Adaptive stabilisation of ethanol production during the continuous fermentation of Saccharomyces cerevisiae

Ignatova, Miléna; Patarinska, T.; Ljubenova, V.; Bucha, J.; Böhm, Josef; Nedoma, Petr

doi:10.1049/ip-cta:20030872

Cited by 11 publications

(4 citation statements)

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“…For each microorganism and target product, there exists an optimal physiological state or trajectory, where the target product is produced at the maximum quantity and at a satisfactory quality. The goal of the control is to maintain the biochemical processes at that optimal state during the fermentation process [6]. Sometimes, the optimal trajectory approaches the critical one, which is unstable, making it difficult to control.…”

Section: Current State and Motivationmentioning

confidence: 99%

Model Reference Adaptive Control for Milk Fermentation in Batch Bioreactors

2020

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This paper presents the advanced control theory’s original utilisation to realise a system that controls the fermentation process in batch bioreactors. Proper fermentation control is essential for quality fermentation products and the economical operation of bioreactors. Batch bioreactors are very popular due to their simple construction. However, this simplicity presents limitations in implementing control systems that would ensure a controlled fermentation process. Batch bioreactors do not allow the inflow/outflow of substances during operation. Therefore, we have developed a control system based on a stirrer drive instead of material flow. The newly developed control system ensures tracking of the fermentation product time course to the reference trajectory by changing the stirrer’s speed. Firstly, the paper presents the derivation of the enhanced mathematical model suitable for developing a control system. A linearisation and eigenvalue analysis of this model were made. Due to the time-consuming determination of the fermentation model and the variation of the controlled plant during operation, the use of adaptive control is advantageous. Secondly, a comparison of different adaptive approaches was made. The model reference adaptive control was selected on this basis. The control theory is presented, and the control realisation described. Experimental results obtained with the laboratory batch bioreactor confirm the advantages of the proposed adaptive approach compared to the conventional PI-control.

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Section: Current State and Motivationmentioning

confidence: 99%

Model Reference Adaptive Control for Milk Fermentation in Batch Bioreactors

2020

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“…are yield coeffi cients., R Sus , R res -susceptible and resistant starch utilization rate respectively, μ and ν -specifi c growth and ethanol production rates respectively. Since the model for control has described the dynamics of the main variables as well as the unstructured one, an identifi cation of the parameters for model (2) is done using the batch phase of the process, applying an optimization procedure proposed in [9,11,12,13]. The optimization criterion is the minimization of the mean square error between the state variables of unstructured model and model (2).…”

Section: Model For Controlmentioning

confidence: 99%

“…The SSFSE process could be accounted into such class of processes because the glucose is produced as intermediate product by starch and then consumed as substrate for biomass growth and ethanol production. The methods mentioned above are based on the so called General Dynamical Model Approach (3,(8)(9)(10)(11)(12)(13)18). Software sensors of intermediate metabolite production and consumption rates are designed [19] and included in the adaptive control law (7,13).…”

Section: Introductionmentioning

confidence: 99%

Control of one Stage Bio Ethanol Production by Recombinant Strain

Lyubenova

Ochoa

Repke

et al. 2007

Biotechnology & Biotechnological Equipment

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A new method for adaptive control of simultaneous saccharifi cation and fermentation of starch to ethanol by the recombinant strain Saccharomyces cerevisiae YPB-G is proposed. The process monitoring is enriched by new software sensors of glucose consumption and production rates. The difference between their values is defi ned as a control marker which is used for switching from batch to fed-batch mode automatically and for determining the amplitude and duration of starch feeding pulses (control input). Simulation results have shown that the proposed control strategy stabilizes the process at an equilibrium state for the glucose concentration. In this way, the ethanol concentration in the reactor and the productivity of the process are increased.

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“…Modern industrial chemical plants consist of many process units arranged in a complex structure which produces a process network [1], such as a multistage extraction process [2], a gas boiler heating system [3] and an ethanol production process [4]. Such a process network contains many subsystems interacting with each other through mass and energy interconnections [5] and these interconnections result in strong coupling among subsystems.…”

Section: Introductionmentioning

confidence: 99%