Polder Flushing: Model Predictive Control of Flushing Operations to Effective and Real Time Control of Salinity in Polders

We have developed a versatile Model Predictive Control (MPC) framework, which can handle real-time control of a large variety of water systems. The framework combines a fast-solvable optimisation model (a quadratic program) with evaluation and realignment by a detailed hydrological-hydrodynamic model. The flexibility of the MPC framework is highlighted by two case studies: (1) a large-scale river system with several weeks of travel time, and (2) an urban storm and wastewater system with a concentration time of about half an hour to one hour. Both case studies demonstrate a large potential for improving operations by system-wide real-time optimisation.

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Combined Sewer Overflow and flooding Reduction through a Safe Real-Time Control based on Multi-Reinforcement Learning, Model Predictive Control, and q value improvement

10.1002/essoar.10507735.1 ◽

2021 ◽

Author(s):

WenChong Tian ◽

Zhenliang Liao ◽

Guozheng Zhi ◽

Zhiyu Zhang ◽

XUAN WANG

Keyword(s):

Reinforcement Learning ◽

Model Predictive Control ◽

Real Time ◽

Predictive Control ◽

Combined Sewer Overflow ◽

Q Value ◽

Real Time Control ◽

Time Control ◽

Combined Sewer ◽

Reinforcement Learning Model

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Survey of Optimal Control and Model Predictive Control with State Estimation and a Real Time Application

Műszaki Tudományos Közlemények ◽

10.33894/mtk-2020.13.01 ◽

2020 ◽

Vol 13 (1) ◽

pp. 19-30

Author(s):

László Dávid ◽

Katalin György ◽

László-Alpár Galaczi

Keyword(s):

Optimal Control ◽

Model Predictive Control ◽

Real Time ◽

Predictive Control ◽

Industrial Applications ◽

Electron Gun ◽

Real Time Control ◽

Practical Applications ◽

Time Control ◽

Self Tuning

AbstractThe optimal control and its limited version namely the model predictive control represent one of the most important nonlinear control alternatives nowadays. The success of them are also proven in many practical applications. These can provide for several industrial applications the optimal trajectory calculation as well as calculation of the real-time control signal. One successful version of this is Generalized Predictive Control (GPC). A big advantage of these control algorithms is that they solutions are able to take into account the limitations of the inputs, and the states. In some cases, it is important to know the mathematical model chosen and the complete state information. Otherwise, the model can be estimated during the operation. Our study shows through the control of the cathode heating of a high-power electron beam device the self-tuning adaptive control thus constructed. Using a suitable dynamic model and an extended Kalman estimator, we determine the estimated temperature of the two cathodes during operation and the saturation electron current, which ensures the maximum cathode life. The practical application was tested on a CTW 5/60 type electron gun.

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