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The demand for clean, sustainable, secure energy is growing... and the U.S. Department of Energy's National Renewable Energy Laboratory (NREL) is answering the call. NREL's National Bioenergy Center is pioneering biofuels research and development and accelerating the pace these technologies move into the marketplace.
The objective of the Analytical Development and Support (ADS) Project is to produce and maintain the critical analytical methods and tools that enable evaluation of emerging biofuels R&D at NREL and in the broader biofuels research community. Our project is divided into two tasks: one task to develop novel analytical techniques and improve existing methods and one task to maintain existing analytical capabilities at NREL and provide outreach to the wider community. The ADS Project is world-recognized for our Laboratory Analytical Procedures (LAPS) which provide detailed procedures for compositional analysis of biomass and have been adopted as the de facto standards within the biofuels community largely due to the transparency of the methods and the high reputation of NREL's research. Our dialog with stakeholders allows us to provide robust, precise, accurate, and publicly available analytical procedures for better valuation of scientific tools such as our recent accomplishment in developing a cellulose assay to support the EPA and industry in calculation of converted cellulose during starch ethanol production. We continue to develop analytical capabilities to support BETO's directives to research cost advantaged fuels such as animal wastes and novel bioproducts like 2,3-Butane-diol.
Wet anaerobic storage of algal biomass is a promising preservation approach that can ensure a continuous supply of these feedstocks to biorefineries year-round. An effective solution to preservation must ensure minimal dry matter loss and a change in biochemical composition during storage. Therefore, the objective of this study is to investigate the preservation of Nannochloropsis gaditana biomass through wet anaerobic storage and its impact on biomass quality. Prior to storage, the algae sample is inoculated with two different strains of lactic acid bacteria and thereafter stored for 30 and 180 days. Each inoculant limited the dry matter loss to <10% (dry basis) after the storage duration. Final pH values (4.3–4.8) indicate that the biomass samples are properly ensiled, achieving the acidic conditions necessary for preservation. Compositional analysis of the biomass after storage shows a reduction in carbohydrate content, a relative increase in lipid content, and no significant change in the protein fraction. Glucose and galactose were the most prevalent sugar monomers. The low dry matter loss and minimal compositional change indicate that wet anaerobic storage is an effective means of preserving algal biomass and ensuring a constant supply of algal biomass feedstock to a biorefinery.
In a biorefinery producing cellulosic biofuels, biomass pretreatment will significantly influence the efficacy of enzymatic hydrolysis and microbial fermentation. Comparison of different biomass pretreatment techniques by studying the impact of pretreatment on downstream operations at industrially relevant conditions and performing comprehensive mass balances will help focus attention on necessary process improvements, and thereby help reduce the cost of biofuel production. An on-going collaboration between the three US Department of Energy (DOE) funded bioenergy research centers (Great Lakes Bioenergy Research Center (GLBRC), Joint BioEnergy Institute (JBEI) and BioEnergy Science Center (BESC)) has given us a unique opportunity to compare the performance of three pretreatment processes, notably dilute acid (DA), ionic liquid (IL) and ammonia fiber expansion (AFEX TM ), using the same source of corn stover. Separate hydrolysis and fermentation (SHF) was carried out using various combinations of commercially available enzymes and engineered yeast (Saccharomyces cerevisiae 424A) strain. The optimal commercial enzyme combination (Ctec2: Htec2: Multifect Pectinase, percentage total protein loading basis) was evaluated for each pretreatment with a microplate-based assay using milled pretreated solids at 0.2% glucan loading and 15 mg total protein loading/g of glucan. The best enzyme combinations were 67:33:0 for DA, 39:33:28 for IL and 67:17:17 for AFEX. The amounts of sugar (kg) (glucose: xylose: total gluco- and xylo-oligomers) per 100 kg of untreated corn stover produced after 72 hours of 6% glucan loading enzymatic hydrolysis were: DA (25:2:2), IL (31:15:2) and AFEX (26:13:7). Additionally, the amounts of ethanol (kg) produced per 100 kg of untreated corn stover and the respective ethanol metabolic yield (%) achieved with exogenous nutrient supplemented fermentations were: DA (14.0, 92.0%), IL (21.2, 93.0%) and AFEX (20.5, 95.0%), respectively. The reason for lower ethanol yield for DA is because most of the xylose produced during the pretreatment was removed and not converted to ethanol during fermentation. Compositional analysis of the pretreated biomass solids showed no significant change in composition for AFEX treated corn stover, while about 85% of hemicellulose was solubilized after DA pretreatment, and about 90% of lignin was removed after IL pretreatment. As expected, the optimal commercial enzyme combination was different for the solids prepared by different pretreatment technologies. Due to loss of nutrients during the pretreatment and washing steps, DA and IL pretreated hydrolysates required exogenous nutrient supplementation to ferment glucose and xylose efficiently, while AFEX pretreated hydrolysate did not require nutrient supplementation.
