Robust Control for cultivation of microorganisms in a high density fed-batch bioreactor

Rodríguez, A.; Muñoz, Jorge; Luna, Ricardo; Correa, Jose Ricardo Perez; Bocanegra, Alma Rosa Dominguez; Ramirez, Hebert Sira; Castro, R.

doi:10.1109/tla.2015.7164219

Cited by 13 publications

(15 citation statements)

References 1 publication

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“…For = 0, 1, 2, the controller parameters are determined through a Monte Carlo method, where the number of simulations is determined by application of Theorem 2 of [30]. All simulations are implemented in Matlab6 (MathWorks, Inc.), with a sample time Δ = 0.01 h. For each controller (i.e., for each ), 1,000 simulations are run; and the controller parameters are randomly chosen, such that the roots ( ) of the linear systems defined in the righthand side of (9a) and (9b) and left-hand side of (15) and (19) verify ∈ (0, 1). More specifically, for = 0, the controller parameters are directly taken from [30].…”

Section: Resultsmentioning

confidence: 99%

“…where 1 , 2 , and 3 are the roots of the linear system in (19). Table 2 summarizes the results obtained with each controller.…”

Section: Resultsmentioning

confidence: 99%

“…As suggested by (19), under the assumption of a constant or linear varying uncertainty ( 2 E = 0), the uncertainty has no influence on the error dynamics. As in the case of the single integrator, the controller parameters K, k 1 , and k 2 should be chosen in order to ensure the stability of the left-hand side of (19).…”

Section: Multiple Integral Actionsmentioning

confidence: 99%

“…As in the case of the single integrator, the controller parameters K, k 1 , and k 2 should be chosen in order to ensure the stability of the left-hand side of (19).…”

Section: Multiple Integral Actionsmentioning

confidence: 99%

“…A fed-batch bioreactor control with simultaneous dynamic identification of unknown state and parameters has been investigated through an algorithm based on the auxiliary model principle with adaptive controllers 2 Complexity [18]. A control strategy was proposed to track a desired profile for the biomass of a yeast culture in a high-density fed-batch bioreactor [19]. This last strategy was based on a model that included an uncertainty term estimated through an adaptive technique.…”

Section: Introductionmentioning

confidence: 99%

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Neural Network-Based State Estimation for a Closed-Loop Control Strategy Applied to a Fed-Batch Bioreactor

et al. 2017

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The lack of online information on some bioprocess variables and the presence of model and parametric uncertainties pose significant challenges to the design of efficient closed-loop control strategies. To address this issue, this work proposes an online state estimator based on a Radial Basis Function (RBF) neural network that operates in closed loop together with a control law derived on a linear algebra-based design strategy. The proposed methodology is applied to a class of nonlinear systems with three types of uncertainties: (i) time-varying parameters, (ii) uncertain nonlinearities, and (iii) unmodeled dynamics. To reduce the effect of uncertainties on the bioreactor, some integrators of the tracking error are introduced, which in turn allow the derivation of the proper control actions. This new control scheme guarantees that all signals are uniformly and ultimately bounded, and the tracking error converges to small values. The effectiveness of the proposed approach is illustrated on the basis of simulated experiments on a fed-batch bioreactor, and its performance is compared with two controllers available in the literature.

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Section: Resultsmentioning

confidence: 99%

“…where 1 , 2 , and 3 are the roots of the linear system in (19). Table 2 summarizes the results obtained with each controller.…”

Section: Resultsmentioning

confidence: 99%

Section: Multiple Integral Actionsmentioning

confidence: 99%

“…As in the case of the single integrator, the controller parameters K, k 1 , and k 2 should be chosen in order to ensure the stability of the left-hand side of (19).…”

Section: Multiple Integral Actionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Neural Network-Based State Estimation for a Closed-Loop Control Strategy Applied to a Fed-Batch Bioreactor

et al. 2017

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Optimal Control of a MIMO Bioreactor System Using Direct Approach

Simorgh

Razminia

Mobayen

et al. 2021

Int. J. Control Autom. Syst.

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Implementation of Stir-Speed Adopted Controllers Onto a Batch Bioreactor for Improved Fermentation

Ritonja

2021

IEEE Access

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Quality, quantity, and economy of the fermentation product depend on the transient response and the steady-state of the fermentation process. Due to its simple construction, an industrial batch bioreactor cannot be equipped with a closed-loop control system that applies adding or removing substances during fermentation. This theoretical and practical study displays how fermentation can be controlled, in this case by altering the rotational speed of a stirrer system of such a bioreactor. A numerical analysis was employed to set up a mathematical model that describes the impact of the stirrer speed on the fermentation. The model consists of linear and non-linear parts. To determine their parameters, a least square identification and a particle swarm optimization method were utilized. The model was fitted and tested experimentally in a laboratory on the operating batch bioreactor. The design and synthesis of a conventional linear control system and an advanced model reference adaptive control system, which is also convenient for control of non-linear controlled plants, were carried out based on the determined mathematical model. Both control concepts were first implemented into the dSpace 1103 control system, convenient for faster development and prototyping. Finally, they were realized with the Siemens SIMATIC S7 programmable logic controller, which was ideal for industrial environment operation. During the operation of the bioreactor, the control systems were tested along with the acquisition of measured data involving CO2 concentration of a fermentation product. The findings of successfully applying linear and adaptive controllers of the milk fermentation in the batch bioreactor are analyzed and presented.

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Robust Control for cultivation of microorganisms in a high density fed-batch bioreactor

Cited by 13 publications

References 1 publication

Neural Network-Based State Estimation for a Closed-Loop Control Strategy Applied to a Fed-Batch Bioreactor

Neural Network-Based State Estimation for a Closed-Loop Control Strategy Applied to a Fed-Batch Bioreactor

Optimal Control of a MIMO Bioreactor System Using Direct Approach

Implementation of Stir-Speed Adopted Controllers Onto a Batch Bioreactor for Improved Fermentation

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