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Genetic Hybrid Predictive Controller for Optimized Dissolved-Oxygen Tracking at Lower Control Level

Identifieur interne : 001762 ( Main/Exploration ); précédent : 001761; suivant : 001763

Genetic Hybrid Predictive Controller for Optimized Dissolved-Oxygen Tracking at Lower Control Level

Auteurs : Kazimierz Duzinkiewicz [Pologne] ; Mietek A. Brdys [Pologne, Royaume-Uni] ; Wojciech Kurek [Pologne] ; Robert Piotrowski [Pologne]

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Mots-clés :

Abstract

A hierarchical two-level controller for dissolved-oxygen reference trajectory tracking in activated sludge processes has been recently developed and successfully validated on a real wastewater treatment plant. The upper level control unit generates trajectories of the desired airflows to be delivered by the aeration system to the aerobic zones of the biological reactor. A nonlinear model predictive control algorithm is applied to design this controller. The aeration system itself is a complicated hybrid nonlinear dynamical system. The lower level controller (LLC) forces the aeration system to follow these set-point trajectories, minimizing a cost of energy due to pumping of the air and accounting for system operational limitations such as the limits on the allowed frequency of switching of the blowers and on their capacity. The predictive control is also applied to design the LLC based on a piecewise-linearized hybrid dynamics of the aeration system. Casting the mixed-integer nonlinear optimization problem under heterogeneous constraints due to the limits on the blower switching frequency into the approximated mixed-integer form is done at a cost of introducing large number of auxiliary variables into the lower level predictive controller optimization task. This paper derives another nonlinear hybrid predictive control algorithm for the LLC. It is directly based on the nonlinear hybrid dynamics and logical formulation of the switching constraint. A genetic algorithm is derived with dedicated operators allowing for efficient handling of the switching constraint and nonlinear hybrid system dynamics. The efficiency of the control algorithm is validated by simulation based on real data records.


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<term>Activated sludge</term>
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<term>Blower</term>
<term>Casting</term>
<term>Control synthesis</term>
<term>Control unit</term>
<term>Database</term>
<term>Dynamical system</term>
<term>Energy cost</term>
<term>Force control</term>
<term>Genetic algorithm</term>
<term>Hybrid control</term>
<term>Hybrid system</term>
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<term>Mixed integer programming</term>
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<term>Motion control</term>
<term>Non linear control</term>
<term>Non linear model</term>
<term>Non linear programming</term>
<term>Non linear system</term>
<term>Optimal trajectory</term>
<term>Optimization</term>
<term>Pneumatic drive</term>
<term>Position control</term>
<term>Predictive control</term>
<term>Pumping</term>
<term>Set point</term>
<term>Tracking task</term>
<term>Waste water purification</term>
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<term>Commande hybride</term>
<term>Commande prédictive</term>
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<term>Commande mouvement</term>
<term>Commande position</term>
<term>Unité contrôle</term>
<term>Trajectoire optimale</term>
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<term>Bioréacteur</term>
<term>Coût énergie</term>
<term>Pompage</term>
<term>Transmission pneumatique</term>
<term>Soufflante</term>
<term>Coulée en moule</term>
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<term>Système non linéaire</term>
<term>Valeur consigne</term>
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<term>Programmation non linéaire</term>
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<term>Commande force</term>
<term>Commande de niveau</term>
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<div type="abstract" xml:lang="en">A hierarchical two-level controller for dissolved-oxygen reference trajectory tracking in activated sludge processes has been recently developed and successfully validated on a real wastewater treatment plant. The upper level control unit generates trajectories of the desired airflows to be delivered by the aeration system to the aerobic zones of the biological reactor. A nonlinear model predictive control algorithm is applied to design this controller. The aeration system itself is a complicated hybrid nonlinear dynamical system. The lower level controller (LLC) forces the aeration system to follow these set-point trajectories, minimizing a cost of energy due to pumping of the air and accounting for system operational limitations such as the limits on the allowed frequency of switching of the blowers and on their capacity. The predictive control is also applied to design the LLC based on a piecewise-linearized hybrid dynamics of the aeration system. Casting the mixed-integer nonlinear optimization problem under heterogeneous constraints due to the limits on the blower switching frequency into the approximated mixed-integer form is done at a cost of introducing large number of auxiliary variables into the lower level predictive controller optimization task. This paper derives another nonlinear hybrid predictive control algorithm for the LLC. It is directly based on the nonlinear hybrid dynamics and logical formulation of the switching constraint. A genetic algorithm is derived with dedicated operators allowing for efficient handling of the switching constraint and nonlinear hybrid system dynamics. The efficiency of the control algorithm is validated by simulation based on real data records.</div>
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