It is notoriously difficult to build an accurate universal model for biomass pyrolysis due to its sensitivity to a wide number of critical material attributes such as chemical species and physical sizes. In this work, a biomass pyrolysis kinetics with 32 heterogeneous reactions and 59 species was implemented in an open-source multiphase computational fluid dynamics (CFD) software MFiX and validated against two different experimental pyrolysis data sets that provided detailed data describing chemical component yields. The reaction scheme was then used to build a surrogate model and assess the sensitivity of pyrolysis yields to feedstock compositions. The sensitivity analysis determined that the yield of bio-char showed a strong positive sensitivity to the carbon-rich lignin and tannin pseudo-species in the reaction scheme while the bio-oil and bio-gas were correlated to oxygen-rich lignin pseudo-species. The reaction scheme was then integrated into a coarse-grained discrete element model to simulate fast pyrolysis in a bubbling fluidized bed over a range of feedstock particle sizes. The reactor simulations showed further sensitivity to particle size and hydrodynamics. Notably, particles under 0.5 mm have small heat transfer limitations but left the reactor before completely converting and thus reduced the bio-oil yield. Results from this study can be used to guide future development of highly accurate models for fast pyrolysis reactors with a variety of feedstock properties and operating conditions.
Poplars trees are well suited for biofuel production due to their fast growing habit, favorable wood composition and adaptation to a broad range of environments. The availability of a reference genome sequence, ease of vegetative propagation and availability of transformation methods also make poplar an ideal model for the study of wood formation and biomass growth in woody, perennial plants. The objective of this project was to conduct a genome-wide association genetics study to identify genes that regulate bioenergy traits in Populus deltoides (eastern cottonwood). Populus deltoides is a genetically diverse keystone forest species in North America and an important short rotation woody crop for the bioenergy industry. We searched for associations between eight growth and wood composition traits and common and low-frequency single-nucleotide polymorphisms (SNPs) detected by targeted resequencing of 18,153 genes in a population of 391 unrelated individuals. To increase power to detect associations with low-frequency variants, multiple-marker association tests were used in combination with single-marker association tests. Significant associations were discovered for all phenotypes and are indicative that low-frequency polymorphisms contribute to phenotypic variance of several bioenergy traits. These polymorphism are critical tools for the development of specialized plant feedstocks for bioenergy.
Poplars trees are well suited for biofuel production due to their fast growing habit, favorable wood composition and adaptation to a broad range of environments. The availability of a reference genome sequence, ease of vegetative propagation and availability of transformation methods also make poplar an ideal model for the study of wood formation and biomass growth in woody, perennial plants. The objective of this project was to conduct a genome-wide association genetics study to identify genes that regulate bioenergy traits in Populus deltoides (eastern cottonwood). Populus deltoides is a genetically diverse keystone forest species in North America and an important short rotation woody crop for the bioenergy industry. We searched for associations between eight growth and wood composition traits and common and low-frequency single-nucleotide polymorphisms (SNPs) detected by targeted resequencing of 18,153 genes in a population of 391 unrelated individuals. To increase power to detect associations with low-frequency variants, multiple-marker association tests were used in combination with single-marker association tests. Significant associations were discovered for all phenotypes and are indicative that low-frequency polymorphisms contribute to phenotypic variance of several bioenergy traits. These polymorphism are critical tools for the development of specialized plant feedstocks for bioenergy.
