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Model reduction in model predictive control of combined water quantity and quality in open channels

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Title: Model reduction in model predictive control of combined water quantity and quality in open channels
Authors: Xu, M. min.xu@tudelft.nl, van Overloop, P.J.1, van de Giesen, N.C.1
Source: Environmental Modelling & Software. Apr2013, Vol. 42, p72-87. 16p.
Subjects: Predictive control systems, Mathematical models, Water quality, Real-time control, Aquaporins, Water quality management, Proper orthogonal decomposition, Water supply
Abstract: Abstract: Model predictive control (MPC) is an advanced real-time control technique that uses an internal model to predict the future system behavior and generates optimal control actions by solving an optimization problem. MPC has been more and more applied for controlling open water systems, especially open water channels. Most of the research however focuses on water quantity (water level) control. Since water quality management is recently attracting more attention, extending MPC on combined water quantity and quality management is a logical next step. In this paper, we study the application of complex models in MPC to control both water quantity and quality. However, because of the online optimization of MPC, the computational time becomes an issue. In order to reduce the computational time, a model reduction technique, Proper Orthogonal Decomposition (POD), is applied to reduce the model order. The method is tested on a Polder flushing case. The results show that POD can significantly reduce the model order for both water quantity and quality with high accuracy. The MPC using the reduced model performs well in controlling combined water quantity and quality in open water channels. [Copyright &y& Elsevier]
Copyright of Environmental Modelling & Software is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
An: 85854945
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PubType: Academic Journal
PubTypeId: academicJournal
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  Data: <searchLink fieldCode="JN" term="%22Environmental+Modelling+%26+Software%22">Environmental Modelling & Software</searchLink>. Apr2013, Vol. 42, p72-87. 16p.
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  Data: <searchLink fieldCode="DE" term="%22Predictive+control+systems%22">Predictive control systems</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+models%22">Mathematical models</searchLink><br /><searchLink fieldCode="DE" term="%22Water+quality%22">Water quality</searchLink><br /><searchLink fieldCode="DE" term="%22Real-time+control%22">Real-time control</searchLink><br /><searchLink fieldCode="DE" term="%22Aquaporins%22">Aquaporins</searchLink><br /><searchLink fieldCode="DE" term="%22Water+quality+management%22">Water quality management</searchLink><br /><searchLink fieldCode="DE" term="%22Proper+orthogonal+decomposition%22">Proper orthogonal decomposition</searchLink><br /><searchLink fieldCode="DE" term="%22Water+supply%22">Water supply</searchLink>
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  Data: Abstract: Model predictive control (MPC) is an advanced real-time control technique that uses an internal model to predict the future system behavior and generates optimal control actions by solving an optimization problem. MPC has been more and more applied for controlling open water systems, especially open water channels. Most of the research however focuses on water quantity (water level) control. Since water quality management is recently attracting more attention, extending MPC on combined water quantity and quality management is a logical next step. In this paper, we study the application of complex models in MPC to control both water quantity and quality. However, because of the online optimization of MPC, the computational time becomes an issue. In order to reduce the computational time, a model reduction technique, Proper Orthogonal Decomposition (POD), is applied to reduce the model order. The method is tested on a Polder flushing case. The results show that POD can significantly reduce the model order for both water quantity and quality with high accuracy. The MPC using the reduced model performs well in controlling combined water quantity and quality in open water channels. [Copyright &y& Elsevier]
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  Data: <i>Copyright of Environmental Modelling & Software is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1016/j.envsoft.2012.12.008
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        Text: English
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      – SubjectFull: Mathematical models
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      – SubjectFull: Water quality
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      – SubjectFull: Aquaporins
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      – SubjectFull: Proper orthogonal decomposition
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      – SubjectFull: Water supply
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              Text: Apr2013
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