The Feedstock-Conversion Interface Consortium (FCIC) develops first-principles-based knowledge an... more The Feedstock-Conversion Interface Consortium (FCIC) develops first-principles-based knowledge and tools to understand, quantify, and mitigate the effects of feedstock and process variability across the bioenergy value chain, from the field and forest through downstream conversion. The FCIC is a collaborative and coordinated effort involving researchers in many different disciplines.
Inorganic compounds in biomass, often referred to as ash, are known to be problematic in the ther... more Inorganic compounds in biomass, often referred to as ash, are known to be problematic in the thermochemical conversion of biomass to bio-oil or syngas and, ultimately, hydrocarbon fuels because they negatively influence reaction pathways, contribute to fouling and corrosion, poison catalysts, and impact waste streams. The most common ash-analysis methods, such as inductively coupled plasma-optical emission spectrometry/mass spectrometry (ICP-OES/MS), require considerable time and expensive reagents. Laser-induced breakdown spectroscopy (LIBS) is emerging as a technique for rapid analysis of the inorganic constituents in a wide range of biomass materials. This study compares analytical results using LIBS data to results obtained from three separate ICP-OES/MS methods for 12 samples, including six standard reference materials. Analyzed elements include aluminum, calcium, iron, magnesium, manganese, phosphorus, potassium, sodium, and silicon, and results show that concentrations can be measured with an uncertainty of approximately 100 parts per million using univariate calibration models and relatively few calibration samples. These results indicate that the accuracy of LIBS is comparable to that of ICP-OES methods and indicate that some acid-digestion methods for ICP-OES may not be reliable for Na and Al. These results also demonstrate that germanium can be used as an internal standard to improve the reliability and accuracy of measuring many elements of interest, and that LIBS can be used for rapid determination of total ash in biomass samples. Key benefits of LIBS include little sample preparation, no reagent consumption, and the generation of meaningful analytical data instantaneously.
Developing spectroscopic calibration models requires calibration samples that mimic as much as po... more Developing spectroscopic calibration models requires calibration samples that mimic as much as possible new sample com-positions as well as measurement conditions. This requirement is known as matrix matching calibration samples to new samples, i.e., sample matrix matched chemically, physically, and instrumentally. To accomplish this task, calibration sets have large sample numbers to span the expected sample matrix variations. This large range of calibration variability can result in poor performance. Preferred is a calibration set distinctly matched to the new samples. However, assessing whether each sample in a particular calibration set is appropriately matched to new samples relative to the specific analyte content and all other constituents is not an easy task. It is well documented that even though calibration samples are spectral matches to new sample spectra (have similar measured spectra), the calibration set is usually not fully matrix matched to new sample compositions. For example, using a spectral similarity measure such as Euclidean distance, the same calibration samples are deemed spectral matches to new samples regardless of the analyte of interest. This work presents a process to assess underlying sample matrix interactions between calibration model regression vectors and new sample spectra allowing fully matrix matched samples to be identified. The process is general and applicable to other situations such as matching historical batch processing data where references values are not known for new samples (unlabeled). Two data sets are used to demonstrate the functionality of the process. One consists of nuclear magnetic resonance spectra for mixtures of three alcohols and the other is near infrared corn spectra with four prediction properties measured on three instruments. General trends are reported for a few of the possible data situations. Calibration samples identified as matrix matched to new samples are shown to predict the new samples with lowest prediction errors.
The Feedstock-Conversion Interface Consortium (FCIC) develops first-principles-based knowledge an... more The Feedstock-Conversion Interface Consortium (FCIC) develops first-principles-based knowledge and tools to understand, quantify, and mitigate the effects of feedstock and process variability across the bioenergy value chain, from the field and forest through downstream conversion. The FCIC is a collaborative and coordinated effort involving researchers in many different disciplines.
Biofuels made from biomass and waste residues will largely contribute to United States' 2050 deca... more Biofuels made from biomass and waste residues will largely contribute to United States' 2050 decarbonization goal in the aviation sector. While cellulosic biofuels have the potential fuel performance equivalent to petroleum-based jet fuel, the biofuel industry needs to overcome the supply chain barrier caused by temporal and spatial variability of biomass yield and quality. This study highlights the importance of incorporating spatial and temporal variability during biomass supply chain planning via optimization modeling that incorporates 10 years of drought index data, a primary factor contributing to yield and quality variability. The results imply that the cost of delivering biomass to biorefinery may be significantly underestimated if the multi-year temporal and spatial variation in biomass yield and quality is not captured. For long term sustainable biorefinery operations, the industry should optimize supply chain strategy by studying the variability of yield and quality of biomass in their supply sheds.