Airborne, in-situ measurements from PEM-Tropics-A (September/October 1996) are analyzed to show the presence of distinct pollution plumes in the middle-tropical troposphere of the remote South Pacific (10-30degS). These elevated plumes cause a relative maximum at about 5-7km attitude in the vertical distribution of primary and secondary species characteristic of fuel combustion and biomass burning (CO, C2H2, C2H6, CH3Cl, PAN, O3). Similar plumes were also observed at mid-latitudes in the middle troposphere during three flights east of New Zealand (40-45degS). In all, pollution plumes with CO larger than 100 ppb were observed 24 times on 7 separate flight days south of the equator. The observed plumes were generally embedded in very dry air. Ten-day back trajectory analysis supports the view that these originated from the biomass burning regions of South Africa (and South America) and were transported to the South Pacific along long-distance subsiding trajectories. The chemical composition of the southern Pacific troposphere analyzed from the PEM-Tropics-A data is compared with data from the tropical regions of the northern Pacific (PEM-West-A) and southern Atlantic (TRACE-A) during the same Sept/Oct time period. Sizable perturbations in the abundance of ozone and its key precursors, resulting from the transport of pollution originating from biomass burning sources, are observed in much of the Southern Hemispheric troposphere.
Abstract Background Duckweeds are small, rapidly growing aquatic flowering plants. Due to their ability for biomass production at high rates they represent promising candidates for biofuel feedstocks. Duckweeds are also excellent model organisms because they can be maintained in well-defined liquid media, usually reproduce asexually, and because genomic resources are becoming increasingly available. To demonstrate the utility of duckweed for integrated metabolic studies, we examined the metabolic adaptation of growing Lemna gibba cultures to different nutritional conditions. Results To establish a framework for quantitative metabolic research in duckweeds we derived a central carbon metabolism network model of Lemna gibba based on its draft genome. Lemna gibba fronds were grown with nitrate or glutamine as nitrogen source. The two conditions were compared by quantification of growth kinetics, metabolite levels, transcript abundance, as well as by 13 C-metabolic flux analysis. While growing with glutamine, the fronds grew 1.4 times faster and accumulated more protein and less cell wall components compared to plants grown on nitrate. Characterization of photomixotrophic growth by 13 C-metabolic flux analysis showed that, under both metabolic growth conditions, the Calvin-Benson-Bassham cycle and the oxidative pentose-phosphate pathway are highly active, creating a futile cycle with net ATP consumption. Depending on the nitrogen source, substantial reorganization of fluxes around the tricarboxylic acid cycle took place, leading to differential formation of the biosynthetic precursors of the Asp and Gln families of proteinogenic amino acids. Despite the substantial reorganization of fluxes around the tricarboxylic acid cycle, flux changes could largely not be associated with changes in transcripts. Conclusions Through integrated analysis of growth rate, biomass composition, metabolite levels, and metabolic flux, we show that Lemna gibba is an excellent system for quantitative metabolic studies in plants. Our study showed that Lemna gibba adjusts to different nitrogen sources by reorganizing central metabolism. The observed disconnect between gene expression regulation and metabolism underscores the importance of metabolic flux analysis as a tool in such studies.
