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Addressing the challenge of biomass feedstock variability that affects the optimization of processing and conversion processes, the project's goal is to maximize biorefinery economics by improving the process quality control of feedstock, which will lead to greater plant availability and predictable yields of high value biofuels and co-products.
Abstract A common and costly challenge in the nascent biorefinery industry is the consistent handling and conveyance of biomass feedstock materials, which can vary widely in their chemical, physical, and mechanical properties. Solutions to cope with varying feedstock qualities will be required, including advanced process controls to adjust equipment and reject feedstocks that do not meet a quality standard. In this work, we present and evaluate methods to autonomously assess corn stover feedstock quality in real time and provide data to process controls with low-cost camera hardware. We explore the use of neural networks to classify feedstocks based on actual processing behavior and pixel matrix feature parameterization to further assess particle attributes that may explain the variable processing behavior. We used the pretrained ResNet neural network coupled with a gated recurrent unit (GRU) time-series classifier trained on our image data, resulting in binary classification of feedstock anomalies with favorable performance. The textural aspects of the image data were statistically analyzed to determine if the textural features were predictive of operational disruptions. The significant textural features were angular second moment, prominence, mean height of surface profile, mean resultant vector, shade, skewness, variation of the polar facet orientation, and direction of azimuthal facets. Expansion of these models is recommended across a wider variety of labeled feedstock images of different qualities and species to develop a more robust tool that may be deployed using low-cost cameras within biorefineries.
Planning of biorefinery operations is complicated by the stochastic nature of physical and chemical characteristics of biomass feedstock, such as, moisture level and carbohydrate content. Biomass characteristics affect the performance of the equipment which feed the reactor and the efficiency of the conversion process in a biorefinery. We propose a stochastic optimization model to identify a blend of feedstocks, inventory levels, and operating conditions of equipment to ensure a continuous flowing of biomass to the reactor while meeting the requirements of the biochemical conversion process. We propose a sample average approximation (SAA) of the model, and develop an efficient algorithm to solve the SAA model. A feedstock preprocessing process consists of two-stage grinding and pelleting is used to develop a case study. Extensive numerical analysis are conducted which lead to a number of observations. Our main observation is that sequencing bales based on moisture level and carbohydrate content leads to robust solutions that improve processing time and processing rate of the reactor. We provide a number of managerial insights that facilitate the implementation of the model proposed. Note to Practitioners—This paper is motivated by the challenges faced in the bioenergy industry. The focus of this paper is on plants which use the biochemical conversion process to generate liquid fuels. It has been observed that variations in biomass characteristics, such as moisture content, cause variations in feeding of the system which lead to under-utilization of equipment. A requirement of biochemical conversion process is to maintain the carbohydrate content of biomass processed by the reactor, larger than a threshold. We propose a model that identifies the inventory levels and operating conditions of equipment to ensure a continuous flowing of biomass to the reactor. The goal is to improve equipment utilization while satisfying the requirements of the conversion process. The model is tested using real-life data. We found out that by sequencing bales based on moisture level and carbohydrate content, a plant can reduce variability in the system leading to improved system reliability, higher processing rates of the reactor, and higher throughput.
The impacts of feedstock variability on pyrolysis processes are significant but poorly defined. Current engineering designs are based on empirical guidelines, useful only over a narrow range of feedstock properties. The objectives of this project are to (1) Develop science-based knowledge of how feedstock attributes and operational parameters impact pyrolysis process reliability and product quality; and (2) Build an experimental and computational toolset that predicts these outcomes, enabling processes to optimize reliability and product quality. Biomass is a complex feedstock. Controlling for and testing the effects of individual attributes is very challenging. This project couples multiscale experimentation and modeling to accurately capture the fundamental physics and chemistry of biomass flow and conversion behavior in feeding and pyrolysis reactor operations. Our focus is on pine residue attributes – anatomical fraction (bark, needles, wood), particle morphology (size/shape distribution, density, porosity), and chemical composition (extractives, biopolymers, alkali metals) – that impact product quality for downstream catalytic upgrading. Because detailed pyrolysis product characterization is limited, cutting-edge analytical techniques are being developed to reveal impactful product attributes. The tools and knowledge developed here will enable integrated pyrolysis-based processes that are more robust, flexible, and market-responsive with respect to feedstock variability.
