Engineering topics

Otto, William G.

Publications and source records attributed to Otto, William G..

6 records

DOE OSTI2024

Biomass Attributes and Attribute Modifications Affecting Systems and Methods to Separate and Fractionate

Chemical and physical heterogeneity in biomass feedstocks such as agricultural or forestry residues is due to substantial differences in plant tissue types. These differences can contribute significant challenges to handling, preprocessing, and conversion in biorefining processes. An understanding of this chemical and physical heterogeneity can be used to inform fractionation technologies that could facilitate more streamlined processing and potentially be employed to yield multiple co-product streams for a single feedstock. In this chapter, the motivation and scope of biomass fractionation is first outlined. Physical and chemical properties of biomass feedstocks, along with their distribution and diversity within plants, are next discussed with respect to how these differences can be exploited in a fractionation process. A summary of some of the key physical principles that allow for fractionation is next covered along with how these physical principles are exploited in equipment designs. Examples from the literature are briefly discussed that highlight how these approaches can be employed to achieve processing objectives. Several case studies on physical fractionation of corn stover and forestry residues are presented that illustrate how integrated fractionation processes could be employed. Lastly, prospects and potential economic drivers for adoption of biomass fractionation technologies are discussed.

09 BIOMASS FUELS↗

DOE OSTI2024

Biomass Attributes and Attribute Modifications Affecting Systems and Methods to Separate and Fractionate. In: Handbook of Biorefinery Research and Technology

Chemical and physical heterogeneity in biomass feedstocks such as agricultural or forestry residues is due to substantial differences in plant tissue types. These differences can contribute significant challenges to handling, preprocessing, and conversion in biorefining processes. An understanding of this chemical and physical heterogeneity can be used to inform fractionation technologies that could facilitate more streamlined processing and potentially be employed to yield multiple co-product streams for a single feedstock. In this chapter, the motivation and scope of biomass fractionation is first outlined. Physical and chemical properties of biomass feedstocks, along with their distribution and diversity within plants is next discussed with respect to how these differences can be exploited in a fractionation process. A summary of some of the key physical principles that allow for fractionation is next covered along with how these physical principles are exploited in equipment designs. Examples from the literature are briefly discussed that highlight how these approaches can be employed to achieve processing objectives. Several case studies on physical fractionation of corn stover and forestry residues are presented that illustrate how integrated fractionation processes could be employed. Finally, prospects and potential economic drivers for adoption of biomass fractionation technologies are discussed.

Biomass chemical properties↗

DOE OSTI2022

Particle classification by image analysis improves understanding of corn stover degradation mechanisms during deconstruction

Biomass feedstock heterogeneity is a principal roadblock to implementation of the biorefinery concept. Even within an identical cultivar of corn stover, different bales contain not only varying abundance moisture, ash, glucan, and other chemical compounds, but also varying abundance of tissue anatomies (e.g., leaf, husk, cob, or stalk). These different anatomical components not only differ in their response to pretreatment and enzymatic hydrolysis to glucose, but also vary in their mechanical and conveyance properties. Although this heterogeneous nature of corn stover feedstock has been identified as a challenge, a fundamental knowledge gap of how these tissues behave during biorefining processing remains. Here, we demonstrate the use of a commercial fiber image analyzer typically used for wood fiber characterization to monitor the particle size and shapes of non-woody feedstock during milling, pretreatment, and hydrolysis. Additionally, we present novel use of Gaussian process classification to distinguish bundle, parenchyma, and fiber particles to an accuracy of 96.4%. Quantitative probability distribution plots for characteristics such as length and roundness allow elucidation of particle morphology as pretreatment and enzymatic hydrolysis progress. In both stalk pith and stalk rind, particles peel into individual cells whose walls are subsequently fragmented during enzymatic hydrolysis.

