Abstract
The main objective of this work was to design a software sensor device based on state observer for a class of continuous bioreactor with application to heavy metal removal and locally analyze the observability properties of the considered system, considering parametric uncertainties. First, an alternative phenomenological model of the main state variables of the process was formulated, considering an unstructured kinetic approach based on Levenspiel product inhibition model; this kinetic model was experimentally validated. The kinetic model was used as a benchmark plant and extended for continuous operation in order to analyze the local observability properties, considering several sets of measured outputs that produce observable subspaces of different dimensions. In addition, we present a nonlinear observer, which is robust against parametric uncertainties, to estimate the observable states of the bioreactor. The convergence of the proposed methodology was analyzed using Lyapunov stability theory. Numerical experiments were done in order to show the performance of the proposed observer and the observability properties of the system.
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