CLOCWiSe: Constraint Logic for Operational Control of Water Systems

Brdyś, Mietek A.; Creemers, Tom; Goossens, H.H.L.M.; Riera, Jordi Ferrer; Heinsbroek, Anton; Lisiak, Zbigniew

doi:10.1061/40430(1999)63

Cited by 3 publications

(1 citation statement)

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“…However, it is possible to obtain good suboptimal recursive algorithms. We shall apply the recursive algorithm developed in [Brdys 1999)] that introduces a concept of moving information window in order to stabilise the bounds. It should be pointed out that the moving window idea has nothing to do with the one used in signal processing literature.…”

Section: Recursive Algorithmmentioning

confidence: 99%

Robust On-Line Monitoring of Mixing Quality in Water Supply Networks

Brdyś

Lisiak

1999

Wrpmd'99

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The paper applies an algorithm for recursive estimation of variables and parameters in general dynamic networks that has been developed by [Brdys 99] to robust on-line monitoring of the mixing water quality in dynamic water networks. A complete hydraulic information is assumed to be available on-line. First a parsimonious parametrisation of a mathematical model of mixing quality is derived. Next a recursive estimation algorithm is designed that uses the model and available measurements to generate on-line robust estimates of unknown quantities. The parameters and variables are estimated simultaneously. Robustness of the estimates is achieved through non-probabilistic set-bounded modelling of uncertainty in the measurement and modelling errors. The stable and tight bounds on the estimated quantities are obtained by employing a concept of moving information window. The estimation scheme is very flexible in integrating the information available. In particular, if only concentrations of the quality parameters in a network inputs are measured, the estimator operates as the quality simulator under uncertain models and not accurately known inputs. This can be viewed as a sort of generic soft sensor. Performance of the monitoring scheme is illustrated by applications to two case-study networks.

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Section: Recursive Algorithmmentioning

confidence: 99%