Publicaciones

Autores
Roberti, F. ;Soria, C. ;Slawiñski, E. ;Mut, V. ;Carelli, R.

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Autores
Toibero, M. ;Roberti, F. ;Auat Cheeín, F. ;Soria, C. ;Carelli, R.

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Autores
Traslosheros, A. ;Sebastián, J. ;Roberti, F. ;Carelli, R. ;Vaca, R.

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Autores
Amicarelli, A. ;di Sciascio, F. ;Quintero Montoya, O. ; Ortiz, O.;

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Biomass concentration in a biotechnological process is one of the states that characterizes a bioprocess. Moreover, it is generally the main direct or indirectly desired product. It is well known that the biomass concentration is not normally measured because this measurement is not possible or this is economically non viable. Therefore, for control purposes it is necessary to replace the unavailable biomass concentration measurements with reliable and robust online estimations. To this aim, several states observers can be found in the literature. Observers can be coarsely divided into two broad classes: first principles or phenomenological estimators and empirical estimators. The phenomenological estimators can be also subdivided into classical observers and asymptotic observers. Classical observers include extended Kalman filter (EKF), extended Luenberger observer, the high gain observer, nonlinear observers, and the full horizon observer. In this class of estimators, a detailed knowledge of the reaction kinetics and associated phenomena are required to represent the balance equations. Modeling the biological kinetics reactions is a difficult and time-consuming task, and therefore the model used by the estimators could differ significantly from reality. This is the main drawback of these phenomenological estimators, i.e., their efficiency strongly relies on the model quality. Asymptotic observers are based on the idea that uncertainty in bioprocess models lies in the process kinetics models. The design of these observers is based on a state transformation performed to provide a model which is independent of the kinetics. A potential drawback of the asymptotic observers is that the rate of convergence is completely determined by the operating conditions, i.e., the rate of convergence can be very slow or even cannot converge at all. Empirical estimators are based on constructing appropriate nonlinear models of biotechnological processes exclusively from the process input-output data without considering the functional or phenomenological relations between the bioprocess variables. However, the conventional empirical modeling approach must know the structure (functional form) of the data-fitting model in advance. This Chapter addresses the problem of the biomass estimation of a batch biotechnological process: the Bacillus thuringiensis (Bt) δ-endotoxins production process, and presents different alternatives that can be successfully used in this sense. The development of the Chapter includes the design of various biomass estimators, namely: a phenomenological biomass estimator, a standard EKF biomass estimator, a biomass estimator based on RNA, a decentralized Kalman Filter, a biomass concentration estimator based on Bayesian regression with Gaussian Process and a biomass estimator based on Recursive Bayesian Filtering. The phenomenological biomass estimator is based on information of the substrate concentration balance and dissolved oxygen balance for the process. The biomass concentration does not depend only on the mentioned variables; therefore, the proposed observer does not take into account biomass variations produced due to variations of temperature, pH, antifoam aggregation and inherent conditions of inocula. For comparison aims, it is included the biomass estimate of a standard extended Kalman filter which observation vector is built with experimental dissolved oxygen and substrate data. For the second observer it is selected a Recurrent Multilayer Perceptron as biomass observer. For the training phase it is considered a Back-Propagation algorithm and experimental data (substrate concentration and dissolved oxygen concentration) from a set of available fermentations. For the network validation it is considered another experimental dataset, achieving adequate biomass estimation. The proposed virtual sensor provides satisfactory results for the biomass estimation showing acceptable performance. It should be noted the importance of the choice of the input variables, since it is a batch process which have "infinite memory". Next, the results provided by these observers (phenomenological and the RNA) are compared with a new estimation given by its fusion through a Decentralized Kalman Filter. In designing a biomass concentration estimator based on Bayesian regression with Gaussian Process; the time evolution of biomass is conceived as a dynamic system perturbed by a certain process noise. Although the bioprocess is not truly stochastic, this noise is used for modeling the uncertainties in the system dynamics, i.e., the stochasticity is only used for representing the model uncertainties. Biomass concentration estimation is obtained indirectly through observed noisy measurements. Noise in the measurements refers to a disturbance in the sense that the measurements are uncertain, i.e., even in the hypothetical case that the true biomass concentration is known, the measurements would not be deterministic functions of this true biomass, but would have a certain distribution of possible values. The major difficulty when the biomass estimation is implemented is related to the uncertainty of the models used to describe their dynamics. Each estimation method has its own advantages and drawbacks according to their ability to take into account the model uncertainties and the measurement errors. Finally, it is proposed a state estimation formulation like a filtering problem, specifically, a non linear Non Gaussian Filtering is used, through the called Particle Filter. In such approach, the stochastic terms are introduced into the dynamics, measurements and initial conditions. The object of interest is the full state conditional probability law to some noise measurements of some components. An analysis of results and discussion about the estimators is presented and results for the Bacillus thuringiensis δ-endotoxins production process on the basis of experimental data from a set of various fermentations are presented.

Autores
Salinas, L. ;Slawiñski, E. ;Mut, V. ;Sebastián, J.

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Autores
Capraro, F. ;Tosetti, S. ;Vita Serman, F.

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Autores
Auat Cheeín, F. ;Lopez, N. ;Soria, C. ;di Sciascio, F. ;Lobo Pereyra, F. ;Carelli, R.

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Autores
Scaglia, G. ;Rosales, A. ;Quintero Montoya, O. ;Mut, V.