Switchgrass (Panicum virgatum L.) is a promising feedstock for bioenergy and bioproducts; however... more Switchgrass (Panicum virgatum L.) is a promising feedstock for bioenergy and bioproducts; however, its inherent variability in chemical attributes creates challenges for uniform conversion efficiencies and product quality. It is necessary to understand the range of variation and factors (i.e., field management, environmental) influencing chemical attributes for process improvement and risk assessment. The objectives of this study were to (1) examine the impact of nitrogen fertilizer application rate, year, and location on switchgrass chemical attributes, (2) examine the relationships among chemical attributes, weather and soil data, and (3) develop models to predict chemical attributes using environmental factors. Switchgrass samples from a field study spanning four locations including upland cultivars, one location including a lowland cultivar, and between three and six harvest years were assessed for glucan, xylan, lignin, volatiles, carbon, nitrogen, and ash concentrations. Using variance estimation, location/cultivar, nitrogen application rate, and year explained 65%–96% of the variation for switchgrass chemical attributes. Location/cultivar × year interaction was a significant factor for all chemical attributes indicating environmental‐based influences. Nitrogen rate was less influential. Production variables and environmental conditions occurring during the switchgrass field trials were used to successfully predict chemical attributes using linear regression models. Upland switchgrass results highlight the complexity in plant responses to growing conditions because all production and environmental variables had strong relationships with one or more chemical attributes. Lowland switchgrass was limited to observations of year‐to‐year environmental variability and nitrogen application rate. All explanatory variable categories were important for lowland switchgrass models but stand age and precipitation relationships were particularly strong. The relationships found in this study can be used to understand spatial and temporal variation in switchgrass chemical attributes. The ability to predict chemical attributes critical for conversion processes in a geospatial/temporal manner would provide state‐of‐the‐art knowledge for risk assessment in the bioenergy and bioproducts industry.
This report contains business sensitive and/or potential intellectual property information. Writt... more This report contains business sensitive and/or potential intellectual property information. Written permission from the author must be obtained prior to distribution beyond Laboratory and DOE staff. This milestone report will be converted into a publicly available Laboratory technical report.
The Bioenergy Feedstock Library (BFL), part of the Biomass Feedstock National User Facility (BFNU... more The Bioenergy Feedstock Library (BFL), part of the Biomass Feedstock National User Facility (BFNUF) located at INL, is a physical sample repository and a web-accessible electronic database. The BFL stores physical and chemical characteristics of biomass and waste carbon sources for energy use as well as samples generated from across U.S. Department of Energy (DOE) Bioenergy Technologies Office (BETO) funded projects. The objective of this Bioenergy Feedstock Library Annual Summary Report for 2022 is to focus on the (1) publicly available analytical data and equipment tracked through the BFNUF, (2) physical samples available for request, (3) sample and data archival progress from recent BETO-funded projects, and (4) publicly available data sets created upon request from BETO, INL projects, or outside entities. This report highlights key statistics from FY22 and available data and information important for INL, BFL users, academics, and industry. Some key highlights from this report include: • The BFL currently tracks over 100,000 unique samples each with its own barcode; over 50,000 of these samples and associated data have been made publicly available. • The BFL hosts over 150 unique feedstock types and over 400 unique subtypes (e.g., cultivars) for publicly available samples.
Perennial grass mixtures established on Conservation Reserve Program (CRP) lands can be an import... more Perennial grass mixtures established on Conservation Reserve Program (CRP) lands can be an important source of feedstock for bioenergy production. This study aimed to evaluate management practices for optimizing the quality of bioenergy feedstock and stand persistence of grass‐legume mixtures under diverse environments. A 5‐year field study (2008–2012) was conducted to assess the effects of two harvest timings (at anthesis vs after complete senescence) and three nitrogen (N) rates (0, 56, 112 kg N ha−1) on biomass chemical compositions (i.e., cell wall components, ash, volatiles, total carbon, and N contents) and the feedstock energy potential, examined by the theoretical ethanol yield (TEY) and the total TEY (i.e., the product of biomass yield and TEY, L ha−1), of cool‐season mixtures in Georgia and Missouri and a warm‐season mixture in Kansas. The canonical correlation analysis (CCA) was used to investigate the effect of vegetative species transitions on feedstock quality. Althoug...
