Importance of stability study of continuous systems for ethanol production

Astudillo, Isabel Cristina Paz; Álzate, Carlos Ariel Cardona

doi:10.1016/j.jbiotec.2010.10.073

Cited by 44 publications

(30 citation statements)

References 90 publications

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“…In general, many fermentation studies have confirmed that the unstructured models poorly describe dynamic experiments in which composition and biomass activity change [13,15]. By contrast, the use of a more detailed approach of cell metabolism, aimed at better describing the dynamic behavior of the process, can lead to the development of structured models containing a large number of variables and parameters.…”

Section: Discussionmentioning

confidence: 99%

“…cCH 1:8 O 0:5 N 0:2 þ dCO 2 þ eH 2 O þ f C 2 H 6 Oðmolar basisÞ Kinetic modeling of growth, ethanol production, and substrate consumption by yeasts has been traditionally conducted using an unsegregated and unstructured approach for the biomass. This approach ignores the presence of individual cells and structural, functional, and compositional aspects of the cell, describing the complex processes of growth, ethanol production, and substrate consumption through simple kinetic equations [10][11][12][13][14][15]. Figure 1 shows a simplified scheme of this approach for the ethanol fermentation process by yeasts and bacteria.…”

Section: Introductionmentioning

confidence: 99%

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Kinetic Modeling of 1‐G Ethanol Fermentations

Oliveira¹,

Stremel²,

Dechechi³

et al. 2017

Fermentation Processes

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The most recent rise in demand for bioethanol, due mainly to economic and environmental issues, has required highly productive and efficient processes. In this sense, mathematical models play an important role in the design, optimization, and control of bioreactors for ethanol production. Such bioreactors are generally modeled by a set of first-order ordinary differential equations, which are derived from mass and energy balances over bioreactors. Complementary equations have also been included to describe fermentation kinetics, based on Monod equation with additional terms accounting for inhibition effects linked to the substrate, products, and biomass. In this chapter, a reasonable number of unstructured kinetic models of 1-G ethanol fermentations have been compiled and reviewed. Segregated models, as regards the physiological state of the biomass (cell viability), have also been reviewed, and it was found that some of the analyzed kinetic models are also applied to the modeling of second-generation ethanol production processes.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Kinetic Modeling of 1‐G Ethanol Fermentations

Oliveira¹,

Stremel²,

Dechechi³

et al. 2017

Fermentation Processes

View full text Add to dashboard Cite

show abstract

“…For this reason, a more complete knowledge of dynamic and static behavior is required, in order to understand, to operate, to optimize and to control ethanol fermentation processes [1]. Mathematical models have an important role to play in the optimization and control of bioethanol production processes.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…In these models, it is assumed that cells are entities in solution that interact with the environment [1,3]. No internal cell structure is recognized, and the cell population is treated as homogeneous [1]. In structured models, the biomass structure is defined by means of more than one variable, which represent cell components, such as the RNA content, enzymes, reactants and products [3].…”

Section: Introductionmentioning

confidence: 99%

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Kinetic Modeling and Optimization of a Batch Ethanol Fermentation Process

Oliveira

Oliveira²,

Tacin³

et al. 2016

J Bioprocess Biotech

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Mathematical model with time‐delay and delayed controller for a bioreactor

Villafuerte-Segura

Itzá-Ortiz

López‐Pérez

et al. 2022

Math Methods in App Sciences

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In this paper, a fractional Lotka-Volterra mathematical model for a bioreactor is proposed and used to fit the data provided by a bioprocess known as continuous fermentation of Zymomonas mobilis. The model contemplates a time-delay 𝜏 due to the dead-time (non-trivial) that the microbe needed to metabolize the substrate. A Hopf bifurcation analysis is performed to characterize the inherent self oscillatory experimental bioprocess response. As consequence, stability conditions for the equilibrium point together with conditions for limit cycles using the delay 𝜏 as bifurcation parameter are obtained. Under the assumptions that the use of observers, estimators, or extra laboratory measurements are avoided to prevent the rise of computational or monetary costs, for the purpose of control, we will only consider the measurement of the biomass. A simple controller that can be employed is the proportional action controller u(t) = k p x(t), which is shown to fail to stabilize the obtained model under the proposed analysis.Another suitable choice is the use of a delayed controller u(t) = k r x(t − h) which successfully stabilizes the model even when it is unstable. The delay h in the feedback control is due to the dead-time necessary to obtain the measurement of the biomass in the bioreactor by dry weight. Finally, the proposed theoretical results are corroborated through numerical simulations.

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Importance of stability study of continuous systems for ethanol production

Cited by 44 publications

References 90 publications

Kinetic Modeling of 1‐G Ethanol Fermentations

Kinetic Modeling of 1‐G Ethanol Fermentations

Kinetic Modeling and Optimization of a Batch Ethanol Fermentation Process

Mathematical model with time‐delay and delayed controller for a bioreactor

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