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Neural Network Prediction of Corn Stover Saccharification Based on Its Structural Features.

ABSTRACT: The classic assay for a large population biomass is time-consuming, labor intensive, and chemically expensive. This paper would find out a rapid assay for predicting biomass digestibility from biomass structural features without hydrolysis. We examined the 62 representative corn stover accessions that displayed a diverse cell-wall composition and varied biomass digestibility. Correlation analysis was firstly to detect effects of cell-wall compositions and wall polymer features on corn stover digestibility. Based on the dependable relationship of structural features and digestibility, a neural networks model has been developed and successfully predicted the corn stover saccharification based on the features without enzymatic hydrolysis. The actual measured and net-simulated predicted corn stover saccharification had good results as mean square error of 1.80E-05, coefficient of determination of 0.942 and average relative deviation of 3.95. The trained networks satisfactorily predicted the saccharification results based on the features of corn stover. Predicting the corn stover saccharification without hydrolysis will reduce capital and operational costs for corn stover purchasing and storage.

SUBMITTER: Gao L

PROVIDER: S-EPMC6109571 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Neural Network Prediction of Corn Stover Saccharification Based on Its Structural Features.

Gao Le L Chen Shulin S Zhang Dongyuan D

BioMed research international 20180812

The classic assay for a large population biomass is time-consuming, labor intensive, and chemically expensive. This paper would find out a rapid assay for predicting biomass digestibility from biomass structural features without hydrolysis. We examined the 62 representative corn stover accessions that displayed a diverse cell-wall composition and varied biomass digestibility. Correlation analysis was firstly to detect effects of cell-wall compositions and wall polymer features on corn stover dig ...[more]

PMID: 30159330

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Project description:Production of biofuels, bioproducts, and bioenergy requires a well-characterized, stable, and reasonably uniform biomass supply and well-established supply chains for shipping biomass from farm fields to biorefineries, while achieving year-round production targets. Preserving and stabilizing biomass feedstock during storage is a necessity for cost-effective and sustainable biofuel production. Ensiling is a common storage method used to preserve and even improve forage quality; however, the impact of ensiling on biomass physical and chemical properties that influence bioconversion processes has been variable. Our objective in this work was to determine the effects of ensiling on lignocellulosic feedstock physicochemical properties and how that influences bioconversion requirements. We observed statistically significant decreases (p < 0.05) in the content of two major structural carbohydrates (glucan and xylan) of 5 and 8%, respectively, between the ensiled and non-ensiled materials. We were unable to detect differences in sugar yields from structural carbohydrates after pretreatment and enzymatic hydrolysis of the ensiled materials compared to non-ensiled controls. Based on this work, we conclude that ensiling the corn stover did not change the bioconversion requirements compared to the control samples and incurred losses of structural carbohydrates. At the light microscopy level, ensiled corn stover exhibited little structural change or relocation of cell wall components as detected by immunocytochemistry. However, more subtle structural changes were revealed by electron microscopy, as ensiled cell walls exhibit ultrastructural characteristics such as wall delimitation intermediate between non-ensiled and dilute-acid-pretreated cell walls. These findings suggest that alternative methods of conversion, such as deacetylation and mechanical refining, could take advantage of lamellar defects and may be more effective than dilute acid or hot water pretreatment for biomass conversion of ensiled materials.

Project description:BackgroundSimultaneous saccharification and co-fermentation (SSCF) process involves enzymatic hydrolysis of pretreated lignocellulosic biomass and fermentation of glucose and xylose in one bioreactor. The optimal temperatures for enzymatic hydrolysis are higher than the standard fermentation temperature of ethanologenic Saccharomyces cerevisiae. Moreover, degradation products resulting from biomass pretreatment impair fermentation of sugars, especially xylose, and can synergize with high temperature stress. One approach to resolve both concerns is to utilize a strain background with innate tolerance to both elevated temperatures and degradation products.ResultsIn this study, we screened a panel of 108 wild and domesticated Saccharomyces cerevisiae strains isolated from a wide range of environmental niches. One wild strain was selected based on its growth tolerance to simultaneous elevated temperature and AFEX™ (Ammonia Fiber Expansion) degradation products. After engineering the strain with two copies of the Scheffersomyces stipitis xylose reductase, xylitol dehydrogenase and xylulokinase genes, we compared the ability of this engineered strain to the benchmark 424A(LNH-ST) strain in ethanol production and xylose fermentation in standard lab medium and AFEX pretreated corn stover (ACS) hydrolysates, as well as in SSCF of ACS at different temperatures. In SSCF of 9% (w/w) glucan loading ACS at 35°C, the engineered strain showed higher cell viabilities and produced a similar amount of ethanol (51.3 g/L) compared to the benchmark 424A(LNH-ST) strain.ConclusionThese results validate our approach in the selection of wild Saccharomyces cerevisiae strains with thermo-tolerance and degradation products tolerance properties for lignocellulosic biofuel production. The wild and domesticated yeast strains phenotyped in this work are publically available for others to use as genetic backgrounds for fermentation of their pretreated biomass at elevated temperatures.

Project description:The production of value-added chemicals alongside biofuels from lignocellulosic hydrolysates is critical for developing economically viable biorefineries. Here, the production of propionic acid (PA), a potential building block for C3-based chemicals, from corn stover hydrolysate is investigated using the native PA-producing bacterium Propionibacterium acidipropionici.A wide range of culture conditions and process parameters were examined and experimentally optimized to maximize titer, rate, and yield of PA. The effect of gas sparging during fermentation was first examined, and N2 was found to exhibit improved performance over CO2. Subsequently, the effects of different hydrolysate concentrations, nitrogen sources, and neutralization agents were investigated. One of the best combinations found during batch experiments used yeast extract (YE) as the primary nitrogen source and NH4OH for pH control. This combination enabled PA titers of 30.8 g/L with a productivity of 0.40 g/L h from 76.8 g/L biomass sugars, while successfully minimizing lactic acid production. Due to the economic significance of downstream separations, increasing titers using fed-batch fermentation was examined by changing both feeding media and strategy. Continuous feeding of hydrolysate was found to be superior to pulsed feeding and combined with high YE concentrations increased PA titers to 62.7 g/L and improved the simultaneous utilization of different biomass sugars. Additionally, applying high YE supplementation maintains the lactic acid concentration below 4 g/L for the duration of the fermentation. Finally, with the aim of increasing productivity, high cell density fed-batch fermentations were conducted. PA titers increased to 64.7 g/L with a productivity of 2.35 g/L h for the batch stage and 0.77 g/L h for the overall process.These results highlight the importance of media and fermentation strategy to improve PA production. Overall, this work demonstrates the feasibility of producing PA from corn stover hydrolysate.

Dataset Information

Neural Network Prediction of Corn Stover Saccharification Based on Its Structural Features.

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Neural Network Prediction of Corn Stover Saccharification Based on Its Structural Features.

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