Switchgrass (Panicum virgatum L.) is a promising feedstock for bioenergy and bioproducts; however... more Switchgrass (Panicum virgatum L.) is a promising feedstock for bioenergy and bioproducts; however, its inherent variability in chemical attributes creates challenges for uniform conversion efficiencies and product quality. It is necessary to understand the range of variation and factors (i.e., field management, environmental) influencing chemical attributes for process improvement and risk assessment. The objectives of this study were to (1) examine the impact of nitrogen fertilizer application rate, year, and location on switchgrass chemical attributes, (2) examine the relationships among chemical attributes, weather and soil data, and (3) develop models to predict chemical attributes using environmental factors. Switchgrass samples from a field study spanning four locations including upland cultivars, one location including a lowland cultivar, and between three and six harvest years were assessed for glucan, xylan, lignin, volatiles, carbon, nitrogen, and ash concentrations. Using...
In the United States, corn (Zea mays L.) stover has been targeted for second generation fuel prod... more In the United States, corn (Zea mays L.) stover has been targeted for second generation fuel production and other bio-products. Our objective was to characterize sugar and structural composition as a function of vertical distribution of corn stover (leaves and stalk) that was sampled at physiological maturity and about three weeks later from multiple USA locations. A small subset of samples was assessed for thermochemical composition. Concentrations of lignin, glucan, and xylan were about 10% greater at grain harvest than at physiological maturity, but harvestable biomass was about 25% less due to stalk breakage. Gross heating density above the ear averaged 16.3 ± 0.40 MJ kg −1 , but with an alkalinity measure of 0.83 g MJ −1 , slagging is likely to occur during gasification. Assuming a stover harvest height of 10 cm, the estimated ethanol yield would be >2500 L ha −1 , but it would be only 1000 L ha −1 if stover harvest was restricted to the material from above the primary ear. Vertical composition of corn stover is relatively uniform; thus, decision on cutting height may be driven by agronomic, economic and environmental considerations.
The Feedstock-Conversion Interface Consortium (FCIC) develops first-principles-based knowledge an... more The Feedstock-Conversion Interface Consortium (FCIC) develops first-principles-based knowledge and tools to understand, quantify, and mitigate the effects of feedstock and process variability across the bioenergy value chain, from the field and forest through downstream conversion. The FCIC is a collaborative and coordinated effort involving researchers in many different disciplines.
Inorganic compounds in biomass, often referred to as ash, are known to be problematic in the ther... more Inorganic compounds in biomass, often referred to as ash, are known to be problematic in the thermochemical conversion of biomass to bio-oil or syngas and, ultimately, hydrocarbon fuels because they negatively influence reaction pathways, contribute to fouling and corrosion, poison catalysts, and impact waste streams. The most common ash-analysis methods, such as inductively coupled plasma-optical emission spectrometry/mass spectrometry (ICP-OES/MS), require considerable time and expensive reagents. Laser-induced breakdown spectroscopy (LIBS) is emerging as a technique for rapid analysis of the inorganic constituents in a wide range of biomass materials. This study compares analytical results using LIBS data to results obtained from three separate ICP-OES/MS methods for 12 samples, including six standard reference materials. Analyzed elements include aluminum, calcium, iron, magnesium, manganese, phosphorus, potassium, sodium, and silicon, and results show that concentrations can be measured with an uncertainty of approximately 100 parts per million using univariate calibration models and relatively few calibration samples. These results indicate that the accuracy of LIBS is comparable to that of ICP-OES methods and indicate that some acid-digestion methods for ICP-OES may not be reliable for Na and Al. These results also demonstrate that germanium can be used as an internal standard to improve the reliability and accuracy of measuring many elements of interest, and that LIBS can be used for rapid determination of total ash in biomass samples. Key benefits of LIBS include little sample preparation, no reagent consumption, and the generation of meaningful analytical data instantaneously.
