Kinetics of substrate consumption and product formation in closed acetic fermentation systems

Abundant amount of melon fruit waste with high sugar anda water content is potential as a substrate for hydrogen production by dark fermentation. This study investigated the performance of biohydrogen production from melon fruit waste in a stirred tank reactor with initial concentration 13100 mg sCOD/L, in a room temperature, initial pH 7 and controlling final pH 5.5 by adding NaOH. The fermentation lasted for 24 hours. pH, VS, sCOD, VFA, biogas volume, hydrogen content, and cell concentration was analized every hour to determine the performance of reactor. Hydrogen content yielding 16.20% with hydrogen production rate (HPR) of 458.12 mL/Lreactor/day in STP condition. Substrate consumption at the end of fermentation reached 24.61% sCOD and 78.28% VS. Metabolite products was dominated by acetate and butyrate with butyrate to acetate ratio of 1.2. The kinetic of product formation was investigated by the kinetic model of Gompertz. Meanwhile the kinetics of cell growth was approximated by Logistics model.

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Thermolysis of polyethylene hydroperoxides in the melt6. Mechanisms and formal kinetics of product formation in the presence of phenolic antioxidants

Polymer Degradation and Stability ◽

10.1016/s0141-3910(02)00094-0 ◽

2002 ◽

Vol 77 (1) ◽

pp. 157-168 ◽

Cited By ~ 9

Author(s):

F. Gugumus

Keyword(s):

Product Formation ◽

Phenolic Antioxidants ◽

Formal Kinetics ◽

Kinetics Of

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Improved Dynamic System of Microbial Continuous Fermentation and its Parameter Identification

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.633-634.545 ◽

2014 ◽

Vol 633-634 ◽

pp. 545-549

Author(s):

Hong Li Xiao ◽

Lan Zhang Chong ◽

Fei Li Hang ◽

Wang Yong

Keyword(s):

Dynamic System ◽

Consumption Rate ◽

Continuous Fermentation ◽

Formation Rate ◽

Product Formation ◽

Cellular Growth ◽

Specific Substrate ◽

Substrate Consumption ◽

Specific Product ◽

Product Formation Rate

In this paper, the nonlinear dynamic system of microbe continuous fermentation products 1,3-propanediol (1,3-PD) is rewritten by improving the specific cellular growth rate, specific substrate consumption rate and specific product formation rate. Firstly, under the condition of substrate glycol excess and active trans-membrane transport, according to the dynamic behavior the fermentation process, we consider the glycerol and 1,3–PD concentration within the cell, and improve the specific cellular growth rate, specific substrate consumption rate and specific product formation rate, then rewrite the dynamic system of microbial continuous fermentation process. Secondly, taking the dynamic system as main constraint condition, we establish the parameter identification model and prove the existence of the optimal solution. Lastly, the numerical results calculated by particle swarm algorithm show that the improved model is suitable for describe the dynamic behavior of 1,3-PD, but is not accurate enough for by-products.

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Application Of The Z And Laplace Transforms To Panification Yeast Production Using An Airlift Bioreactor

10.21203/rs.3.rs-20594/v1 ◽

2020 ◽

Author(s):

Arnaldo Silva Oliveira ◽

Juan C. B. Neto ◽

Igor J. B. Santos ◽

Edson R. Nucci

Keyword(s):

Cell Growth ◽

Product Formation ◽

Laplace Transforms ◽

Discrete Models ◽

Average Error ◽

Growth Substrate ◽

Substrate Consumption ◽

Yeast Saccharomyces Cerevisiae ◽

Mathematical Techniques ◽

Discrete Time Models

Abstract The Z- and Laplace transforms are mathematical techniques applied to solve difference equations and differential equations, respectively. Mathematical models used to describe cell growth, substrate consumption and product formation in bioprocesses can be represented by these types of equations. Thus, in this work, the fermentation process of the yeast Saccharomyces cerevisiae was modeled using different models from the literature, and the Z- and Laplace transforms were applied to solve the equations. Once the equations were solved, the models were represented in state space and simulated in Octave® software. Finally, the models were compared to experimental data from previous studies and to each other. Verhulst was the model that best described the process, with an average error of 4.74% for cell growth and 13.9% for substrate consumption. This work is unprecedented since no works that use the Z transform and discrete models for the representation of fermentation of this yeast were found in the literature. Even more importantly, this work proved that discrete-time models can be applied to bioprocesses with the same precision as continuous-time models.

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Experimental and Kinetic Production of Ethanol Using Mucilage Juice Residues from Cocoa Processing

International Journal of Chemical Reactor Engineering ◽

10.1515/ijcre-2017-0262 ◽

2018 ◽

Vol 16 (11) ◽

Cited By ~ 2

Author(s):

Teresa Romero Cortes ◽

Jaime A. Cuervo-Parra ◽

Víctor José Robles-Olvera ◽

Eduardo Rangel Cortes ◽

Pablo A. López Pérez

Keyword(s):

Ethanol Yield ◽

Scale Up ◽

Pilot Scale ◽

Product Formation ◽

Model Parameters ◽

Pichia Kudriavzevii ◽

Proposed Model ◽

Analysis Of Results ◽

Residual Plots ◽

Kinetics Of

AbstractEthanol was produced using mucilage juice residues from processed cocoa with Pichia kudriavzevii in batch fermentation. Experimental results showed that maximum ethanol concentration was 13.8 g/L, ethanol yield was 0.50 g-ethanol/g glucose with a productivity of 0.25 g/L h. Likewise, a novel phenomenological model based on the mechanism of multiple parallel coupled reactions was used to describe the kinetics of substrate, enzyme, biomass and product formation. Model parameters were optimized by applying the Levenberg-Marquardt approach. Analysis of results was based on statistical metrics (such as confidence interval), sensitivity and by comparing calculated curves with the experimental data (residual plots). The efficacy of the proposed mathematical model was statistically evaluated using the dimensionless coefficient for efficiency. Results indicated that the proposed model can be applied as a way of augmenting bioethanol production from laboratory scale up to semi-pilot scale.

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