Model Reference Adaptive Control for Milk Fermentation in Batch Bioreactors

Ritonja, Jožef; Goršek, Andreja; Pečar, Darja

doi:10.3390/app10249118

Cited by 6 publications

(10 citation statements)

References 32 publications

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“…Reference [23] presents the control of CO2 production based on a linear controller, which generates a control input for the heating/cooling system. In [24] the dissolved CO2 concentration is controlled by using the elementary model reference adaptive controller. All four references (21)(22)(23)(24) show the use of linear or nonlinear controllers, which are basically intended for controlling linear processes.…”

Section: B Existing Solutions With Literature Reviewmentioning

confidence: 99%

“…In [24] the dissolved CO2 concentration is controlled by using the elementary model reference adaptive controller. All four references (21)(22)(23)(24) show the use of linear or nonlinear controllers, which are basically intended for controlling linear processes. Nevertheless, the obtained simulation and experimental results prove that the regulators control the considered nonlinear fermentation process satisfactorily.…”

Section: B Existing Solutions With Literature Reviewmentioning

confidence: 99%

“…It is necessary to point out that most commercial versions of the batch bioreactors are equipped with control systems that allow regulation of the temperature or the stirrer speed but do not have control systems that would control the dynamics of the entire fermentation process. Existing control systems, namely, do not ensure that transient responses of concentrations of substances will follow reference trajectories (except in [23,24]).…”

Section: B Existing Solutions With Literature Reviewmentioning

confidence: 99%

“…and the σ-term to avoid divergence of the integral gains in presence of disturbance: = 0.95 (24) were obtained through the simulations, and were used to carry out the adaptive control.…”

Section: B Adaptive Structure and Parametersmentioning

confidence: 99%

See 3 more Smart Citations

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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Section: B Existing Solutions With Literature Reviewmentioning

confidence: 99%

Section: B Existing Solutions With Literature Reviewmentioning

confidence: 99%

Section: B Existing Solutions With Literature Reviewmentioning

confidence: 99%

“…and the σ-term to avoid divergence of the integral gains in presence of disturbance: = 0.95 (24) were obtained through the simulations, and were used to carry out the adaptive control.…”

Section: B Adaptive Structure and Parametersmentioning

confidence: 99%

See 2 more Smart Citations

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

Ritonja

2021

IEEE Access

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“…Reference [30] shows the implementation of the PI-controller. Reference [31] studies the application of model reference adaptive control. The absence of publications studying the control of the yield of the fermentation product in batch bioreactors was also an additional motivation to work even more in-depth and intensively in this area.…”

Section: Literature Reviewmentioning

confidence: 99%

Adaptive Control of CO2 Production during Milk Fermentation in a Batch Bioreactor

Ritonja

2021

Mathematics

Self Cite

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The basic characteristic of batch bioreactors is their inability to inflow or outflow the substances during the fermentation process. This follows in the simple construction and maintenance, which is the significant advantage of batch bioreactors. Unfortunately, this characteristic also results in the inability of the current industrial and laboratory batch bioreactors to control fermentation production during the process duration. In some recent studies, it was shown that changing the temperature could influence the execution of the fermentation process. The presented paper shows that this phenomenon could be used to develop the closed-loop control system for the fermentation production control in batch bioreactors. First, based on theoretical work, experiments, and numerical methods, the appropriate structure of the mathematical model was determined and parameters were identified. Next, the closed-loop control system structure for batch bioreactor was proposed, and the linear and adaptive control system based on this structure and the derived and identified model were developed. Both modeling and adaptive control system design are new and represent original contributions. As expected, due to the non-linearity of the controlled plant, the adaptive control represents a more successful approach. The simulation and experimental results were used to confirm the applicability of the proposed solution.

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Designing event‐triggered fault hiding controller for non‐linear affine bioreactor system: Adaptive virtual actuator approach

Ghasemi

Zadeh

2022

IET Control Theory & Appl

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In recent years, bioreactor control is very important due to the challenges associated with the highly non‐linear behaviours. The dynamical structure of the system has been extended compared to previous researches and correspondingly the controller configuration is developed in this approach. This approach deliberates an event‐triggered adaptive fault‐hiding prototype for the non‐linear affine bioreactor system. Since this controller has the ability to hide the faults and external disturbances effects, the separated fault detection, isolation, and identification unit are not required in this approach. Also, the measurement information is triggered by the incidence of a certain event to amend the network resources. The focal advantages of the planned methodology are: (1) Robustness of the planned approach against external disturbances, (2) the presented virtual actuator conceals mechanism faults, (3) the convergence of tracking errors in the presence of both the faults and the external disturbances is guaranteed to neighbourhood of zero, and (4) significantly reducing data exchange based on event‐triggered strategy with a state‐independent threshold. The stability of closed‐loop system is guaranteed based on Lyapunov approach. The simulation results show that the proposed fault‐tolerant approach on the bioreactor systems is effective.

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Model Reference Adaptive Control for Milk Fermentation in Batch Bioreactors

Cited by 6 publications

References 32 publications

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

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

Adaptive Control of CO2 Production during Milk Fermentation in a Batch Bioreactor

Designing event‐triggered fault hiding controller for non‐linear affine bioreactor system: Adaptive virtual actuator approach

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