Developing spectroscopic calibration models requires calibration samples that mimic as much as po... more Developing spectroscopic calibration models requires calibration samples that mimic as much as possible new sample com-positions as well as measurement conditions. This requirement is known as matrix matching calibration samples to new samples, i.e., sample matrix matched chemically, physically, and instrumentally. To accomplish this task, calibration sets have large sample numbers to span the expected sample matrix variations. This large range of calibration variability can result in poor performance. Preferred is a calibration set distinctly matched to the new samples. However, assessing whether each sample in a particular calibration set is appropriately matched to new samples relative to the specific analyte content and all other constituents is not an easy task. It is well documented that even though calibration samples are spectral matches to new sample spectra (have similar measured spectra), the calibration set is usually not fully matrix matched to new sample compositions. For example, using a spectral similarity measure such as Euclidean distance, the same calibration samples are deemed spectral matches to new samples regardless of the analyte of interest. This work presents a process to assess underlying sample matrix interactions between calibration model regression vectors and new sample spectra allowing fully matrix matched samples to be identified. The process is general and applicable to other situations such as matching historical batch processing data where references values are not known for new samples (unlabeled). Two data sets are used to demonstrate the functionality of the process. One consists of nuclear magnetic resonance spectra for mixtures of three alcohols and the other is near infrared corn spectra with four prediction properties measured on three instruments. General trends are reported for a few of the possible data situations. Calibration samples identified as matrix matched to new samples are shown to predict the new samples with lowest prediction errors.
The Feedstock-Conversion Interface Consortium (FCIC) develops first-principles-based knowledge an... more The Feedstock-Conversion Interface Consortium (FCIC) develops first-principles-based knowledge and tools to understand, quantify, and mitigate the effects of feedstock and process variability across the bioenergy value chain, from the field and forest through downstream conversion. The FCIC is a collaborative and coordinated effort involving researchers in many different disciplines.
Biofuels made from biomass and waste residues will largely contribute to United States' 2050 deca... more Biofuels made from biomass and waste residues will largely contribute to United States' 2050 decarbonization goal in the aviation sector. While cellulosic biofuels have the potential fuel performance equivalent to petroleum-based jet fuel, the biofuel industry needs to overcome the supply chain barrier caused by temporal and spatial variability of biomass yield and quality. This study highlights the importance of incorporating spatial and temporal variability during biomass supply chain planning via optimization modeling that incorporates 10 years of drought index data, a primary factor contributing to yield and quality variability. The results imply that the cost of delivering biomass to biorefinery may be significantly underestimated if the multi-year temporal and spatial variation in biomass yield and quality is not captured. For long term sustainable biorefinery operations, the industry should optimize supply chain strategy by studying the variability of yield and quality of biomass in their supply sheds.
Switchgrass (Panicum virgatum L.) is a promising feedstock for bioenergy and bioproducts; however... more Switchgrass (Panicum virgatum L.) is a promising feedstock for bioenergy and bioproducts; however, its inherent variability in chemical attributes creates challenges for uniform conversion efficiencies and product quality. It is necessary to understand the range of variation and factors (i.e., field management, environmental) influencing chemical attributes for process improvement and risk assessment. The objectives of this study were to (1) examine the impact of nitrogen fertilizer application rate, year, and location on switchgrass chemical attributes, (2) examine the relationships among chemical attributes, weather and soil data, and (3) develop models to predict chemical attributes using environmental factors. Switchgrass samples from a field study spanning four locations including upland cultivars, one location including a lowland cultivar, and between three and six harvest years were assessed for glucan, xylan, lignin, volatiles, carbon, nitrogen, and ash concentrations. Using variance estimation, location/cultivar, nitrogen application rate, and year explained 65%–96% of the variation for switchgrass chemical attributes. Location/cultivar × year interaction was a significant factor for all chemical attributes indicating environmental‐based influences. Nitrogen rate was less influential. Production variables and environmental conditions occurring during the switchgrass field trials were used to successfully predict chemical attributes using linear regression models. Upland switchgrass results highlight the complexity in plant responses to growing conditions because all production and environmental variables had strong relationships with one or more chemical attributes. Lowland switchgrass was limited to observations of year‐to‐year environmental variability and nitrogen application rate. All explanatory variable categories were important for lowland switchgrass models but stand age and precipitation relationships were particularly strong. The relationships found in this study can be used to understand spatial and temporal variation in switchgrass chemical attributes. The ability to predict chemical attributes critical for conversion processes in a geospatial/temporal manner would provide state‐of‐the‐art knowledge for risk assessment in the bioenergy and bioproducts industry.
