Environmental Modelling & Software
Position Paper: A general framework for Dynamic Emulation Modelling in environmental problems
Environmental Modelling & Software
Data-driven dynamic emulation modelling for the optimal management of environmental systems
Environmental Modelling & Software
Numerical assessment of metamodelling strategies in computationally intensive optimization
Environmental Modelling & Software
Environmental Modelling & Software
Model-based iterative learning control with a quadratic criterion for time-varying linear systems
Automatica (Journal of IFAC)
A fully adaptive forecasting model for short-term drinking water demand
Environmental Modelling & Software
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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.