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

Abstract

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.

Application of Selected Methods of Black Box for Modelling the Settleability Process in Wastewater Treatment Plant

“…Therefore, the forecasts of the STP used models of black box, which at the stage of learning model is generated structure model without the need to know the physics of the analyzed phenomenon. One of the most commonly used for this purpose methods are artificial neural networks [2][3][4][5], but there are also used other methods such as vectors carrying, tree reinforced models, autoregressive MARS (multivariate adaptive regression splines) and the like. The former method is a modification of the classical model MLP (multi-layer perceptron) where the hidden layer represents a non-linear projection of a vector set of input data characteristics of the space in which they are linearly separable.…”