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Autores
Amicarelli, A. ;di Sciascio. ;Toibero, M F. ;Álvarez Zapata, H.;

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This paper proposes a model to characterize the Dissolved Oxygen Dynamics (DO) for the Bacillus thuringiensis (Bt) δ-endotoxins production process. The objective of this work is to include this dynamics into a phenomenological model of the process in order to facilitate the biomass estimation from the knowledge of oxygen consumption; and for control purposes, by allowing the addition of a new control variable in order to favorably influence the bioprocess evolution. The mentioned DO model is based on first principles and parameter estimation and model verification are supported by real experimental data. Finally, a control strategy is designed based on this model with its corresponding asymptotic stability and robustness analysis.

Autores
Auat Cheeín, F. ;Carelli, R.

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Autores
Auat Cheeín, F. ;Scaglia, G. ;di Sciascio, F. ;Carelli, R.

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Autores
Rossomando, F. ;Soria, C. ;Carelli, R.

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Autores
Slawiñski, E. ;Mut, V.

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Autores
Orosco, E. ;Soria, C. ;Lopez, N. ;di Sciascio, F.

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Autores
Tosetti, S. ;Capraro, F. ;Patiño, D.

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Autores
Auat Cheeín, F. ;de la Cruz, C. ;Soria, C. ;Freire Bastos, T. ;Carelli, R.

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Autores
Andaluz, V. ;Roberti, F. ;Carelli, R.

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Autores
Guzzo, M. ;;Segura, M.

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Autores
Andaluz, V. ;Roberti, F. ;Carelli, R.

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Autores
Santos Brandao, A. ;Sarapura, J. ;Caldeira, E. ;Sarcinelli-Filho, M. ;Carelli, R.

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Autores
Orosco, E. ;Lopez, N. ;Soria, C. ;di Sciascio, F.

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Autores
Rampinellli, V. ;Santos Brandao, A. ;Sarcinelli, M. ;Martins, F. ;Carelli, R.

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Autores
Santos Brandao, A. ;Sarcinelli, M. ;Carelli, R.

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Autores
Santos Brandao, A. ;Sarapura, J. ;Caldeira, E. ;Sarcinelli, M. ;Carelli, R.

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Autores
Lopez, N. ;Orosco, E. ;di Sciascio, F.

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Autores
Traslosheros, A. ;Sebastián, J. ;Castillo, E. ;Roberti, F. ;Carelli, R.

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Autores
Delmas, G. ;Orellana, A. ;Ferreira, F. ;Secchi, H.

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Autores
Soria, C. ;Orellana, A. ;Carelli, R.

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Autores
Torres, J. ;Martin, M.

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Autores
Andaluz, V. ;Roberti, F. ;Carelli, R.

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Autores
Andaluz, V. ;Roberti, F. ;Carelli, R.

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Autores
Andaluz, V. ;Roberti, F. ;Carelli, R.

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Autores
Morales, B. ;Roberti, F. ;Sebastián, J. ;Carelli, R.

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Autores
Schugurensky, C. ;Fullana, R.

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1

Autores
Schugurensky, C. ;Fullana, R.

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1

Autores
Salinas, L. ;Slawiñski, E. ;Mut, V.

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1

Autores
Andaluz, V. ;Roberti, F. ;Carelli, R.

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Autores
Andaluz, V. ;Roberti, F. ;Carelli, R.

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Autores
Andaluz, V. ;Roberti, F. ;Carelli, R.

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0

Autores
Santos Brandao, A. ;Sarapura, J. ;Caldeira, E. ;Sarcinelli-Filho, M. ;Carelli, R.

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1

Autores
Delmas, G. ;Orellana, A. ;Ferreira, F. ;Secchi, H.

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Autores
Teodovich Sosa, L. ;Carelli, R.

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Autores
Vago Santana, L. ;Santos Brandao, A. ;Sarcinelli, M. ;Carelli, R.

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Autores
Zavalla, E. ;Fernandez, A. ;Orellana, A.

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Autores
Zavalla, E. ;Fernandez, A. ;Orellana, A.

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Autores
Diaz, M. ;MASANET, M. ;Fernandez, A. ;Zavalla, E.

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Autores
Ansaldi, E. ;Orellana, A. ;Zanini, A.

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Autores
Delmas, G. ;Orellana, A. ;Ferreira, F. ;Secchi, H.

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Autores
Amicarelli, A.; di Sciascio, F.;

Resumen:
En este trabajo se estudia la estabilidad y robustez de un controlador de oxígeno disuelto para el proceso de producción de δ-endotoxinas de Bacillus thuringiensis (Bt). El estudio se realiza a partir de las cotas para el error de control halladas mediante el análisis de Lyapunov. Estas cotas teóricas (o de Lyapunov) obtenidas mediante métodos matemáticos analíticos pueden ser contrastadas con cotas empíricas determinadas a través de Métodos de Monte Carlo. Se presenta la estrategia de control y el análisis de estabilidad según Lyapunov y luego se consideran en este trabajo errores de estimación de biomasa y errores paramétricos de modelado para evaluar la robustez de la estrategia de control. Se incluyen resultados de simulación a modo de ilustrar el trabajo.

Autores
Morales, B. ;Sarapura, J. ;Andaluz, V. ;Toibero, M. ;Roberti, F. ;Carelli, R.

Resumen:
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Autores
Diaz, M. ;MASANET, M. ;Fernandez, A. ;Zavalla, E.

Resumen:
1