Feedstock design is crucial for lignocellulosic biomass use. Current strategies for feedstock design cannot be readily applied to improve the quality of biomass-based materials, limiting the sustainability and economics of lignocellulosic biorefineries. Recent studies have advanced the understanding of biomass structure–property relationships and discovered several characteristics, such as molecular weight, uniformity, linkage profile, and functional groups, that are critical for manufacturing diverse quality biomaterials. Further, these discoveries call for fundamentally different strategies for feedstock development. Such strategies need to rediscover the roles of monolignol biosynthesis enzymes and leverage lignin polymerization enzymes to achieve precise control of lignin molecular structure. These innovations could transform biomass into feedstock for high-quality biomaterials, addressing essential environmental challenges and empowering the bioeconomy.
Formulation control is key to achieving consistent target properties of energetic materials, as feedstock variations and slight deviations in the ratios of different ingredients can have major effects on final product properties, particularly in dense pastes with high particle loading >65 vol.%. In large‐scale operations, it is imperative to either correct or remove batches of material that perform outside baseline property specifications as early as possible to avoid unnecessary processing of suboptimal material. Quality monitoring is the practice of measuring material properties during processing using process analytical technologies as opposed to only testing the properties of the final product; it is a key principle in the quality‐by‐design frameworks used for designing formulations and manufacturing processes. Herein, a process analytical technology method for correlating material properties of dense pastes directly after mixing in a Resonant Acoustic Mixer to motor data is developed and used to detect differences in the particle content of dense paste formulations. This method was also capable of detecting variations in powder feedstock properties, such as particle packing efficiency, and is sensitive enough to detect changes of 2 wt.% in the total solids content of the formulation. The techniques presented herein show excellent promise for use as a process analytical technology capable of quantifying formulation effects on material movement modes during resonant acoustic mixing.
Abstract Manufacturing plastic parts with secondary feedstocks has risen to the forefront of importance in recent years. However, the variation in molecular weight and rheology of secondary feedstock can lead to inconsistent part quality. This work evaluates the effectiveness of a novel closed‐loop adaptive process control system that adjusts nozzle pressure in response to in‐mold pressure data. Five different recycled polypropylene blends, with a broad distribution of flow properties, were evaluated to determine the effectiveness of the control system at reducing processing variation. The experimental results show that the process control strategy reduced the variation within the mold, as seen by in‐mold pressure curves and calculated in‐mold viscosity values. Additionally, the parameters that control the automated process adjustments were investigated, showing the importance of optimization. The analysis of the correlation between in‐mold rheology and mechanical properties showed a slight variation in the mechanical properties and parts weight with a coefficient of variation of under 5%. Overall, the results demonstrate the ability of pressure‐controlled molding and automated viscosity adjustment to reduce the variability when molding a secondary feedstock. Highlights Pressure‐controlled injection molding of recycled polypropylene. Automated closed‐loop adaptive process control methodology. Methodology resulted in a reduction in pressure variation during molding. Changes in mechanical properties and in‐mold viscosity were investigated. Results show the potential of pressure‐controlled molding at reducing variation.
The ability to additively-manufacture mechanically responsive molecules, known as mechanophores (MPs), that are incorporated into polymer feedstocks provides opportunities for self-healing, real-time damage detection, and improvements in quality assurance and control capabilities to several industries (wind energy technology, building and construction, etc.) who are adopting additive manufacturing (AM). However, before the applications are realized and industrially adopted, further research and development regarding MP-incorporated AM feedstock availability, production scale-up, and processability and printability is needed. Here, the goal of this review is to bridge the gap between the bench top and real world applications of AM of MPs by identifying high impact application spaces and highlighting the challenges that need to be overcome for widespread adoption. The state-of-the-art of AM of MP-incorporated feedstocks is reviewed, followed by a discussion of potential future applications, current challenges, and research areas that work toward commercialization of AM of MP-incorporated feedstocks.
