The purpose of this paper is to present an observer-based control design with application to continuous bioreactors. For this purpose, phenomenological bioreactor models were represented by identified linear models plus unknown modelling error terms. Therefore, an uncertainty-based observer with a polynomial structure capable of estimating the unknown modelling error of the reactor representation is coupled to a linear input-output controller. The proposed methodology was evaluated in a sulphate reduction bioprocess and an acetone-butanol-ethanol (ABE) fermentation process for butanol (biofuel) production, under continuous regimes. Experimentally validated mathematical models were considered for this purpose. A theoretical framework is presented to demonstrate the corresponding closed-loop stability of the systems, and numerical simulations were carried out to corroborate the satisfactory performance of the proposed methodology.
Bioscorodite crystallization is a promising process for the proper immobilization of arsenic from acidic metallurgical wastewater, and Acidianus brierleyi is an effective archaeon to oxidize Fe(II) and As(III) simultaneously. This paper deals with the development of an experimentally validated mathematical model to gain insight into the simultaneous processes of Fe(II) and As(III) oxidation via microbial cells and the exopolysaccharide (EPS) matrix, As(V) precipitation, and bioscorodite crystallization, which are affected by several factors. After the mathematical structure was proposed, a model fitting was performed, finding global determination coefficients between 0.96 and 0.99 (with p-values < 0.001) for all the variables. The global sensitivity analysis via Monte Carlo simulations allowed us to identify the critical parameters whose sensitivity depends on culture conditions. The model was then implemented to evaluate the effect of cell concentration, Fe(II) and As(III) concentrations (at Fe/As = 1.4), and oxidation rate constants for A. brierleyi and the EPS region, noting that these factors play an important role in the process. Our results showed that the proposed model can be used as a robust simulation platform for the further analysis of the bioscorodite crystallization process under extremophilic conditions.
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