Adriana Amicarelli

Including dissolved oxygen dynamics into the Bt δ-endotoxins production process model and its application to process control

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.

Nonlinear control of the dissolved oxygen concentration integrated with a biomass estimator for production of Bacillus thuringiensis δ-endotoxins

Abstract Bacillus thuringiensis is a microorganism that allows the biosynthesis of δ-endotoxins with toxic properties against some insect larvae, being often used for the production of biological insecticides. A key issue for the bioprocess design consists in adequately tracking a pre-specified optimal profile of the dissolved oxygen concentration. To this effect, this paper aims at developing a novel control law based on a nonlinear dynamic inversion method. The closed-loop strategy includes an observer based on a Bayesian Regression with Gaussian Process, which is used for on-line estimating the biomass present in the bioreactor. Unlike other approaches, the proposed controller leads to an improved response time with effective disturbance rejection properties, while simultaneously prevents undesired oscillations of the dissolved oxygen concentration. Simulation results based on available experimental data were used to show the effectiveness of the proposal.

Substrate Feeding Strategy Integrated with a Biomass Bayesian Estimator for a Biotechnological Process

Abstract This work proposes a substrate feeding strategy for a bioprocess integrated with a biomass estimator based in nonlinear filtering techniques. The performance of the proposed estimator and the substrate strategy are illustrated for the δ-endotoxin production of Bacillus thuringiensis (Bt) in batch and fed batch cultures. Nonlinear filtering techniques constitutes an adequate option as estimation tool because of the strongly nonlinear dynamics of this bioprocess and also due to nature of the uncertainties and perturbations that cannot be supposed Gaussians distributed. Biomass estimation is performed from substrate and dissolved oxygen. Substrate feeding strategy is intended to obtain high product concentration. Simulations results along with their experimental verifications demonstrate the acceptable performance of the proposed biomass estimator and the substrate feeding strategy.

On-line Biomass Estimation in a Batch Bio-technological Process: Bacillus Thuringiensis δ - Endotoxins Production.

Biomass concentration in a biotechnological process is one of the states that characterize 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 online because this measurement is not possible or this is economically unprofitable. 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. A review of commonly used techniques can be found in (Bastin & Dochain, 1990; Dochain, 2003) and references therein. 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, high gain observer, nonlinear observers, and full horizon observer. In this class of estimators, a detailed knowledge of the reaction kinetics and associated transport 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 disadvantage 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 the observer may not converge. 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 is based on the knowledge of the structure (functional form) of the data-fitting model (in advance). This is a difficult task since it involves the heuristic selection of an appropriate nonlinear model structure from numerous alternatives.

Model based predictive strategy for dissolved oxygen control applied to a batch bioprocess

This paper proposes a novel Model Predictive Control strategy of dissolved oxygen (DO). The control design finds application on the Bt delta-endotoxins batch process production. This strategy improves the DO control by preventing the usual drawbacks that appear with a step level change in the DO reference profile. Simulations results are provided as well as comparison results against a well-known Lyapunov based controller and a classical PID controller.

Bacillus thuringiensis process design using state controllability index

Batch processes are inherently irreversible. In fact, design parameters and initial states can affect irreversibility and controllability of a batch process. Current design methodologies do not include controllability as a criterion for process specification. In this work, the case of Bacillus thuringiensis process is studied in batch operation, and its operational conditions are defined according to a phenomenological-based model. Regarding process design, a novel methodology is presented using set-theoretic methods and a controllability index to find the best design parameter and initial state values. Using this methodology the controllability index is doubled improving the dynamic behavior of the process.