Kinetic models for batch ethanol production from sweet sorghum juice under normal and high gravity fermentations: Logistic and modified Gompertz models

Phukoetphim, Niphaphat; Salakkam, Apilak; Laopaiboon, Pattana; Laopaiboon, Lakkana

doi:10.1016/j.jbiotec.2016.12.012

Cited by 92 publications

(40 citation statements)

References 19 publications

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“…Total reducing sugar was quantified with the 3,5-dinitrosalicylic acid method [ 16 ] and glucose was measured using a glucose kit assay (Megazyme). The yeast biomass concentration in the fermentation broth was determined using a pre-established correlation dependence on biomass dry weight as a function of cell count [ 14 ]. The concentration of bioethanol was determined using a Vernier Ethanol sensor interfaced with the Vernier LabQuest monitor (ETH-BTA, Vernier Software and Technology, USA).…”

Section: Methodsmentioning

confidence: 99%

“…With increasing interest in the commercial applications of batch bioethanol processes, several kinetics models have been proposed which describe microbial growth, product formation and substrate consumption [ 14 ]. These models are extremely useful in the process development of bioethanol production, since they assist in predicting fermentation performance in response to changes in various factors [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

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Bioethanol production from sugarcane leaf waste: Effect of various optimized pretreatments and fermentation conditions on process kinetics

Moodley

2019

Biotechnology Reports

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Highlights Bioethanol kinetics was investigated under SSA-F, SSA-U, MSA-F and MSA-U conditions. Monod, logistic and modified Gompertz models gave R 2 > 0.97. SSA-U pretreated SLW produced 25% more bioethanol than MSA-U. No difference was observed between filtered and unfiltered enzymatic hydrolysate. SLW residue showed a suitable protein and fat content for animal feed.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Bioethanol production from sugarcane leaf waste: Effect of various optimized pretreatments and fermentation conditions on process kinetics

Moodley

2019

Biotechnology Reports

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“…Therefore, a proper prediction for this phase could provide an accurate estimate of the data for the further downstream process. model ethanol production from different biomass feedstocks such as sweet sorghum stalks [51], sweet sorghum juice [33], synthetic grape must [36], and sugar beet [47]. Such a kinetics model allows us to define specific parameters that may help to scale up the bioprocess [47].…”

Section: Kinetic Parameters Estimation For Biomass Ethanol 1-propanmentioning

confidence: 99%

“…vv/DMFIT.aspx) to obtain the kinetic parameters µ max , lag phase time, and final biomass content, where µ max is the maximum specific growth rate (h −1 ) of the yeast. Ethanol and selected aliphatic components were modeled by the modified Gompertz (Equation (3)) model using Matlab ® (R2016a, The Math Works Inc., Natick, MA, USA), where P max is the potential maximum concentration of the metabolite, r p,m is the maximum component production rate, T L is the lag phase time, and t is the fermentation time [33].…”

Section: Kinetic Parameter Estimationmentioning

confidence: 99%

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Bioethanol Production from Cachaza as Hydrogen Feedstock: Effect of Ammonium Sulfate during Fermentation

et al. 2017

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Abstract:Cachaza is a type of non-centrifugal sugarcane press-mud that, if it is not employed efficiently, generates water pollution, soil eutrophication, and the spread of possible pathogens. This biomass can be fermented to produce bioethanol. Our intention is to obtain bioethanol that can be catalytically reformed to produce hydrogen (H 2 ) for further use in fuel cells for electricity production. However, some impurities could negatively affect the catalyst performance during the bioethanol reforming process. Hence, the aim of this study was to assess the fermentation of Cachaza using ammonium sulfate ((NH 4 ) 2 SO 4 ) loadings and Saccharomyces cerevisiae strain to produce the highest ethanol concentration with the minimum amount of impurities in anticipation of facilitating further bioethanol purification and reforming for H 2 production. The results showed that ethanol production from Cachaza fermentation was about 50 g·L −1 and the (NH 4 ) 2 SO 4 addition did not affect its production. However, it significantly reduced the production of branched alcohols. When a 160 mg·L −1 (NH 4 ) 2 SO 4 was added to the fermentation culture, 2-methyl-1-propanol was reduced by 41% and 3-methyl-1-butanol was reduced by 6%, probably due to the repression of the catabolic nitrogen mechanism. Conversely, 1-propanol doubled its concentration likely due to the higher threonine synthesis promoted by the reducing sugar presence. Afterwards, we employed the modified Gompertz model to fit the ethanol, 2M1P, 3M1B, and 1-propanol production, which provided acceptable fits (R 2 > 0.881) for the tested compounds during Cachaza fermentation. To the best of our knowledge, there are no reports of the modelling of aliphatic production during fermentation; this model will be employed to calculate yields with further scaling and for life cycle assessment.

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Yeast immobilization systems for second‐generation ethanol production: actual trends and future perspectives

Chacón-Navarrete

Martı́n

Moreno-García

2021

Biofuels Bioprod Bioref

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Yeast immobilization with low‐cost carrier materials is a suitable strategy to optimize the fermentation of lignocellulosic hydrolysates for the production of second‐generation (2G) ethanol. It is defined as the physical confinement of intact cells to a certain region of space (the carrier) with the preservation of their biological activity. This technological approach facilitates promising strategies for second‐generation bioethanol production due to the enhancement of the fermentation performance that is expected to be achieved. Using immobilized cells, the resistance to inhibitors contained in the hydrolysates and the co‐utilization of sugars are improved, along with facilitating separation operations and the reuse of yeast in new production cycles. Until now, the most common immobilization technology used calcium alginate as a yeast carrier but other supports such as biochar or multispecies biofilm membranes have emerged as interesting alternatives. This review compiles updated information about cell carriers and yeast‐cell requirements for immobilization, and the benefits and drawbacks of different immobilization systems for second‐generation bioethanol production are investigated and compared.

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Kinetic models for batch ethanol production from sweet sorghum juice under normal and high gravity fermentations: Logistic and modified Gompertz models

Cited by 92 publications

References 19 publications

Bioethanol production from sugarcane leaf waste: Effect of various optimized pretreatments and fermentation conditions on process kinetics

Bioethanol production from sugarcane leaf waste: Effect of various optimized pretreatments and fermentation conditions on process kinetics

Bioethanol Production from Cachaza as Hydrogen Feedstock: Effect of Ammonium Sulfate during Fermentation

Yeast immobilization systems for second‐generation ethanol production: actual trends and future perspectives

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