Manufacturing with secondary feedstock has been identified as an effective strategy to improve plastic circularity. However, product quality inconsistencies arise due to variations in molecular weight, rheology, and mechanical properties. This work evaluates the processing of recycled high-density polyethylene using pressure-controlled injection molding with a focus on processing behavior and energy consumption. Further, the effects of injection velocity, packing pressure, and transfer position are benchmarked against a conventional velocity-controlled process. The experimental results show that the novel process control strategy significantly affects the mechanical properties, in-mold rheology, and energy consumption. Parts fabricated using pressure-controlled injection molding showed higher tensile properties due to increased macromolecular orientation. Additionally, reduced energy was used due to lower melt pressures required to completely replicate the cavity geometry. The results demonstrate the potential of the technology to support increased utilization of secondary feedstock and reduced carbon footprint.
Selective laser melting (SLM) is a powder-bed fusion process in Additive Manufacturing (AM) that uses a laser beam to selectively fuse layers of powder into near net-shape components with little porosity. However, inconsistencies in the part properties due to the presence of defects in as-built components has hindered the widespread adoption of SLM for industrial applications forcing researchers to study the sources of variation for quality control purposes. A critical area suspected of creating variation in the part properties is the feedstock, where batch-to-batch differences as well as changes in the powder properties with reuse have the potential to affect performance. During processing, laser spatter and condensate eject from the melt pool and deposit into the powder-bed surrounding the parts. These particulates, more commonly known as ejecta, differ morphologically and chemically from the virgin powder compromising its reusability. In this study, 304L stainless steel powder was recycled for a total of 5 times through a systematic approach aimed at accelerating powder degradation to reveal the influence on both the tensile and impact toughness properties. Through analysis of variance (ANOVA), it was found that tensile properties did not change with reuse, while the impact toughness showed a steady decline illustrating the differences in static and dynamic part properties due to powder recycling.
Although selective laser sintering is considered a major player in the additive manufacturing community, significant limitations exist when it comes to processing the polymeric powder feedstocks in the laser sintering machine. While these limitations – such as inadequate and uneven heating and complex thermal phenomena leading to curling and shrinkage – cannot be ignored and are being addressed in the community, it is also vitally important to turn our attention to the expansion of commercially available powder feedstocks. A major drawback of SLS is the lack of available feedstocks. At Los Alamos National Laboratory, a primary desire for advancement in the manufacturing or development of new feedstocks lies in the nuclear weapons applications program. New feedstocks with greater thermal stability and performance would provide the opportunity for insertion of production parts, rather than just prototype parts. Additionally, the ability to print with so-called commodity polymers like polyethylene and polypropylene poses great economic advantages for prototyping and production of large batches of parts. However, a gap exists between the Lab’s needs and what is commercially available – a gap which could be filled by collaboration with the broader industrial sector. Furthermore, connecting with and building relationships with industry partners allows for greater control and input in the developmental process of new powders. This would provide reliable feedstocks, improved quality assurance, and overall higher performance of processes across the additive manufacturing community.
