is the most important one for pumps. The developed models are expected to benefit academics and practitioners (municipal engineers, consultants, and contractors) to prioritize inspection and rehabilitation planning for existing water treatment plants. [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]