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Smart Salinity Management in Low-lying Deltaic Areas: A Model Predictive Control Scheme Applied to a Test Canal. / Aydin, Boran; Abraham, Edo; Rutten, Martine; Delsman, Joost ; Oude Essink, Gualbert H.P.

In: Geophysical Research Abstracts (online), Vol. 19, EGU2017-14448, 2017.

Research output: Contribution to journalMeeting AbstractScientific

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Aydin B, Abraham E, Rutten M, Delsman J, Oude Essink GHP. Smart Salinity Management in Low-lying Deltaic Areas: A Model Predictive Control Scheme Applied to a Test Canal. Geophysical Research Abstracts (online). 2017;19. EGU2017-14448.

Author

Aydin, Boran ; Abraham, Edo ; Rutten, Martine ; Delsman, Joost ; Oude Essink, Gualbert H.P. / Smart Salinity Management in Low-lying Deltaic Areas: A Model Predictive Control Scheme Applied to a Test Canal. In: Geophysical Research Abstracts (online). 2017 ; Vol. 19.

BibTeX

@article{bcef0a6fa916409b8ca68c6779a56b47,
title = "Smart Salinity Management in Low-lying Deltaic Areas: A Model Predictive Control Scheme Applied to a Test Canal",
abstract = "Saline groundwater exfiltration to surface water increases the surface water salinization and degrades the surface water quality in low-lying deltaic areas. The use of surface water will be less appropriate for agricultural, industrial and drinking water production due to salinization, and therefore, freshwater diverted from river is used for flushing canals and ditches in these areas. Current water management strategies for flushing control in low lying polders have to be revised due to expecting negative effects of climate change, sea level increase and decreasing fresh water availability. Model predictive control (MPC) is a powerful control option which is increasingly used by operational water managers for managing water systems. The explicit consideration of constraints and multi-objective management are important features of MPC. In this study, a MPC scheme is developed and tested for combined salinity and water level control of a polder ditch. Saline groundwater exfiltration flux and concentration is modelled and used as known disturbances for the MPC scheme by using Rapid Saline Groundwater Exfiltration Model (RSGEM). The developed control scheme is tested on a test case using real data from a Dutch polder affected by high saline groundwater exfiltration to observe the performance of the controller for a real scenario. Simulation results show that MPC can increase the operational efficiency of flushing operations significantly.",
author = "Boran Aydin and Edo Abraham and Martine Rutten and Joost Delsman and {Oude Essink}, {Gualbert H.P.}",
year = "2017",
language = "English",
volume = "19",
journal = "Geophysical Research Abstracts (online)",
issn = "1607-7962",
note = "EGU General Assembly 2017, EGU 2017 ; Conference date: 23-04-2017 Through 28-04-2017",
url = "http://www.egu2017.eu/",

}

RIS

TY - JOUR

T1 - Smart Salinity Management in Low-lying Deltaic Areas: A Model Predictive Control Scheme Applied to a Test Canal

AU - Aydin, Boran

AU - Abraham, Edo

AU - Rutten, Martine

AU - Delsman, Joost

AU - Oude Essink, Gualbert H.P.

PY - 2017

Y1 - 2017

N2 - Saline groundwater exfiltration to surface water increases the surface water salinization and degrades the surface water quality in low-lying deltaic areas. The use of surface water will be less appropriate for agricultural, industrial and drinking water production due to salinization, and therefore, freshwater diverted from river is used for flushing canals and ditches in these areas. Current water management strategies for flushing control in low lying polders have to be revised due to expecting negative effects of climate change, sea level increase and decreasing fresh water availability. Model predictive control (MPC) is a powerful control option which is increasingly used by operational water managers for managing water systems. The explicit consideration of constraints and multi-objective management are important features of MPC. In this study, a MPC scheme is developed and tested for combined salinity and water level control of a polder ditch. Saline groundwater exfiltration flux and concentration is modelled and used as known disturbances for the MPC scheme by using Rapid Saline Groundwater Exfiltration Model (RSGEM). The developed control scheme is tested on a test case using real data from a Dutch polder affected by high saline groundwater exfiltration to observe the performance of the controller for a real scenario. Simulation results show that MPC can increase the operational efficiency of flushing operations significantly.

AB - Saline groundwater exfiltration to surface water increases the surface water salinization and degrades the surface water quality in low-lying deltaic areas. The use of surface water will be less appropriate for agricultural, industrial and drinking water production due to salinization, and therefore, freshwater diverted from river is used for flushing canals and ditches in these areas. Current water management strategies for flushing control in low lying polders have to be revised due to expecting negative effects of climate change, sea level increase and decreasing fresh water availability. Model predictive control (MPC) is a powerful control option which is increasingly used by operational water managers for managing water systems. The explicit consideration of constraints and multi-objective management are important features of MPC. In this study, a MPC scheme is developed and tested for combined salinity and water level control of a polder ditch. Saline groundwater exfiltration flux and concentration is modelled and used as known disturbances for the MPC scheme by using Rapid Saline Groundwater Exfiltration Model (RSGEM). The developed control scheme is tested on a test case using real data from a Dutch polder affected by high saline groundwater exfiltration to observe the performance of the controller for a real scenario. Simulation results show that MPC can increase the operational efficiency of flushing operations significantly.

M3 - Meeting Abstract

VL - 19

JO - Geophysical Research Abstracts (online)

JF - Geophysical Research Abstracts (online)

SN - 1607-7962

M1 - EGU2017-14448

T2 - EGU General Assembly 2017

Y2 - 23 April 2017 through 28 April 2017

ER -

ID: 27441514