Use of sewer on-line total solids data in wastewater treatment plant modelling

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We describe a neural network model of a municipal wastewater treatment plant (WWTP) in which on-line total solids (TS) sewer data generated by a novel microwave sensor is used as a model input variable. The predictive performance of the model is compared with and without sewer data and with modelling with a traditional linear multiple linear regression (MLR) model. In addition, the benefits of using neural networks are discussed. According to our results, the neural network based MLP (multilayer perceptron) model provides a better estimate than the corresponding MLR model of WWTP effluent TS load. The inclusion of sewer TS data as an input variable improved the performance of the models. The results suggest that increased on-line sensing of WWTPs should be stressed and that neural networks are useful as a modelling tool due to their capability of handling the nonlinear and dynamic data of sewer and WWTP systems.

Keywords: modelling, neural networks, on-line sensing, optimization, total solids, wastewater treatment

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