This report contains business sensitive and/or potential intellectual property information. Writt... more This report contains business sensitive and/or potential intellectual property information. Written permission from the author must be obtained prior to distribution beyond Laboratory and DOE staff. This milestone report will be converted into a publicly available Laboratory technical report.
The Bioenergy Feedstock Library (BFL), part of the Biomass Feedstock National User Facility (BFNU... more The Bioenergy Feedstock Library (BFL), part of the Biomass Feedstock National User Facility (BFNUF) located at INL, is a physical sample repository and a web-accessible electronic database. The BFL stores physical and chemical characteristics of biomass and waste carbon sources for energy use as well as samples generated from across U.S. Department of Energy (DOE) Bioenergy Technologies Office (BETO) funded projects. The objective of this Bioenergy Feedstock Library Annual Summary Report for 2022 is to focus on the (1) publicly available analytical data and equipment tracked through the BFNUF, (2) physical samples available for request, (3) sample and data archival progress from recent BETO-funded projects, and (4) publicly available data sets created upon request from BETO, INL projects, or outside entities. This report highlights key statistics from FY22 and available data and information important for INL, BFL users, academics, and industry. Some key highlights from this report include: • The BFL currently tracks over 100,000 unique samples each with its own barcode; over 50,000 of these samples and associated data have been made publicly available. • The BFL hosts over 150 unique feedstock types and over 400 unique subtypes (e.g., cultivars) for publicly available samples.
Perennial grass mixtures established on Conservation Reserve Program (CRP) lands can be an import... more Perennial grass mixtures established on Conservation Reserve Program (CRP) lands can be an important source of feedstock for bioenergy production. This study aimed to evaluate management practices for optimizing the quality of bioenergy feedstock and stand persistence of grass‐legume mixtures under diverse environments. A 5‐year field study (2008–2012) was conducted to assess the effects of two harvest timings (at anthesis vs after complete senescence) and three nitrogen (N) rates (0, 56, 112 kg N ha−1) on biomass chemical compositions (i.e., cell wall components, ash, volatiles, total carbon, and N contents) and the feedstock energy potential, examined by the theoretical ethanol yield (TEY) and the total TEY (i.e., the product of biomass yield and TEY, L ha−1), of cool‐season mixtures in Georgia and Missouri and a warm‐season mixture in Kansas. The canonical correlation analysis (CCA) was used to investigate the effect of vegetative species transitions on feedstock quality. Althoug...
Switchgrass (Panicum virgatum L.) is a promising feedstock for bioenergy and bioproducts; however... more Switchgrass (Panicum virgatum L.) is a promising feedstock for bioenergy and bioproducts; however, its inherent variability in chemical attributes creates challenges for uniform conversion efficiencies and product quality. It is necessary to understand the range of variation and factors (i.e., field management, environmental) influencing chemical attributes for process improvement and risk assessment. The objectives of this study were to (1) examine the impact of nitrogen fertilizer application rate, year, and location on switchgrass chemical attributes, (2) examine the relationships among chemical attributes, weather and soil data, and (3) develop models to predict chemical attributes using environmental factors. Switchgrass samples from a field study spanning four locations including upland cultivars, one location including a lowland cultivar, and between three and six harvest years were assessed for glucan, xylan, lignin, volatiles, carbon, nitrogen, and ash concentrations. Using...
In the United States, corn (Zea mays L.) stover has been targeted for second generation fuel prod... more In the United States, corn (Zea mays L.) stover has been targeted for second generation fuel production and other bio-products. Our objective was to characterize sugar and structural composition as a function of vertical distribution of corn stover (leaves and stalk) that was sampled at physiological maturity and about three weeks later from multiple USA locations. A small subset of samples was assessed for thermochemical composition. Concentrations of lignin, glucan, and xylan were about 10% greater at grain harvest than at physiological maturity, but harvestable biomass was about 25% less due to stalk breakage. Gross heating density above the ear averaged 16.3 ± 0.40 MJ kg −1 , but with an alkalinity measure of 0.83 g MJ −1 , slagging is likely to occur during gasification. Assuming a stover harvest height of 10 cm, the estimated ethanol yield would be >2500 L ha −1 , but it would be only 1000 L ha −1 if stover harvest was restricted to the material from above the primary ear. Vertical composition of corn stover is relatively uniform; thus, decision on cutting height may be driven by agronomic, economic and environmental considerations.
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