Réduction de modèle, observation et commande prédictive d'une station d'épuration d'eaux usées

Abstract : Wastewater treatment processes are large scale, non linear systems, submitted to important disturbances of influent flow rate and load. Model predictive control (MPC) widely used industrial technique for advanced multivariable control, has been applied to the Benchmark Simulation Model 1 (BSM1) simulation benchmark of wastewater treatment process. An open loop identification method has been developed to determine a linear model for a set of input-output measurements of the process. All the step responses have been obtained in open loop from step variations of the manipulated inputs and measured disturbances around their steady state values. The non-linearities of the model are taken into account by variable parameters. The step responses coefficient obtained make it possible to determine by optimization the corresponding transfer functions. That functions are classified by five mathematical models, such as : first order, first order with integrator, inverse response, second order and second order with zero. The numerical values of coefficients of each model selected were calculated using a least squares criterion. Model predictive control (MPC) uses the resulting model as an internal model to control the process. Dynamic matrix control DMC and quadratic dynamic matrix control QDMC predictive control strategies, in the absence and presence of feedforward compensation, have been tested. Two measured disturbances have been used for feedforward control, the influent flow rate and ammonium concentration
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Ali Assaf. Réduction de modèle, observation et commande prédictive d'une station d'épuration d'eaux usées. Autre. Université de Lorraine, 2012. Français. ⟨NNT : 2012LORR0282⟩. ⟨tel-01749454⟩

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