PREDICTION OF HYDRAULIC LOAD FOR URBAN STORM CONTROL OF A MUNICIPAL WWT PLANT

Three different methodologies are assessed which provide predictions o f the hydraulic load to the treatment plant one hour ahead. The three models represent three different levels of complexity ranging from a s imple regression model over an adaptive grey-box model to a complex hy drological and full dynamical wave model (Chow et al., 1988). The simp le regression model is estimated as a transfer function model of rainf all intensity to influent flow. It also provides a model for the base flow. The grey-box model is a state space model which incorporates ada ptation to the dry weather flow as well as the rainfall runoff. The fu ll dynamical flow model is a distributed deterministic model with many parameters, which has been calibrated based on extensive measurement campaigns in the sewer system. The three models are compared by the ab ility to predict the hydraulic load one hour ahead. Five rain events i n a test period are used for evaluating the three different methods. T he predictions are compared to the actual measured flow at the plant o ne hour later. The results show that the simple regression model and t he adaptive grey-box model which are identified and estimated on measu red data perform significantly better than the hydrological and full d ynamical flow model which is not identifiable and needs calibration by hand. Far frontal rains no significant difference in the prediction p erformance between the simple regression model and the adaptive grey-b ox model is observed. This is due to a rather uniform distribution of frontal rains. A single convective lain justifies the adaptivity of th e grey-box model for non-uniformly distributed rain, i.e. the predicti ons of the grey-box model were significantly better than the predictio ns of the simple regression model for this rain event. In general, mod els for model-based predictive control should be kept simple and ident ifiable from measured data. (C) 1998 Published by Elsevier Science Ltd . All rights reserved.