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At least 91 records · Page 5

Optimized Illuminance Operation-A Light-Driven Dilution Strategy to Improve Microalgae Biomass Productivity

Periodic dilution is a necessary operation to avoid light limitation due to self-shading as the culture grows dense. However, environmental conditions are constantly changing in outdoor cultivation systems, making it difficult to determine the optimal dilution rate. To address this challenge, this study evaluated a dilution approach based on light penetration to optimize illuminance (OptiLum) in the culture. Biomass concentration was controlled via sensor-feedback directed dilution to ensure that light reaching the bottom of the culture was maintained above the compensation intensity. Under replicated outdoor pond conditions, by keeping the entire culture within a net-positive photosynthetic zone, the OptiLum operation improved the biomass productivities of two top-performing strains, Picochlorum celeri and Tetraselmis striata, by 95 % and 86 %, respectively, compared to conventional semi-continuous batch cultivation. The dilution rate varied daily and was dynamically adjusted based on the light status within the culture, which is concurrently influenced by weather, culture density, and growth rate. The techno-economic analysis showed that the OptiLum operation could reduce biomass production cost by as much as 24 % and 33 % for P. celeri and T. striata, respectively, assuming a low-cost dewatering approach with initial gravity settling of biomass can be realized for both strains. However, a more costly two-stage dewatering strategy, comprising only membranes and centrifuges, may be necessary for non-settling strains, such as P. celeri, which would alternatively increase production costs by 23 % for the OptiLum case. The results demonstrated that the proposed OptiLum operation is a promising approach to improve biomass productivity and lower production cost via weather-responsive and self-adjusting dilution.

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Economic and environmental performance of biomass gasification for renewable natural gas production in the context of the U.S. natural gas supply

Bioenergy technologies offer potential for reducing greenhouse gas (GHG) emissions. One such promising technology is biomass gasification, which is the conversion of biomass into renewable natural gas (RNG) for use with a natural gas combined-cycle power generation system. However, the associated economic and emission effects need to be better understood to enable optimal decision-making and avoid missed opportunities for enhancing efficiency and increasing system circularity. This analysis explores opportunities to (1) decarbonize natural-gas-based systems and (2) leverage the extensive US natural gas infrastructure to mobilize biomass resources to achieve environmental and economic benefits. Here, in this analysis, the research team used a spatially explicit biomass logistics model (integrated with relevant biomass availability, technoeconomic analysis, and life cycle assessment information) to simulate economically optimal biomass allocation for RNG production and use for decarbonization in the United States. Results show that the United States has the potential to produce 9203 million GJ of RNG within the expected range of $\$$12–30/GJ. Further analyses tested the overall RNG production system's sensitivity to economic and emissions parameters of nine different processes. The sensitivity analysis results indicate that the median carbon abatement cost of RNG is most sensitive to changes in emissions associated with conversion processes and land use changes. These findings provide a deeper understanding of RNG's economic and emission potential for decision-making and guiding future research.

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Enhancing the flowability of woody biomass slurries in wet biorefineries

Feeding wet lignocellulosic biomass (e.g., softwood and hardwood) slurries into high-pressure, high-temperature reactors at an industrially relevant scale presents significant challenges, such as process equipment plugging. Here, in this study, we investigate the possibility of improving the flowability of biomass slurries in an industrial scale wet biorefinery by tuning the physical and chemical characteristics of the biomass particles. To understand the effects of the chemical characteristics of woody biomass on flowability, cellulose pulp particles are produced from pulp sheets via knife milling, pelletizing, and crumbling. These cellulose pulp particles are processed for acid hydrolysis dehydration (AHDH) at one –tonne-per day pilot scale. The levulinic acid yield (a product of cellulose AHDH) and the flowability of the biomass particles are compared using market pulp, 1 mm crumbled pine softwood, a blend of 2 mm hammer-milled pine softwood with 10 wt.% bark, and 2 mm hammer-milled hardwood material with 10 wt.% bark (to represent forest residues). The results indicate that the presence of hemicellulose and lignin influences the flowability of lignocellulosic feedstocks. Crumbled wood particles show poor flowability at the pilot scale, while the presence of fine materials (less than 0.7 mm) and bark improves the flowability of biomass slurries without affecting the organic acid yields (based on C6 carbohydrate content).

