Multiobjective parameter estimation problems of fermentation processes using a high ethanol tolerance yeast

Wang, Feng‐Sheng; Sheu, Jeng-Shin

doi:10.1016/s0009-2509(00)00038-5

Cited by 96 publications

(36 citation statements)

References 15 publications

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“…One different proposal to describe the inhibitory effects of ethanol upon μ X and μ P was presented by Wang and Sheu [45] when they applied multiobjective optimization methods to estimate the parameters of kinetic models of batch and fed-batch processes for ethanol production, using one yeast that is highly tolerant to ethanol (Saccharomyces diastaticus). In their study, the kinetic models for the specific rate of cell growth and product formation were represented as follows:…”

Section: Fermentation Processes 98mentioning

confidence: 99%

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: Fermentation Processes 98mentioning

confidence: 99%

Kinetic Modeling of 1‐G Ethanol Fermentations

Oliveira¹,

Stremel²,

Dechechi³

et al. 2017

Fermentation Processes

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“…Interesting is also the Saccharomyces diastaticus (LORRE 316), an ethanol tolerant yeast capable of producing ethanol from corn starch, yielding a final concentration as high as 17.5% (v/v) (Wang & Sheu, 2000).…”

Section: Inhibitormentioning

confidence: 99%

Latest Frontiers in the Biotechnologies for Ethanol Production from Lignocellulosic Biomass

Paola¹,

Isabella²,

Romano³

2011

Biofuel Production-Recent Developments and Prospects

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“…Second, the parameters in the kinetic expressions are determined; this is usually achieved by measuring the concentrations of cells, product and substrate as they vary over time and then using nonlinear regression methods to estimate the kinetic parameters. Parameter estimation is an essential step in the verification and subsequent use of a mathematical model [6]. from batch experimental data is generally applied to simulate the concentration profiles of cell mass, substrate and product in fed-batch and continuous fermentation processes, in order to assess alternative operating modes [6].…”

Section: Introductionmentioning

confidence: 99%

“…Parameter estimation is an essential step in the verification and subsequent use of a mathematical model [6]. from batch experimental data is generally applied to simulate the concentration profiles of cell mass, substrate and product in fed-batch and continuous fermentation processes, in order to assess alternative operating modes [6]. Clearly, the limitation of this procedure is that the kinetic behavior of fed-batch and continuous fermentation systems is not exactly the same as that of a batch system; even so, it is possible to have a fair first approximation to the behavior of these other fermentation systems [8].…”

Section: Introductionmentioning

confidence: 99%