09 BIOMASS FUELS↗

DOE OSTI2022

Predictive models enhance feedstock quality of corn stover via air classification

Feedstock heterogeneity is a fundamental obstacle to cost-competitive biobased products. Agricultural products like corn stover have anatomical components that vary in their chemical composition, mechanical properties, structure, and response to chemical and biological treatments. A technique that can enrich streams in select anatomical fractions would allow a tailored deconstruction approach to increase overall process efficiency. Air classification can be leveraged for such refining, however, fundamental characterization and understanding of the particle properties that underly the physics of air classification are only modestly documented. Here, we determine fundamental particle properties including mass-to-area ratio, drag coefficient, and partition velocity that describe how anatomical tissues of corn stover behave during air classification. In this work, mass-to-area ratios of anatomical tissues vary by nearly two orders of magnitude from 2.3 mg/mm 2 for cob to 0.04 mg/mm 2 for leaf. Drag coefficients of longer, fibrous materials (i.e., rind, husk, and sheath) are shown to correlate with particle area (p-value < 0.001) whereas granular tissues (i.e., cob, pith, and leaf) correlate better with mass-to-area ratio (p-values < 0.001). When compared to experimental observations, a simulated two-stage air classification and size reduction scenario predicts the overall partitioning of anatomical tissues within 15% for pith, husk, rind, and cob tissues. The model predicts an air-classified fraction preferentially enriched in cob (purity = 20%), rind (purity = 74%), and pith (purity = 4.5%) with a mass yield of 47%. Empirical relations for these properties can be used to predict the partitioning of corn stover during air classification based on anatomical type and size.

09 BIOMASS FUELS↗

DOE OSTI2022

Development of an ammonia pretreatment that creates synergies between biorefineries and advanced biomass logistics models

In this study, a novel ammonia-based pretreatment for densified lignocellulosic biomass was developed to reduce ammonia usage and integrate with viable biomass logistics scenarios. The COmpacted Biomass with Recycled Ammonia (COBRA) pretreatment performed at 100 °C allows >95% conversion of sugarcane bagasse (SCB) carbohydrates into soluble monomeric and oligomeric sugars (glucose and xylose) using industrially relevant 6% glucan loading (~21% solids loading) enzymatic hydrolysis conditions at reduced enzyme loadings. Pretreatment via COBRA with simultaneous lignin extraction (COBRA-LE) improved Saccharomyces cerevisiae 424A(LNH-ST) metabolic yield from 89% to 97.5% relative to COBRA without delignification, allowing a process ethanol yield of 71.6%. A technoeconomic analysis on SCB biorefining to ethanol in the state of São Paulo, Brazil, compared COBRA to other mature technologies, such as AFEX and steam-explosion. Amongst all scenarios studied, biorefineries based on COBRA-LE pretreatment offered the lowest average minimum ethanol selling price of US$1.45 per gallon ethanol. COBRA pretreatment was subsequently tested on perennial grasses and hardwoods, and >80% total sugar yields were achieved for all cases.

09 BIOMASS FUELS↗

DOE OSTI2022

Near-Infrared Spectroscopy can Predict Anatomical Abundance in Corn Stover

Feedstock heterogeneity is a key challenge impacting the deconstruction and conversion of herbaceous lignocellulosic biomass to biobased fuels, chemicals, and materials. Upstream processing to homogenize biomass feedstock streams into their anatomical components via air classification allows for a more tailored approach to subsequent mechanical and chemical processing. Here, we show that differing corn stover anatomical tissues respond differently to pretreatment and enzymatic hydrolysis and therefore, a one-size-fits-all approach to chemical processing biomass is inappropriate. To inform on-line downstream processing, a robust and high-throughput analytical technique is needed to quantitatively characterize the separated biomass. Predictive correlation of near-infrared spectra to biomass chemical composition is such a technique. Here, we demonstrate the capability of models developed using an “off-the-shelf,” industrially relevant spectrometer with limited spectral range to make strong predictions of both cell wall chemical composition and the relative abundance of anatomical components of the corn stover, the latter for the first time ever. Gaussian process regression (GPR) yields stronger correlations (average R 2 v = 88% for chemical composition and 95% for anatomical relative abundance) than the more commonly used partial least squares (PLS) regression (average R 2 v = 84% for chemical composition and 92% for anatomical relative abundance). In nearly all cases, both GPR and PLS outperform models generated using neural networks. These results highlight the potential for coupling NIRS with predictive models based on GPR due to the potential to yield more robust correlations.

09 BIOMASS FUELS↗