Kinetics of Bioethanol Production from Waste Sorghum Leaves Using Saccharomyces cerevisiae BY4743

Rorke, Daneal C.S.; Kana, E.B. Gueguim

doi:10.3390/fermentation3020019

Cited by 36 publications

(4 citation statements)

References 30 publications

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“…Figure 2 shows the Lineweaver–Burk plot estimating the μ max and K s values in a batch ethanol fermentation where the values of μ max and K s were found to be 0.179 h −1 and 11.37 g.L −1 respectively, which were too close to the values reported in previous studies [17,18] using sweet sorghum leaves and oil palm jus. However, these values were different from the values reported in other studies [19,20,21,22,23].…”

Section: Resultsmentioning

confidence: 48%

Modelling of Molasses Fermentation for Bioethanol Production: A Comparative Investigation of Monod and Andrews Models Accuracy Assessment

et al. 2019

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Modelling has recently become a key tool to promote the bioethanol industry and to optimise the fermentation process to be easily integrated into the industrial sector. In this context, this study aims at investigating the applicability of two mathematical models (Andrews and Monod) for molasses fermentation. The kinetics parameters for Monod and Andrews were estimated from experimental data using Matlab and OriginLab software. The models were simulated and compared with another set of experimental data that was not used for parameters’ estimation. The results of modelling showed that μmax = 0.179 1/h and Ks = 11.37 g.L−1 for the Monod model, whereas μmax = 0.508 1/h, Ks = 47.53 g.L−1 and Ki = 181.01 g.L−1 for the Andrews model, which are too close to the values reported in previous studies. The validation of both models showed that the Monod model is more suitable for batch fermentation modelling at a low concentration, where the highest R squared was observed at S0 = 75 g.L−1 with an R squared equal to 0.99956, 0.99954, and 0.99859 for the biomass, substrate, and product concentrations, respectively. In contrast, the Andrews model was more accurate at a high initial substrate concentration and the model data showed a good agreement compared to the experimental data of batch fermentation at S0 = 225 g.L−1, which was reflected in a high R squared with values 0.99795, 0.99903, and 0.99962 for the biomass, substrate, and product concentrations respectively.

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Section: Resultsmentioning

confidence: 48%

Modelling of Molasses Fermentation for Bioethanol Production: A Comparative Investigation of Monod and Andrews Models Accuracy Assessment

et al. 2019

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“…The consequences of this work show that the reaction surface technique could be utilized to advance ethanol creation yield from wipe gourd. The outcomes gotten in this exploration for bioethanol creation from wipe gourd are along these lines promising (Rorke and Gueguim Kana, 2017;Ballesteros et al, 2004, Fan et al, 1987. The ethanol yield in % decreased up to the mid-range concentration, followed by a further increase in the yield.…”

Section: Process Optimization Of Fermentation Statgementioning

confidence: 88%

Bioethanol (Environment Support Fuel) Production and Optimization From Pineapple Peel and Banana Peel

2023

Global NEST: The International Journal

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<p>The goal of this investigation was to focus on the viability of synchronous aging of natural product waste with Aspergillus Niger TISTR 3063 co-societies producing ethanol. Additionally noted was how the aging temperature affected the yield of ethanol. Peels from pineapple and banana were used as substrates, and they were prepared by being slashed into little rectangular pieces. A 250 ml Erlenmeyer jar containing glucose was used to mature several bundles of natural product waste as a control. Analysis of the factors involved in the production of organic waste included sugar, pH, TS, vs, debris, moisture, COD, and TKN. In my review, bio ethanol was produced from banana strips and pineapple strips using pretreatment techniques, more specifically, soluble and acidic pretreatment. The process of aging interaction is then used to produce bio-ethanol. However, the production of bioethanol from food crops needs to be regulated to ensure food security and requires line-by-line analysis to move potential financial and environmental risks. Utilizing Plan Master Programming version 13, determine whether the nature of the bioethanol has changed after synthesis.Using plan master programming, it is possible to see how the item ethanol is being streamlined. Plot the chart for the item's actual and predicted values, one component diagram, and 3D surface. The fuel qualities of the bioethanol that was given were thought to be comparable to those of diesel fuel, and they also complied with ASTM regulations.</p>

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“…Nowadays, many chemicals are produced from lignocellulosic biomass and ethanol is one of them. Ethanol has wide range of applications due to its renewable nature, ease of storage, the fact that it is free of sulfur, and its less contribution to global warming and air pollution (Rorke & Kana, 2017). Ethanol is commercially produced from edible feedstocks such as corn, maize, sorghum, barley etc.…”

Section: Introductionmentioning

confidence: 99%

Bioethanol Production from Sequential Acidic-Alkaline Pretreated Sorghum Straw Hydrolysate and Investigation of the Effects of Fermentation Parameters using Response Surface Methodology

Dube¹,

Gebriheta²

2022

Preprint

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The depletion and environmental problems associated with fossil fuels have encouraged us to look for alternative feedstock that do not compromise food security and the environment. Sorghum is a fast-growing crop that can be harvested twice a year and produces both food and straw that can be utilized in the production of bio-based fuels. In this study, the production of bioethanol and the effects of fermentation parameters on ethanol yield are presented. A sequential pretreatment method was employed, using dilute sulfuric acid (1%) at 125 °C in the first stage and dilute sodium hydroxide (1.25 %) at 90 °C in the second stage. The residues left after the sequential pretreatment stage were hydrolyzed using acid hydrolysis. The sugar concentration of the hydrolysates was determined using the phenol sulfuric acid method, and three hydrolysates having sugar levels of 30.42 g/L, 31.79 g/L, and 32.9875 g/L were selected for fermentation. The ethanol yield was determined after 72 hours of fermentation at 30 °C with varying inoculum sizes (5%, 10%, and 15%) and pH (4.5, 5, and 5.5). With a maximum ethanol yield of 0.617 mL/g (48.742%) produced at a sugar content of 32.9875 g/L, pH of 5, and inoculum size of 15%, statistical analysis showed that all three independent parameters affected ethanol yield. According to these findings, while raising sugar content, inoculum amount, and pH all initially result in higher ethanol yields, doing so further reduces yield. So, in order to increase ethanol yield, fermentation conditions must be carefully managed while producing ethanol from sequential acidic-alkaline pretreated sorghum straw. The strategy followed by using sequential acidic-alkaline pretreatment of sorghum straw provides prospects for efficient and effective production of biofuels from alternative feedstock.

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Kinetics of Bioethanol Production from Waste Sorghum Leaves Using Saccharomyces cerevisiae BY4743

Cited by 36 publications

References 30 publications

Modelling of Molasses Fermentation for Bioethanol Production: A Comparative Investigation of Monod and Andrews Models Accuracy Assessment

Modelling of Molasses Fermentation for Bioethanol Production: A Comparative Investigation of Monod and Andrews Models Accuracy Assessment

Bioethanol (Environment Support Fuel) Production and Optimization From Pineapple Peel and Banana Peel

Bioethanol Production from Sequential Acidic-Alkaline Pretreated Sorghum Straw Hydrolysate and Investigation of the Effects of Fermentation Parameters using Response Surface Methodology

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