; Evariste Gueguim Kana. Kinetics of Bioethanol Production from Waste Sorghum Leaves Using Saccharomyces cerevisiae BY4743

Effect of nickel oxide nanoparticles on bioethanol production by Pichia kudriavzveii IFM 53048 using banana peel waste substrate

The use of nanomaterials in bioethanol production is promising and on the increase. In this report, the effect of nickel oxide nanoparticles (NiO NPs) on bioethanol production in the presence of a novel yeast strain, Pichia kudriavzveii IFM 53048 isolated from banana wastes was investigated. The hot percolation method was employed for the green synthesis of NiO NPs. The logistic and modified Gompertz kinetic models employed in this study showed a 0.99 coefficient of determination (R2) on cell growth, and substrate utilization on the initial rate data plot which indicate that these model were best suited for bioethanol production studies. As a result, 99.95% of the substrate was utilized to give 0.23 g/L/h-1 bioethanol productivity, and 51.28% fermentation efficiency, respectively. At 0.01 wt% of NiO NPs, maximum production was achieved with 0.27 g/g bioethanol yield. Meanwhile, 0.78 h-1 maximum specific growth rate (µmax) of the microorganism, 3.77 g/L bioethanol concentration (Pm), 0.49 g/L/h production rate (rp.m), and 2.43 h production lag time (tL) were obtained when 0.01 wt% of NiO NPs were used during the bioethanol production process. However, a decrease in bioethanol concentrations occurred at ≥0.02 wt% of NiO NPs. The incorporation of NiO NPs in the simultaneous saccharification and fermentation (SSF) process improved the production of bioethanol by 1.90 fold using banana peel wastes as substrate. These revealed NiO NPs could serve as a suitable biocatalyst in the green production of bioethanol from banana peel waste materials.

Ethanol Production from Olive Stones through Liquid Hot Water Pre-Treatment, Enzymatic Hydrolysis and Fermentation. Influence of Enzyme Loading, and Pre-Treatment Temperature and Time

Olive table industry, olive mills and olive pomace oil extraction industries annually generate huge amounts of olive stones. One of their potential applications is the production of bioethanol by fractionation of their lignocellulose constituents and subsequent fermentation of the released sugars using yeasts. In this work, we studied the influence of temperature (175–225 °C) and residence time (0–5 min) in the liquid hot-water pre-treatment of olive stones as well as the initial enzyme loading (different mixtures of cellulases, hemicellulases and β–glucosidases) in the later enzymatic hydrolysis on the release of fermentable sugars. The Chrastil’s model was applied to the d-glucose data to relate the severity of pre-treatment to enzyme diffusion through the pre-treated cellulose. Finally, the hydrolysate obtained under the most suitable conditions (225 °C and 0 min for pre-treatment; 24 CE initial enzyme concentration) was fermented into ethanol using the yeast Pachysolen tannophilus ATCC 32691. Considering the overall process, 6.4 dm3 ethanol per 100 kg olive stones were produced.

Impact of nanoparticle inclusion on bioethanol production process kinetic and inhibitor profile

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NiO nanoparticle (NP) inclusion enhanced bioethanol production up to 59.96 %.

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Band energy gap impact NP catalytic performance in bioethanol production.

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NiO nanoparticle biocatalyst improved bioethanol productivity by 145 %.

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Modified Gompertz model was used to describe ethanol production with NP inclusion.

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Metallic NiO nanoparticles significantly reduced acetic acid concentration by 110 %.

This study examines the effects of nanoparticle inclusion in instantaneous saccharification and fermentation (NIISF) of waste potato peels. The effect of nanoparticle inclusion on the fermentation process was investigated at different stages which were: pre-treatment, liquefaction, saccharification and fermentation. Inclusion of NiO NPs at the pre-treatment stage gave a 1.60-fold increase and 2.10-fold reduction in bioethanol and acetic acid concentration respectively. Kinetic data on the bioethanol production fit the modified Gompertz model (R ² > 0.98). The lowest production lag time (t _L) of 1.56 h, and highest potential bioethanol concentration (P _m) of 32 g/L were achieved with NiO NPs inclusion at different process stages; the liquefaction stage and the pre-treatment phase, respectively. Elevated bioethanol yield, coupled with substantial reduction in process inhibitors in the NIISF processes, demonstrated the significance of point of nanobiocatalysts inclusion for the scale-up development of bioethanol production from potato peels.

Progress in the development of alkali and metal salt catalysed lignocellulosic pretreatment regimes: Potential for bioethanol production

Lignocellulosic biomass (LCB) is well suited to address present day energy and environmental concerns, since it is abundant, environmentally benign and sustainable. However, the commercial application of LCB has been limited by its recalcitrant structure. To date, several biomass pretreatment systems have been developed to address this major bottleneck but have shown to be toxic and costly. Alkali and metal salt pretreatment regimes have emerged as promising non-toxic and low-cost treatments. This paper examines the progress made in lignocellulosic pretreatment using alkali and metal salts. The reaction mechanism of alkali and metal chloride salts on lignocellulosic biomass degradation are reviewed. The effect of salt pretreatment on lignin removal, hemicellulose solubilization, cellulose crystallinity, and physical structural changes are also presented. In addition, the enzymatic digestibility and inhibitor profile from salt pretreated lignocellulosic biomass are discussed. Furthermore, the challenges and future prospects on lignocellulosic pretreatment and bioethanol production are highlighted.

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

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 μ_max = 0.179 1/h and K_s = 11.37 g.L⁻¹ for the Monod model, whereas μ_max = 0.508 1/h, K_s = 47.53 g.L⁻¹ and K_i = 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.

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

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Bioethanol kinetics was investigated under SSA-F, SSA-U, MSA-F and MSA-U conditions.

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Monod, logistic and modified Gompertz models gave R² > 0.97.

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SSA-U pretreated SLW produced 25% more bioethanol than MSA-U.

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No difference was observed between filtered and unfiltered enzymatic hydrolysate.

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SLW residue showed a suitable protein and fat content for animal feed.

This study examines the kinetics of S. cerevisiae BY4743 growth and bioethanol production from sugarcane leaf waste (SLW), utilizing two different optimized pretreatment regimes; under two fermentation modes: steam salt-alkali filtered enzymatic hydrolysate (SSA-F), steam salt-alkali unfiltered (SSA-U), microwave salt-alkali filtered (MSA-F) and microwave salt-alkali unfiltered (MSA-U). The kinetic coefficients were determined by fitting the Monod, modified Gompertz and logistic models to the experimental data with high coefficients of determination R² > 0.97. A maximum specific growth rate (μ_max) of 0.153 h⁻¹ was obtained under SSA-F and SSA-U whereas, 0.150 h⁻¹ was observed with MSA-F and MSA-U. SSA-U gave a potential maximum bioethanol concentration (P_m) of 31.06 g/L compared to 30.49, 23.26 and 21.79 g/L for SSA-F, MSA-F and MSA-U respectively. An insignificant difference was observed in the μ_max and P_m for the filtered and unfiltered enzymatic hydrolysate for both SSA and MSA pretreatments, thus potentially reducing a unit operation. These findings provide significant insights for process scale up.