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Prediction of the influent wastewater variables using neural network theory
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In order to develop an effective control strategy for the activated sludge process of a wastewater treatment plant, an understanding of the nature of the influent load disturbances to the wastewater treatment plant is necessary. Biological systems are among the most difficult to control and predict. Due to the complex biological reaction mechanisms, the highly time-varying, and multivariable aspects of the wastewater treatment plant ('MNTP), the diagnosis of the 'MNTP are still difficult in practice. The application of intelligent techniques, which can analyse the multi-dimensional nonlinear process data using a visualisation technique, can be useful for analysing and diagnosing the activated-sludge process in the VVVVTP. This complex capability for nonlinearity representation combined with the fact that no model exists for the WVVTP influent dynamics to a WVVTP, makes neural networks an ideal choice for a solution. Forecasting the behaviour of complex systems has been a broad application area for neural networks. Applications such as economic forecasting, electricity load I demand forecasting, and forecasting natural and physical phenomena have been extensively studied, hence the numerous papers presented at annual conferences in this focus area. The cognitive ability of artificial neural networks to map' nonlinear complex input-output relationships, which would allow for better prediction and corrective control of processes, make them particularly attractive. The values of the influent disturbances are usually measured off-line in ~ laboratory, as there are still no reliable on-line sensors available. This work presents the development of a neural network model for prediction of the values of the influent disturbances based on historical plant and weather data, which ultimately affect the activated sludge process. Three different neural networks including the multilayer perceptron, recurrent and radial basis functions neural network are developed for the prediction of the influent disturbances of Chemical Oxygen Demand (COD), Total Kjeldahl Nitrogen (TKN) and influent flow rate respectively. The application area is the prediction of the influent variables at a local municipal wastewater treatment plant. The forecast result is used for the determination of the setpoint to a controller, in order to• optimize plant performance. The results are first applied to a pilot wastewater treatment plant. Much hype exists surrounding the subject of neural networks, and they are sometimes described as 'computers that think'. This sort of definition creates unrealistic expectations and is one of the reasons why it is discredited. The results obtained will hopefully present helpful insights as to the scope and possibilities as to the application for neural networks, but also present the practical challenges which neural network practitioners and designers of intelligent systems face. The solution of the problem for development of the mathematical model for dynamic behaviour of the influent disturbances according to the influence of the weather conditions and the season of the year is the first attempt in the scientific and research literature so far.