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Nonlinear Modeling, Identification and Control of Membrane Bioreactors

Guilherme Araujo Pimentel 1, 2, 3 
1 MODEMIC - Modelling and Optimisation of the Dynamics of Ecosystems with MICro-organisme
CRISAM - Inria Sophia Antipolis - Méditerranée , MISTEA - Mathématiques, Informatique et STatistique pour l'Environnement et l'Agronomie
Abstract : This thesis proposes a simple submerged membrane bioreactor (sMBR) dynamic model that comprises physical and biological process behaviors. Filtration (physical aspect) is represented by a resistance-in-series model composed of a reversible resistance, linked to the sludge cake formation (that can be detached by air scouring) and an irreversible fouling resistance. The biological process is described by a simple chemostat model. The model asymptotic behavior, observability, controllability and fast and slow dynamics are analyzed. The latter analysis, based on Tikhonov's theorem, reveals the possibility decouple the dynamics in three time-scales, i.e. long-term fouling evolution (slow dynamic), biological degradation (fast dynamic) and fouling cake formation (ultrafast dynamic).Therefore, a parameter identification is organized in three steps corresponding to the three time-scales obtained from the analytical analysis. The parameter identification is implemented using a weighted least-squares cost function and a lower bound on the covariance matrix of the parameter estimates, which is used to obtain the parameters confidence intervals, is computed by the inverse of the Fisher Information Matrix (FIM). The model capacity to predict trans-membrane pressure and biological degradation is proved by model identification and cross-validation results. As sMBR processes are relativity new, experimental process data are scarce. Thus, a lab-scale recirculating aquaculture system with an sMBR is designed, built and automated. Process online measurements, such as temperature, total suspended solids (TSS), ammonia and nitrate effluent concentrations, air cross- and effluent flow rates and trans-membrane pressure, are gathered in order to validate the proposed model. In addition, experimental data from a pilot plant located in Spain are also used to further analyze and validate the model. Concerning the process control theoretical study of two different approaches are presented: a nonlinear model predictive control (NMPC) is implemented in order to optimize the effluent production rate and maximize the period between two chemical cleaning procedures and a partial-linearizing feedback Lyapunov controller is designed in order to stabilize the fouling by actuating in the air cross- and effluent flows. The results included in this thesis show the importance of analytical model studies in order to gain process insight and deduce model simplification. Another important point is the simple dynamic model structure with a small number of parameters, which is adequate to implement advanced control strategies on sMBR processes and, similarly, to predict biological degradation and fouling build-up dynamics.
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Guilherme Araujo Pimentel. Nonlinear Modeling, Identification and Control of Membrane Bioreactors. Environmental Sciences. Université Montpellier 2, 2015. English. ⟨tel-01130312v1⟩

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