NREL is working closely with DOE, Electrochaea GmbH, and Southern California Gas Company (SoCalGas) to reduce costs of a biomethanation process capable of megawatt-scale deployment that upgrades organic biogas waste streams to produce pipeline quality renewable natural gas (RNG). Biomethanation is a two-step process using a single-celled methanogenic archaea that converts low-carbon low-cost hydrogen (H2) and waste carbon dioxide (CO2) to produce renewable methane (CH4). The process upgrades the biogenic CO2 - while allowing the CH4 to pass through - from biogas sources like dairies, wastewater treatment plants, and landfills. The CH4 produced is a drop-in direct replacement fuel and producers can participate in the growing number of carbon markets; like California's Low Carbon Fuel Standard and the Federal Renewable Fuel Standard. NREL and Argonne National Laboratory have completed a life cycle analysis using the GREET model to show that the biomethanation process produces RNG that is carbon negative even when H2 production via low-temperature water electrolysis is driven by the existing carbon intensity of California's electricity grid. And of course, even further carbon negative (-233 kg CO2e/kWh) when the electricity is produced from low-carbon sources like wind and solar. Leveraging lessons learned from operating SoCalGas' 700L 18-bar bioreactor system, NREL is designing and building a flexible RD&D platform that will enable field trials at biogas and other CO2 sources. A custom 16' long trailer will house a 20L 18-bar bioreactor, 3 - 25 kW proton exchange membrane electrolyzer, and dosing, thermal, and controls systems to support operations with only power, biogas, and water feedstocks required by the field locations. The end-of-project goal is to demonstrate pipeline quality RNG production (> 95% CH4, < 4% H2, <1% CO2, < 0.2% O2 and < 4 parts per million H2 sulfide) using real biogas feedstocks - thereby recycling both greenhouse gases for injection into the natural gas network or to be used onsite.
Presented at the 48th International Technical Conference on Clean Energy (Clearwater Clean Energy Conference), Clearwater, Florida, June 16-19, 2024. This presentation describes the Energy & Environmental Research Center’s development of the Upgraded Carbon Ores-to-Products (UCOP) technology to produce high‑quality graphite and other critical materials from coal and coal wastes for clean energy applications such as batteries and electrodes. It outlines the technical approach, including feedstock cleaning, controlled heat treatment, and graphitization, and presents results demonstrating high graphite purity, novel microstructures, and competitive performance relative to commercial graphite. The work highlights the potential for lower environmental impact and domestic supply chains for critical materials amid increasing global demand and supply‑chain constraints.
The overarching goal of this work was to accelerate the commercialization of high productivity, mixed community microalgal treatment technologies for the synergistic treatment of wastewater and the production of biofuel feedstocks. This project addressed a critical barrier to the financial viability and energy efficiency of algal wastewater treatment: an inability to design and operate high-rate processes that reliably achieve target effluent qualities, areal productivities, and biochemical compositions (lipid, protein, carbohydrate content) despite fluctuations in wastewater composition, weather, and microbial communities. Key outcomes from this work include an optimized and controlled Advanced Biological Nutrient Recovery (ABNR) design as well as a suite of open-source tools that include a calibrated and validated algae process simulator in QSDsan and a novel low-cost, real-time microbial monitoring tool. These tools can be leveraged by other algal cultivation and wastewater treatment technology developers in future work.
Valorization of agricultural and food waste digestates is crucial for sustainable waste management to reduce environmental impacts and improve the economics of commercial farms. Hydrothermal liquefaction (HTL) of anaerobic digestates was evaluated to recover resources by converting them into carbon-dense biocrude oil and a nutrient-rich HTL aqueous phase (HTL-AP) coproduct. The effects of HTL temperature (280–360 °C), reaction time (10–50 min), feedstock pH (2.5–8.5), digestate salt content (1–5 wt%), and digestate cellulose-to-lignin ratio (0.2–1.8) on energy and nutrient recovery were systematically investigated in a set of well-designed experiments following a half-fractional central composite protocol. Response surface analysis combined with HTL product characterization and comparative literature study produced a comprehensive reaction pathway for HTL of anaerobic digestates. Moreover, this analysis revealed the importance of acidic feedstocks (pH 3.00–5.53), high reaction temperatures (337–360 °C), and reaction times <45 or 45–50 min for digestates with Cel/Lig >1 or <1, for maximizing the energy recovered in biocrude (high carbon yield and low heteroatom content) and the amounts of P, NH 3 –N, and Mg distributed in the HTL-AP. Acidic conditions catalyzed biocrude production, inhibited the Maillard reaction (lowering the nitrogen content in biocrude), and partitioned nutrients into the HTL-AP. Higher reaction temperatures coupled with longer reaction times activated hydro-denitrogenation and deoxygenation reactions to improve biocrude quality. Finally, this work provides not only validated methods to achieve targeted resource recovery for specific feedstock compositions using HTL, but also a comprehensive mechanistic understanding of the HTL of biomass waste for controlling target product characteristics.
