Año: 2014
Autores: Amicarelli, A.; Quintero Montoya, O.; di Sciascio, F.;
Resumen: On-line estimation of biomass concentration in batch biotechnological processes is an active area of research
because normally, the biomass is the desired process product output, and also because it is necessary for control purposes to
replace the unavailable biomass concentration measurements with reliable and robust on-line estimations. This work presents
five different alternatives to face the problem of biomass estimation in a particular batch bioprocess (d-endotoxins production
of Bacillus thuringiensis), namely: a phenomenological estimator based on dissolved oxygen balance, an extended Kalman
filter estimator, a Gaussian process regression-based estimator, an artificial neural networks-based estimator, and finally, an
estimator based on information fusion by a decentralized Kalman filter. Each proposed biomass estimation method has its
own advantages and drawbacks according to their ability to take into account the model uncertainties and the measurement
errors. First, the design techniques of these five biomass estimators are exposed, and finally, the behavior of each estimation
method is compared. The availability of efficient biomass estimators is of great importance for engineers because, on the one
hand, it allows developing new control strategies for other bioprocess variables such as for instance: the growth rate of the
microorganism, the dissolved oxygen concentration, and so on. On the other hand, it is also important to improve the
performance of the bioprocess optimization procedure. This work also aims to show the evolution on biomass estimation
techniques from classical to more contemporary approaches, such as the design based on neural networks and Gaussian
processes regression. © 2013 Curtin University of Technology and John Wiley & Sons, Ltd.
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