Dynamic bifurcations in continuous process of bioethanol production under aerobic conditions using Saccharomyces cerevisiae

Ciesielski, Adrian; Grzywacz, Robert

doi:10.1016/j.bej.2020.107609

Cited by 9 publications

(9 citation statements)

References 66 publications

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“…Now, we analyze the parameter dependence of the solution trajectories of the dimensionless model (9)- (11). Let…”

Section: Proof Let Us Definementioning

confidence: 99%

“…Theorem 4. Let Y(t) and Z(t) be two integrals of the system of differential Equations (9)- (11) with parameter sets S1 and S2, R * ≥ B * 2 and initial profiles Y 0 and Z 0 , respectively; then…”

Section: Proof Let Us Definementioning

confidence: 99%

“…However, another important challenge consists of the design of large-scale ethanol continuous processes. This is an area where relatively less work has been carried out compared to batch processes [11][12][13]. This field is also quite challenging since it is known that the process of bioethanol production by continuous fermentation is highly complex and nonlinear [14].…”

Section: Introductionmentioning

confidence: 99%

“…This field is also quite challenging since it is known that the process of bioethanol production by continuous fermentation is highly complex and nonlinear [14]. The nonlinearity manifests itself in the occurrence of phenomena such as bistability [15] and stable limit cycles [11].…”

Section: Introductionmentioning

confidence: 99%

“…The development of mathematical models for continuous ethanol processes can be a very useful tool, which can be applied to explore the complex phenomena that occur in industrial ethanol continuous processes [11][12][13][14][15][16]. On another, equally important level, these mathematical models can also be used for parametric studies and to offer insight into ways to increase the productivity of the process.…”

Section: Introductionmentioning

confidence: 99%

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Stability and Performance Analysis of Bioethanol Production with Delay and Growth Inhibition in a Continuous Bioreactor with Recycle

et al. 2021

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This paper proposes and analyzes a mathematical model for the production of bioethanol in a continuous bioreactor with recycling. The kinetics correspond to the use of Saccharomyces bayanus for the fermentation of sugars found in wastewater from soft drinks. The proposed model considers product growth latency, which was experimentally found in batch studies of ethanol production. Furthermore, the inhibition effect of ethanol is expressed by a modified version of the classical Andrew’s model for substrate inhibition. The proposed model consists of only three ordinary differential equations containing a minimal number of operating parameters, which include the bioreactor residence time, glucose feed concentration, recycle ratio and the fraction of biomass removed from the reactor by the flow. The positivity and the boundedness of solutions of the model were confirmed under reasonable restrictions of parameters. The stability analysis showed that there is a value of residence time at which an exchange of stability occurs between the trivial washout and non-washout solutions. This critical value depends only on the substrate feed concentration, biomass death rate, recycle ratio and purge fraction. Dynamic simulations of the model were carried out for substrate concentration in the range of 100–250 g/L, commonly used for the production of ethanol. An inverse response due to the inhibition effects of ethanol was observed in the time evolution of substrate and biomass concentrations. Parametric studies showed that ethanol concentration increases with the recycle ratio, with the inverse of residence time and with the inverse of purge fraction. The effect of ethanol latency has, on the other hand, a substantial effect on ethanol concentration. Despite its unstructured nature and the fact that some parameters such as temperature and acidity were not taken into consideration, the proposed model managed to provide useful results on the bioreactor-settler stability and the effect of key parameters on its dynamic behavior, which could pave the way for future optimization studies.

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“…Now, we analyze the parameter dependence of the solution trajectories of the dimensionless model (9)- (11). Let…”

Section: Proof Let Us Definementioning

confidence: 99%

Section: Proof Let Us Definementioning

confidence: 99%