A fuzzy–split range control system applied to a fermentation process

Fonseca, Rodolpho Rodrigues; Schmitz, Jones Erni; Fileti, Ana Maria Frattini; Silva, Flávio Vasconcelos da

doi:10.1016/j.biortech.2013.05.083

Cited by 32 publications

(14 citation statements)

References 16 publications

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“…The temperature control of an alcoholic fermentation process through Takagi-Sugeno modeling is presented in [24]. A fuzzy-split range control system applied to the fermentation process is shown in [25].…”

Section: Literature Reviewmentioning

confidence: 99%

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

Ritonja

2021

Mathematics

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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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Section: Literature Reviewmentioning

confidence: 99%

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

Ritonja

2021

Mathematics

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“…The modified Monod equation according to the Michaelis–Menten developed by Aiba [21] gives the cell kinetics of the presented model.μ=μoCsKs+Cse-K1CpThe mathematical models of the fermentation process for the production of alcohol have been developed in the literature and used by various researchers to achieve different control objectives by using different control algorithms. The mathematical models of the bioreactor for the production of ethanol have been developed and implemented for temperature control [[1], [2], [3],[5], [6], [7],20]. Therefore, these mathematical models of the bioreactor are also considered in the present study for temperature control.…”

Section: Modeling Of a Continuous Bioreactormentioning

confidence: 99%

“…A fractional order IMC-PID (FOIMC-PID) and modified fractional order IMC-PID (MFOIMC-PID) were used for temperature control of the bioreactor in the fermentation process and further, a water cycle algorithm was used to optimize the designed controller parameters which lead to WMFOIMC-PID controller [1]. Takagi-Sugeno [5] and fuzzy-PI with split range control [6] were used to control the temperature of the bioreactor of the fermentation process. Pachauri et al [7] suggested two degrees of freedom PID based inferential control for the temperature control of continuous bioreactor in a fermentation process.…”

Section: Introductionmentioning

confidence: 99%

Temperature control of fermentation bioreactor for ethanol production using IMC-PID controller

Kumar

Prasad

Giri

et al. 2019

Biotechnology Reports

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“…Usually, conventional PID controller is not recommended in literature for nonlinear process. Therefore, people are searching some alterative solution in this regard and are inclined towards intelligent techniques such as fuzzy logic control [4,5,6,15]. Therefore, non-linear nature of CSTR requires other methods than conventional control techniques.…”

Section: Introductionmentioning

confidence: 99%

Split-range control of a Jacketed CSTR using self-tuning fuzzy PI controller

Gupta

Rana

Kumar

et al. 2015

2015 International Conference on Advances in Computer Engineering and Applications

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CSTR is a highly non-linear process and the nonlinear behavior of such processes requires other methods than conventional control. This paper addresses a novel application of Self-tuning Fuzzy PI Controller (STFPIC) to a Split-range Control strategy for circulation of cold fluid or hot fluid through Jacketed Continuous Stirred Tank Reactor (CSTR) in cascade control mode. In the primary loop STFPIC was used while in the secondary loop conventional proportional controller was implemented. Self-tuning mechanism of fuzzy PI controller is implemented with the help of a gain multiplier whose value is updated based on the instant error and rate of change of error. Grey Wolf Optimizer is used for tuning of primary as well as secondary controller gains in order to reduce Integral of Absolute Error (IAE). The overall approach has been simulated in LabVIEW ™ environment and a comparative study of STFPIC with conventional PID controller have been presented in this paper. Based on the intensive simulation studies it is found that the STFPIC outperformed PID for set-point tracking by 62.5% and for disturbance rejection by 63.4%, in terms of IAE.

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A fuzzy–split range control system applied to a fermentation process

Cited by 32 publications

References 16 publications

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

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

Temperature control of fermentation bioreactor for ethanol production using IMC-PID controller

Split-range control of a Jacketed CSTR using self-tuning fuzzy PI controller

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