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Predicting the evolution of biomass bulk density through feedstock preprocessing: Discrete element modeling, regression analysis, and pilot-scale validation

Bulk density is an important material property of biomass feedstocks, influencing handling, storage, transport costs, and conversion efficiency. In this study, predictive regression models for loose and tapped bulk densities of Alamo and Cave-in-Rock switchgrass are developed using a comprehensive dataset generated via calibrated bonded-sphere discrete element method (DEM) simulations. Here, a key contribution of this study is the use of a DEM-based approach, which correlates density with moisture content and particle size distribution parameters and enables analysis across a continuous particle size range, overcoming limitations of purely experimental data. For comparison, regression models are also developed using only experimental data from pilot-scale runs at the Biomass Feedstock National User Facility at Idaho National Laboratory. Validation against pilot-scale data showed reasonable prediction accuracy for both model types, particularly for smaller particle sizes (post-secondary grinding). While the experimental model showed slightly better performance matching the validation data in some cases, the DEM-based model benefits from a much larger dataset, reduced predictor multicollinearity, and continuous parameter coverage, highlighting the utility of validated simulation models for developing robust predictive tools for biomass preprocessing applications.

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Graphitization of banana peel – synergistic effect of Ca and other elements of the biomass on carbon structure transformation

Recently, growing interest has been observed in using calcium as an effective catalyst for graphitizing disordered carbon materials derived from biomass and bioprecursors, such as cellulose and lignin, at relatively low tem peratures (<2000 °C). Herein, it is demonstrated that in the presence of elements such as calcium, silicon, sulfur, and potassium, which are abundant in banana peel composed of cellulose and lignin base, graphitization occurs much more effectively. The addition of external calcium source, in the form of calcium carbonate, to the banana peel-derived carbon and heat-treatment of the mixture under protective atmosphere up to 1750 °C results in a significant increase in the degree of the graphitization order and local formation of graphite crystals, whereas the same preparation and heat-treatment procedure applied to carbon material derived from pristine cellulose does not lead to such effective graphitization. It is indicated that the graphitization of banana peel carbon occurs due to the synergistic effect of the external Ca-based catalyst and the internal elements present in the raw biomass, supporting the transformation of the disordered graphene-like layers into graphitic structures. These findings are important for the future production of green graphite from biomass

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Can classical DEM simultaneously capture compressibility and flowability of milled biomass?

Accurate prediction of the rheological behavior of biomass is essential for the design and operation of hoppers, feeders, and storage systems in biorefineries. This study examines whether the classical, coarse-grained discrete element method (DEM) formulation can simultaneously reproduce the compressibility and flowability of milled herbaceous biomass, using Miscanthus × giganteus as a representative material. The model represents particles as rigid spheres interacting through Hertz-Mindlin elastic-frictional contacts augmented with an area-dependent cohesion term. Laboratory cyclic compression and wedge-shaped hopper discharge experiments were used as calibration benchmarks. Although the model can independently reproduce each behavior by appropriately tuning particle Young's modulus E and cohesion energy density k, an extensive parametric investigation comprising more than 600 simulations reveals that the optimal parameter regions for compression and hopper flow are distinct and non-overlapping in (E, k) space. Surrogate surface analysis further shows that the corresponding objective-function valleys exhibit similar trends but are approximately parallel and spatially offset, precluding a unified calibration within the explored domain. Sensitivity analysis indicates that compressibility is governed predominantly by stiffness and cohesion, whereas the slope of the mass flow rate-opening relation in hopper discharge is primarily controlled by tangential friction. Extensions incorporating particle size distribution and clumped-sphere representations do not eliminate the incompatibility. These results systematically reveal, for the first time, the structural limitation of simplified DEM formulations in representing biomass rheological behavior, underscoring the necessity for models incorporating additional physical mechanisms, such as particle deformability or enhanced interlocking, to achieve unified predictive capability for biomass handling behavior.