Corresponding Standard Reference Material Data used in Partial Least Squares Regression Models for Sugar Composition Estimates in Biomass in: Economic Impact of Yield and Composition Variation in Bioenergy Crops: Populus trichocarpa (for corresponding manuscript: DOI: 10.1002/bbb.2148) PDF Files: Images of 1H NMR spectra for neutralized 2-stage acid hydrolysates of 4 NIST Standard Reference Material biomass samples (Monterey Pine 8493, Sugarcane Bagasse 8491, Wheat Straw 8494, and Eastern Cottonwood/Poplar 8492) and 2 Center for Bioenergy Innovation reference biomass samples (Poplar - Populus trichocarpa and Switchgrass - Panicum Virgatum). Suppression of the water peak was achieved using a NOESY-1D with presaturation, a recycle delay of 5 s, and a total of 64 scans. Spectra were acquired at 298 K and processed with automatic phase correction, baseline correction, and chemical shift referencing to TSP-d4. Images show all 1H data from 10 to 1ppm with inset spectra of region of interest (4.0 to 3.1 ppm). Text Files: Spectra for neutralized 2-stage acid hydrolysates of 4 NIST Standard Reference Material biomass samples (Monterey Pine 8493, Sugarcane Bagasse 8491, Wheat Straw 8494, and Eastern Cottonwood/Poplar 8492) and 2 Center for Bioenergy Innovation reference biomass samples (Poplar - Populus trichocarpa and Switchgrass - Panicum Virgatum) were converted into text files for plotting. Files contain 8192 points of raw spectral data from 12.23 to -2.78 ppm. The text file contains 4 columns of data and includes: Point number, Intensity, Hz, and ppm. Xcel Spreadsheet: HPLC measured monomeric sugar concentrations and bucketed 1H NMR data used to build monomeric sugar composition prediction models. Sugar composition in biomass determined from HPLC analyses are given in mg sugar/mg of biomass. Spectral bucketing was performed using Bruker’s AMIX software. Spectra were divided into 0.005 ppm buckets in the region of 3.10– 4.15 ppm for a total of 210 buckets. Headers for the bucketed data are the chemical shift in ppm of the center of the bucket. Bucketed data was used to build partial least squares models for subsequent predictions in The Unscrambler v. 10.5(CAMO A/S, Trondheim, Norway). The formation of methanol during hydrolysis interferes with the quantitative NMR analysis of sugars, so the methanol peak centered at 3.37 ppm and spanning four buckets (3.2925 – 3.2775 ppm) was set to zero for all spectra.
This project will conduct biomass deconstruction (pretreatment and enzymatic saccharification) on two representative biomass feedstocks and four different pretreatment processes, including three high temperature steam/chemical pretreatments and one low temperature chemical/mechanical pretreatment. A third biomass feedstock will undergo biomass deconstruction with three different pretreatment processes, including two high temperature chemical pretreatments and one low temperature chemical/mechanical pretreatment. Several pretreatment conditions will be performed in a screening study using NLR pilot-scale pretreatment equipment to generate a range of pretreated biomass substrates. A selected number of these substrates will be chosen for enzymatic saccharification evaluation, based on standard compositional analysis of the pretreated substrates as a primary indicator of pretreatment efficacy. Resulting enzymatic hydrolysis slurries will be analyzed to determine overall biomass sugar yields. Additional compositional analysis will be performed to determine oligomeric sugar composition and structure, to analyze structural characteristics of solids fractions on native, pretreated, and enzymatically saccharified biomass residues for one of the biomass feedstocks, corn stover. The enzymatically saccharified materials will undergo 2,3-butanediol fermentation in a shaker flask as bench scale. Using relevant process performance data collected in these various conversion steps, technoeconomic analysis activities will be performed to compare the economic potential of the various biomass feedstock and pretreatment processes and to identify key economic drivers and sustainability metrics.
