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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]

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]