Imaging in the visible spectrum is a low-cost tool that can be readily deployed for in-field or over-belt monitoring of biomass quality for bio-refining operations. Rapid image analysis coupled with innovative preprocessing may reduce the impacts of feedstock variability through identification of contaminants or other material attributes to guide selective sorting and quality management. Image analysis was employed to evaluate the quality of corn stover in red-green-blue (RGB) chromatic space. This study used controlled, bench-scale imaging as a proof-of-concept for rapid quality assessment of corn stover based on variations in material attributes, including chemical and physical attributes, that relate to biological degradation and soil contamination. Additionally, logistic regression-based classification algorithms were used to develop a method for biomass screening as a function of biological degradation or soil contamination. This study demonstrated the use of image analysis to extract features from RGB color space to investigate variations in critical material attributes from chemical composition of corn stover. Fourier transform infrared (FT-IR) suggested a correlation between red band intensity and biological degradation, while detailed surface texture analysis was found to distinguish among variations in ash. These insights offer promise for development of a rapid screening tool that could be deployed by farmers for in-field assessment of biomass quality or biorefinery operators for in-line sorting and process optimization.
Selective Laser Sintering (SLS) uses a precisely controlled laser to fuse polymer powder to build complex 3D shapes. While SLS covers a wide application space, the processing knowledge of polymer powder is limited, restricting the number of commercial powders available. This study serves to expand the processing knowledge of polypropylene and polyethylene in parallel with a “mature” SLS feedstock, nylon, through melt flow index (MFI) characterization. Differential Scanning Calorimetry (DSC) was used to explore the sintering window of the polymers. Polyethylene and polypropylene exhibited a relatively narrow sintering window between 4 – 5 °C, whereas the sintering window of nylon was much wider, between 23 – 24 °C. This suggests that the print quality between polyethylene and polypropylene would be similar; however, X-ray computed tomography revealed a higher volume of print defects, voids, and de lamination in polyethylene than in polypropylene. MFI analysis provided additional insight into the difference in print quality, as the MFI of polyethylene was 7.16 g/10 min, 9.95 g/10 min for polypropylene, and 17.23 g/10 min for nylon. MFI is inversely correlated to melt viscosity, and a low melt viscosity is desired for proper coalescing between the layers and particles. These results suggest that MFI is a promising tool, in conjunction with traditional thermal analysis, for screening candidate powder feedstocks for SLS and optimization of print parameters for novel powders.
Grid stiffened continuous fiber reinforced composite panels are an attractive option for creating lightweight structures due to the tailorability for various applications and the resulting high specific properties. However, the panel stiffeners and stiffener intersections result in high tooling complexity and correspondingly high cost of implementation. These factors have limited the impact of such structures in the composites industry. Previous research has demonstrated the ability to produce high quality, high aspect ratio beams, representative of individual grid stiffeners, using E-glass/PET comingled tow via direct digital manufacturing. Further, prior preliminary efforts have demonstrated the potential to use the same approach to manufacture grid intersections that have continuous fiber in both directions. To expand on the previous efforts in grid stiffeners produced by direct digital manufacture with radically reduced tooling requirements, this effort compares two methods of providing positioning and consolidation, nozzle vs. roller. Both processes are based on a commingled yarn feedstock. The extrusion through a nozzle has been shown to enable grid intersection control through local variations in applied consolidation and serves as the baseline process. However, this approach requires a continuous placement path to create the complete grid stiffened panel as no mechanism for cutting and restarting has been implemented. Alternatively, a newly developed placement head incorporating cut and refeed, mounted to a 6-axis robot, offers the potential of improved path placement efficiency. The two techniques are used to produce similar grid composite stiffeners to evaluate the effectiveness of producing the grid intersections. Rate of deposition of the two end effectors are compared and the quality of the associated grid stiffeners, and intersections, are determined through measurement of geometry, fiber volume fraction and void fraction.