The gas and tar composition of a diffusion flame from longleaf pine needles is currently poorly understood and more data are needed to fill in the gap between pyrolysis data and smoke plume data, thus improving physical and chemical modeling of wildland smoke formation. A pilot experiment to measure light gas and tar composition of such a flame is described for three flame regions: persistent flame (flame base), intermittent flame, and smoke plume. Flame gases from 24 experimental fires were collected in canisters and analyzed using EPA method TO-14A for CO 2 , CO, H 2 , CH 4 , and C 2 to C 7 hydrocarbon gases. Condensed gas (tar) samples were collected and analyzed using GC/MS. Other light gases were measured using FTIR spectroscopy. Results from compositional data analysis suggest significant differences in (relative) concentration of compounds detected in the three regions of the flame. Statistical tests for differences in flame zones were performed using the canister data: Concentration of hydrocarbons relative to CO and CO 2 decreased from the persistent flame zone above the pyrolyzing needles through the intermittent flame region into the flame-free plume. This was likely due to both chemical reactions (oxidation) occurring in the flame as well as the introduction of air into the flame/plume by entrainment.
Compositional characterization of biomass is vital for the biofuel industry. Traditional wet chemistry-based methods for analyzing biomass composition are laborious, time-consuming, and require extensive use of chemical reagents as well as highly skilled personnel. In this study, near-infrared (NIR) spectroscopy was used to quickly assess the composition of above-ground vegetative biomass from 113 diverse, photoperiod-sensitive, biomass-type sorghum (Sorghum bicolor) accessions cultivated under field conditions in Central Illinois. Biomass samples were analyzed using NIR spectra collected in the spectral range of 867–2536 nm, with their chemical compositions determined following the National Renewable Energy Laboratory (NREL) protocol. Advanced spectral pre-treatment and band selection techniques were utilized to develop calibration models using partial least squares regression (PLSR). The models’ effectiveness was assessed through cross-validation and independent data tests. The predictions for moisture, ash, extractives, glucan, xylan, acid-soluble lignin (ASL), acid-insoluble lignin (AIL), and total lignin were accurate and reliable, demonstrating the capability of NIR spectroscopy to provide rapid and precise characterization of sorghum biomass. The results demonstrated that NIR spectroscopy is an efficient tool for rapidly characterizing sorghum biomass, making it a sustainable option for screening desirable feedstock for biofuel or bioproduct production.
NIR spectroscopy is a rapid and accurate green technology for high-throughput biomass characterization, including sorghum (Sorghum bicolor), a promising energy crop for the biofuel industry. This study assessed the influence of particle size on NIR spectroscopic analysis (wavelength range: 867–2535 nm) of sorghum biomass composition. Grown under field conditions, a total of 113 types of genetically diverse sorghum accessions were dried, ground, and sieved (<250, 250–600, 600–850, and > 850 µm particle size) for developing partial least square regression (PLSR) prediction models for moisture, ash, extractive, glucan, xylan, acid-soluble lignin (ASL), acid-insoluble lignin (AIL), and total lignin (ASL + AIL). Overall, smaller particle sizes provided better model performance, while no single particle size provided the best performance for all the selected components. With only 9 selected bands and 4 latent variables (LVs), the best PLSR model was obtained for moisture with particle size of 600–850 µm with the square root of the coefficient of determination (R) of 0.85, the ratio of prediction to deviation (RPD) of 2.2, and the root mean square error (RMSE) of 0.46 % in external validation. Similar model performances were also obtained for ash, extractive, glucan, and xylan. This study showed that size reduction could effectively improve NIR spectroscopic analysis for lipid-producing sorghum biomass for the biofuel industry.
Current techno-economic analysis results indicate biomass feedstock cost represents 27% of the overall minimum fuel selling price for biofuels produced from fast pyrolysis followed by hydrotreating (hydro-deoxygenation, HDO). As a result, blended feedstocks have been proposed as a way to both reduce cost as well as tailor key chemistry for improved fuel quality. For this study, two feedstocks were provided by Idaho National Laboratory (INL). Both were pyrolyzed and collected under the same conditions in the National Renewable Energy Laboratory's (NREL) Thermochemical Process Development Unit (TCPDU). The resulting oil properties were then analyzed and characterized for statistical differences.
