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Open AccessArticle

Optimization of Wheat Straw Conversion into Microbial Lipids by Lipomyces tetrasporus DSM 70314 from Bench to Pilot Scale

and

Fermentation 2023, 9(2), 180; https://doi.org/10.3390/fermentation9020180 - 16 Feb 2023

Cited by 2 | Viewed by 1086

Abstract

Microbial lipids are renewable platforms for several applications including biofuels, green chemicals, and nutraceuticals that can be produced from several residual carbon sources. Lignocellulosic biomasses are abundant raw materials for the production of second-generation sugars with conversion yields depending on the quality of the hydrolysates and the metabolic efficiency of the microorganisms. In the present work, wheat straw pre-treated by steam explosion and enzymatically hydrolysed was converted into microbial lipids by Lipomyces tetrasporus DSM 70314. The preliminary optimization of the enzymatic hydrolysis was performed at the bench scale through the response surface methodology (RSM). The fermentation medium and set-up were optimized in terms of the nitrogen (N) source and carbon-to-nitrogen (C/N) ratio yielding to the selection of soy flour as a N source and C/N ratio of 160. The bench scale settings were scaled-up and further optimized at the 10 L-scale and finally at the 50 L pilot scale bioreactor. Process optimization also included oxygen supply strategies. Under optimized conditions, a lipid concentration of 14.8 gL⁻¹ was achieved corresponding to a 23.1% w/w lipid yield and 67.4% w/w lipid cell content. Oleic acid was the most abundant fatty acid with a percentage of 57%. The overall process mass balance was assessed for the production of biodiesel from wheat straw. Full article

(This article belongs to the Special Issue Recent Trend in Biofuel Fermentation from Renewable Biomass)

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Open AccessArticle

q-PCR Methodology for Monitoring the Thermophilic Hydrogen Producers Enriched from Elephant Dung

Khamanitjaree Saripan

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Fermentation 2022, 8(10), 506; https://doi.org/10.3390/fermentation8100506 - 01 Oct 2022

Cited by 1 | Viewed by 980

Abstract

This study aims to create a quantitative polymerase chain reaction (q-PCR) methodology for monitoring the hydrogen-producing mixed cultures enriched from elephant dung using alpha-cellulose as a carbon source through five generations of repetitive sub-culture. The enriched thermophilic mixed cultures from the fifth cultivation cycle gave the highest hydrogen yield of 170.3 mL H₂/g cellulose and were used to generate hydrogen from sawdust. Clostridium sp. and Thermoanaerobacterium sp. were the dominant bacteria in thermophilic mixed cultures with high hydrogen yield, according to polymerase chain reaction-denatured gradient gel electrophoresis (PCR-DGGE). q-PCR primers Chis150F and ClostIR, TherF and TherR, and BacdF and BacdR were developed to amplify the 16S rRNA genes of Clostridium sp., Thermoanaerobacterium sp., and Bacillus sp., respectively, for the quantification of hydrogen-producing bacteria in biohydrogen fermentation. Similar q-PCR analysis of Clostridium sp., Thermoanaerobacterium sp., and Bacillus sp. 16S rRNA gene amplification during hydrogen production from cellulose and sawdust revealed increasing gene copy number with time. The molecular approaches developed in this study can be used to monitor microbial communities in hydrogen fermentation processes efficiently. Full article

(This article belongs to the Special Issue Recent Trend in Biofuel Fermentation from Renewable Biomass)

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Open AccessArticle

Adaptive Network Fuzzy Inference System and Particle Swarm Optimization of Biohydrogen Production Process

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Mohammad Ali Abdelkareem

Fermentation 2022, 8(10), 483; https://doi.org/10.3390/fermentation8100483 - 26 Sep 2022

Cited by 10 | Viewed by 1031

Abstract

Green hydrogen is considered to be one of the best candidates for fossil fuels in the near future. Bio-hydrogen production from the dark fermentation of organic materials, including organic wastes, is one of the most cost-effective and promising methods for hydrogen production. One of the main challenges posed by this method is the low production rate. Therefore, optimizing the operating parameters, such as the initial pH value, operating temperature, N/C ratio, and organic concentration (xylose), plays a significant role in determining the hydrogen production rate. The experimental optimization of such parameters is complex, expensive, and lengthy. The present research used an experimental data asset, adaptive network fuzzy inference system (ANFIS) modeling, and particle swarm optimization to model and optimize hydrogen production. The coupling between ANFIS and PSO demonstrated a robust effect, which was evident through the improvement in the hydrogen production based on the four input parameters. The results were compared with the experimental and RSM optimization models. The proposed method demonstrated an increase in the biohydrogen production of 100 mL/L compared to the experimental results and a 200 mL/L increase compared to the results obtained using ANOVA. Full article

(This article belongs to the Special Issue Recent Trend in Biofuel Fermentation from Renewable Biomass)

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