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Prediction of hydraulic load for urban storm control of a municipal WWT plant

Authors :
Carstensen, J.
Strandbaeek, H.
Nielsen, M. K.
Source :
Water Science & Technology; 1998, Vol. 37 Issue 12, p363, 0p
Publication Year :
1998

Abstract

Three different methodologies are assessed which provide predictionsof the hydraulic load to the treatment plant one hour ahead. The three models represent three different levels of complexity ranging froma simple regression model over an adaptive grey-box model to a complex hydrological and full dynamical wave model (Chow et al., 1988). The simple regression model is estimated as a transfer function model of rainfall intensity to influent flow. It also provides a model for the base flow. The grey-box model is a state space model which incorporates adaptation to the dry weather flow as well as the rainfall runoff. The full 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 ability to predict the hydraulic load one hour ahead. Fiverain events in a test period are used for evaluating the three different methods. The predictions are compared to the actual measured flow at the plant one hour later. The results show that the simple regression model and the adaptive grey-box model which are identified and estimated on measured data perform significantly better than the hydrological and full dynamical flow model which is not identifiable and needs calibration by hand. For frontal rains no significant difference in the prediction performance between the simple regression model and the adaptive grey-box model is observed. This is due to a rather uniform distribution of frontal rains. A single convective rain justifies the adaptivity of the grey-box model for non-uniformly distributed rain, i.e. the predictions of the grey-box model were significantlybetter than the predictions of the simple regression model for this rain event. In general, models for model-based predictive control should be kept simple and identifiable from measured data. (c) 1998 Published by Elsevier Science Ltd. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
02731223
Volume :
37
Issue :
12
Database :
Complementary Index
Journal :
Water Science & Technology
Publication Type :
Academic Journal
Accession number :
8405644
Full Text :
https://doi.org/10.2166/wst.1998.0562