Biomass, a widely used renewable energy source, requires characterization to optimize biofuel and bioproduct processes, customize feedstocks, and ensure economic and environmental sustainability. Conventional wet-chemistry methods for biomass analysis are slow, expensive, and require significant reagents and skilled personnel. In contrast, near-infrared (NIR) spectroscopy, a faster, cost-effective, and reagent-free green technology, enables non-destructive biomass analysis with minimal sample preparation. This study provides an overview of the fundamentals of NIR spectroscopy and explores its recent applications for analyzing various biomass properties important to the biofuel and bioproduct industry. The study also critically evaluates the challenges and opportunities of using NIR spectroscopy for biomass analysis. This review aims to guide future research for rapid and high throughput characterization of biomass in the biomass industry, supporting the United Nations’ sustainable development goal (SDG) 7: producing affordable and sustainable energy.
Switchgrass (Panicum virgatum L.) biomass yield and feedstock quality improvement are priority research areas for bioenergy feedstock development. Identification of quantitative trait loci (QTL) underlying these traits and of trait-linked markers for application in marker-assisted selection (MAS) is of paramount importance in facilitating switchgrass breeding. Detection of QTL for biomass yield and plant height was conducted on parental linkage maps constructed using a heterozygous pseudo-F-1 population derived from a cross between lowland Alamo genotype AP13 and upland Summer genotype VS16. QTL analysis was performed with composite interval mapping. Four QTL for biomass yield and five QTL for plant height were identified using best linear unbiased predictors across ten and eight environments, respectively. The phenotypic variability explained (PVE) by QTL detected in the across environments analysis ranged from 4.9 to 12.4 % for biomass yield and 5.1 to 12.0 % for plant height. A total of 34 and 38 main effect QTL were detected for biomass yield and plant height, respectively, when data from each environment were analyzed separately. The PVE by individual environment QTL ranged from 3.3 to 15.3 % for biomass yield and from 4.3 to 17.4 % for plant height. In addition, 60 and 51 epistatic QTL were detected for biomass yield and plant height, respectively. Significant QTL by environment interactions were detected for QTL mapped in eight genomic regions for each of the two traits. Seven QTL affected both traits and may represent pleiotropic loci. Overall, 11 genomic regions were identified that were important in controlling biomass yield and/or plant height in switchgrass. Furthermore, the markers linked to the main effect and epistatic QTL may be used in MAS to maximize selection gain in switchgrass breeding, leading to a faster development of better biofuel cultivars.
Pretreatment is commonly used to reduce recalcitrance of the lignin-carbohydrate matrix. In this study, leading pretreatment technologies, including dilute sulfuric acid, liquid hot water, alkaline, and organosolv pretreatments, were applied to the selected Populus trichocarpa genotype with relatively low lignin content to elucidate cellulose physicochemical property changes and digestibility-related factors. Pretreated Populus trichocarpa (BESC 131) exhibited higher accessibility and glucose yield than the untreated biomass. Chemical composition and Fourier transform infrared (FTIR) analysis results revealed that hemicellulose and lignin were removed to a varying extent depending on the pretreatment techniques applied. Here, the degree of polymerization of the cellulose was decreased to the largest extent after dilute acid pretreatment, followed by organosolv, alkaline, and liquid hot water pretreatments. Cellulose crystallinity index was slightly changed after the pretreatments; however, its differences were not remarkable between those pretreatment techniques. Among four different pretreatments, organosolv was the most effective pretreatment technology in terms of sugar release, which was three times higher than that of the untreated native biomass. Among all of the tested cell wall traits, the lignin content of Populus trichocarpa was the most remarkable feature associated with glucose release, though Populus trichocarpa recalcitrance was not solely dependent on any single factor.