Process maps with metabolic constraints for bioethanol production by continuous fermentation

Ciesielski, Adrian; Grzywacz, Robert

doi:10.1016/j.ces.2020.116134

Cited by 3 publications

(2 citation statements)

References 29 publications

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“…For ethanol fermentation to yield maximum results, it is crucial to optimize the operating conditions of the process (Asaithambi et al, 2021;Ciesielski & Grzywacz, 2021;Chen et al, 2021;Estevão et al, 2021;Gao et al, 2022;Li et al, 2021;Rodman & Gerogiorgis, 2020;Shen et al, 2021;Urtubia et al, 2021). Among the key parameters, fermentation temperature plays a significant role in achieving high productivity.…”

Section: Introductionmentioning

confidence: 99%

Modeling the Effect of Temperature on Ethanol Fermentation Using Saccharomyces Cerevisiae CSI-1 by Genetic Programming

Nolasco-Hipolito,

Carvajal-Zarrabal,

Carrillo-Ahumada

et al. 2023

JART

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In this study, genetic programming (GP) was the systematic method used to identify the structure and parameters of a mathematical model for ethanol fermentation. The mathematical model simulated the effect of temperature on the kinetics of batch ethanol fermentation and helped to find out the optimum temperature for the better performance of the process. Saccharomyces cerevisiae CSI-1 growing in cane molasses-based media was the microorganism used in all the experiments. Achieving the model’s precision in describing the experimental observations involved the estimation of its structure (non-linear principally) and its constant parameters. The model found describes the fermentation kinetics and showed a fair prediction for Dry cell weight (DCW), Colony forming units/mL (CFU/mL), Residual sucrose (RS), Residual glucose (RG) and Ethanol concentration (E). The model was used to optimize the operating conditions of the process. The predictions from the model in terms of Mean Square Error (MSE) and Sum Squared Error (SSE) fitted the experimental data well with fitness values in a range of R2 ? 0:92.

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Section: Introductionmentioning

confidence: 99%

Modeling the Effect of Temperature on Ethanol Fermentation Using Saccharomyces Cerevisiae CSI-1 by Genetic Programming

Nolasco-Hipolito,

Carvajal-Zarrabal,

Carrillo-Ahumada

et al. 2023

JART

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show abstract

“…A last example we present is the industrial interest. As a production process, metabolism is used in pharmaceutical industry to produce for instance monoclonal antibodies [2] or in petrochemical industry to produce for instance bioethanol [3,4].…”

Section: Introductionmentioning

confidence: 99%

Combining Kinetic and Constraint-Based Modelling to Better Understand Metabolism Dynamics

2021

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To understand the phenotypic capabilities of organisms, it is useful to characterise cellular metabolism through the analysis of its pathways. Dynamic mathematical modelling of metabolic networks is of high interest as it provides the time evolution of the metabolic components. However, it also has limitations, such as the necessary mechanistic details and kinetic parameters are not always available. On the other hand, large metabolic networks exhibit a complex topological structure which can be studied rather efficiently in their stationary regime by constraint-based methods. These methods produce useful predictions on pathway operations. In this review, we present both modelling techniques and we show how they bring complementary views of metabolism. In particular, we show on a simple example how both approaches can be used in conjunction to shed some light on the dynamics of metabolic networks.

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Production of microalgae biomass in a two-stage continuous bioreactor: Control of microalgae-bacteria competition by spatial uncoupling of nitrogen and organic carbon feeding

Altimari

Caprio

Brasiello

et al. 2023

Chemical Engineering Science

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Process maps with metabolic constraints for bioethanol production by continuous fermentation

Cited by 3 publications

References 29 publications

Modeling the Effect of Temperature on Ethanol Fermentation Using Saccharomyces Cerevisiae CSI-1 by Genetic Programming

Modeling the Effect of Temperature on Ethanol Fermentation Using Saccharomyces Cerevisiae CSI-1 by Genetic Programming

Combining Kinetic and Constraint-Based Modelling to Better Understand Metabolism Dynamics

Production of microalgae biomass in a two-stage continuous bioreactor: Control of microalgae-bacteria competition by spatial uncoupling of nitrogen and organic carbon feeding

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