On-line estimation of unknown kinetics for the enzymatic synthesis of ampicillin

Selişteanu, Dan; Petre, Emil; Roman, Monica; Popescu, Dan; Bobasu, Eugen

doi:10.1109/carpathiancc.2011.5945874

Cited by 2 publications

(4 citation statements)

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“…The high gain observers design necessitates a factorization of the model ( 24 ) [ 13 , 14 , 20 ]. We will consider that the yield matrix K is of full rank, which is true for our particular model, and for general class of bioprocesses' models is a generic property.…”

Section: Methodsmentioning

confidence: 99%

“…In order to obtain the equations of the observers for our bioprocess, the next factorization of yield matrix and corresponding partition are considered [ 20 ]:…”

Section: Methodsmentioning

confidence: 99%

“…Then, for the enzymatic synthesis of ampicillin, two high-gain estimators can be derived from ( 26 ) [ 20 ].…”

Section: Methodsmentioning

confidence: 99%

“…In this paper, which is an extended work of [ 20 ], three main correlated issues concerning the enzymatic bioprocess of ampicillin are studied: modeling, kinetics estimation, and control. First, a pseudo bond graph approach is proposed for the modeling of an enzymatic synthesis of ampicillin, widely used in bioindustry.…”

Section: Introductionmentioning

confidence: 99%

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Enzymatic Synthesis of Ampicillin: Nonlinear Modeling, Kinetics Estimation, and Adaptive Control

Roman

Selişteanu

2012

Journal of Biomedicine and Biotechnology

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Nowadays, the use of advanced control strategies in biotechnology is quite low. A main reason is the lack of quality of the data, and the fact that more sophisticated control strategies must be based on a model of the dynamics of bioprocesses. The nonlinearity of the bioprocesses and the absence of cheap and reliable instrumentation require an enhanced modeling effort and identification strategies for the kinetics. The present work approaches modeling and control strategies for the enzymatic synthesis of ampicillin that is carried out inside a fed-batch bioreactor. First, a nonlinear dynamical model of this bioprocess is obtained by using a novel modeling procedure for biotechnology: the bond graph methodology. Second, a high gain observer is designed for the estimation of the imprecisely known kinetics of the synthesis process. Third, by combining an exact linearizing control law with the on-line estimation kinetics algorithm, a nonlinear adaptive control law is designed. The case study discussed shows that a nonlinear feedback control strategy applied to the ampicillin synthesis bioprocess can cope with disturbances, noisy measurements, and parametric uncertainties. Numerical simulations performed with MATLAB environment are included in order to test the behavior and the performances of the proposed estimation and control strategies.

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Section: Methodsmentioning

confidence: 99%

“…In order to obtain the equations of the observers for our bioprocess, the next factorization of yield matrix and corresponding partition are considered [ 20 ]:…”

Section: Methodsmentioning

confidence: 99%

“…Then, for the enzymatic synthesis of ampicillin, two high-gain estimators can be derived from ( 26 ) [ 20 ].…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Enzymatic Synthesis of Ampicillin: Nonlinear Modeling, Kinetics Estimation, and Adaptive Control

Roman

Selişteanu

2012

Journal of Biomedicine and Biotechnology

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Model-Based Monitoring of Biotechnological Processes—A Review

2021

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The monitoring of the main variables and parameters of biotechnological processes is of key importance for the research and control of the processes, especially in industrial installations, where there is a limited number of measurements. For this reason, many researchers are focusing their efforts on developing appropriate algorithms (software sensors (SS)) to provide reliable information on unmeasurable variables and parameters, based on the available on-line information. In the literature, a large number of developments related to this topic that concern data-based and model-based sensors are presented. Up-to-date reviews of data-driven SS for biotechnological processes have already been presented in the scientific literature. Hybrid software sensors as a combination between the abovementioned ones are under development. This gives a reason for the article to be focused on a review of model-based software sensors for biotechnological processes. The most applied model-based methods for monitoring the kinetics and state variables of these processes are analyzed and compared. The following software sensors are considered: Kalman filters, methods based on estimators and observers of a deterministic type, probability observers, high-gain observers, sliding mode observers, adaptive observers, etc. The comparison is made in terms of their stability and number of tuning parameters. Particular attention is paid to the approach of the general dynamic model. The main characteristics of the classic variant proposed by D. Dochain are summarized. Results related to the development of this approach are analyzed. A key point is the presentation of new formalizations of kinetics and the design of new algorithms for its estimation in cases of uncertainty. The efficiency and applicability of the considered software sensors are discussed.

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On-line estimation of unknown kinetics for the enzymatic synthesis of ampicillin

Cited by 2 publications

References 13 publications

Enzymatic Synthesis of Ampicillin: Nonlinear Modeling, Kinetics Estimation, and Adaptive Control

Enzymatic Synthesis of Ampicillin: Nonlinear Modeling, Kinetics Estimation, and Adaptive Control

Model-Based Monitoring of Biotechnological Processes—A Review

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