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Real-time control of combined surface water quantity and quality: polder flushing.

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Title: Real-time control of combined surface water quantity and quality: polder flushing.
Authors: Xu, M.1 min.xu@tudelft.nl;, van Overloop, P. J.1 p.j.a.t.m.vanoverloop@tudelft.nl;, van de Giesen, N. C.1 n.c.vandegiesen@tudelft.nl, Stelling, G. S.2 g.s.stelling@tudelft.nl
Source: Water Science & Technology. 2010, Vol. 61 Issue 4, p869-878. 10p. 4 Diagrams, 4 Charts, 8 Graphs.
Subjects: Real-time control, Water quality monitoring, Water supply, Water depth, Modeling (Sculpture), Automatic control systems, Algorithms, Industrial efficiency, Water pollution
Abstract: In open water systems, keeping both water depths and water quality at specified values is critical for maintaining a 'healthy' water system. Many systems still require manual operation, at least for water quality management. When applying real-time control, both quantity and quality standards need to be met. In this paper, an artificial polder flushing case is studied. Model Predictive Control (MPC) is developed to control the system. In addition to MPC, a 'forward estimation' procedure is used to acquire water quality predictions for the simplified model used in MPC optimization. In order to illustrate the advantages of MPC, classical control [Proportional-Integral control (PI)] has been developed for comparison in the test case. The results show that both algorithms are able to control the polder flushing process, but MPC is more efficient in functionality and control flexibility. [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
Description
Abstract:In open water systems, keeping both water depths and water quality at specified values is critical for maintaining a 'healthy' water system. Many systems still require manual operation, at least for water quality management. When applying real-time control, both quantity and quality standards need to be met. In this paper, an artificial polder flushing case is studied. Model Predictive Control (MPC) is developed to control the system. In addition to MPC, a 'forward estimation' procedure is used to acquire water quality predictions for the simplified model used in MPC optimization. In order to illustrate the advantages of MPC, classical control [Proportional-Integral control (PI)] has been developed for comparison in the test case. The results show that both algorithms are able to control the polder flushing process, but MPC is more efficient in functionality and control flexibility. [ABSTRACT FROM AUTHOR]
ISSN:02731223
DOI:10.2166/wst.2010.847