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Recent developments in pretreatment technologies on lignocellulosic biomass: Effect of key parameters, technological improvements, and challenges

Lignocellulosic biomass is an inexpensive renewable source that can be used to produce biofuels and bioproducts. The recalcitrance nature of biomass hampers polysaccharide accessibility for enzymes and microbes. Several pretreatment methods have been developed for the conversion of lignocellulosic biomass into value-added products. Yet, these pretreatment methods also produce a wide range of secondary compounds, which are inhibitory to enzymes and microorganisms. The selection of an effective and efficient pretreatment method discussed in the review and its process optimization can significantly reduce the production of inhibitory compounds and may lead to enhanced production of fermentable sugars and biochemicals. Furthermore, evolutionary and genetic engineering approaches are being used for the improvement of microbial tolerance towards inhibitors. Advancements in pretreatment and detoxification technologies may help to increase the productivity of lignocellulose-based biorefinery. Here, we discuss the recent advancements in lignocellulosic biomass pretreatment technologies and strategies for the removal of inhibitors.

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Opportunities and challenges for flow-through hydrothermal pretreatment in advanced biorefineries

Hydrothermal pretreatment (HTP) using only water offers great potential to reduce the overall cost of the bioconversion process. However, traditional HTP performed in a batch has limitations in removing lignin and often needs to be performed under severe conditions to achieve reasonable pretreatment effects. Additionally, lignin left in the pretreated residue at these conditions is also highly condensed, thus possessing an even more adverse impact on the hydrolysis process, which requires high enzyme loadings. To address these technical challenges, HTP performed in a flow-through configuration was developed to simultaneously achieve near-complete hemicellulose recovery, high lignin removal and high sugar release. Despite facing challenges such as potentially large water usage, flow-through HTP still represents one of the most cost-effective and eco-friendly pretreatment methods. This review mainly covers the latest cutting-edge innovations of flow-through HTP along with structural and compositional changes of cellulose, hemicellulose, and lignin before and after pretreatment.

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Effect of pelleting on the enzymatic digestibility of corn stover

Pelleting of lignocellulosic biomass to improve its transportation, storage and handling impacts subsequent processing and conversion. Here, this work reports the role of high moisture pelleting in the enzymatic digestibility of corn stover prior to pretreatment, together with associated substrate characteristics. Pelleting increases the digestibility of unpretreated corn stover, from 8.2 to 15.5% glucan conversion, at 5% solid loading using 1 FPU Cellic® CTec2 per g solids. Compositional analysis indicates that loose and pelleted corn stover have similar non-dissolvable compositions, although their extractives are different. Enzymatic hydrolysis of corn stover after size reduction to normalize particle sizes and removal of extractives confirms that pelleting improves corn stover digestibility. Such differences may be explained by the decreased particle size, improved substrate accessibility, and hydrolysis of cross-linking structures induced by pelleting. These findings are useful for the development of processing schemes for sustainable and efficient use of lignocellulose.

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Pretreatments for the continuous production of pyrolytic sugar from lignocellulosic biomass

Fast pyrolysis of lignocellulosic biomass yields little sugar or anhydrosugars compared to pyrolysis of pure polysaccharides because naturally abundant alkali and alkaline earth metals (AAEM) in biomass catalyze the fragmentation of pyranose and furanose rings. Sugar yields can be increased dramatically by pretreating the biomass with sulfuric acid prior to pyrolysis, which passivates the catalytic activity of the metals by converting them into thermally stable salts. However, depolymerization of lignin in biomass also depends on the catalytic activity of AAEM. Thus, passivating AAEM has the unintended consequence of slowing the rate of depolymerization and volatilization of lignin, resulting in a transient melt phase that agglomerates and can foul pyrolysis reactors. Here, to overcome this problem, various non-alkali metal sulfates were tested as replacements for sulfuric acid. Iron in the form of ferrous sulfate proved the most effective in depolymerizing lignin without fragmenting pyranose rings. Conventional nitrogen-blown pyrolysis of ferrous sulfate pretreated corn stover achieved WHSV of 4 h –1 compared to only 0.6 h –1 for acid pretreated corn stover. Autothermal (air-blown) pyrolysis of ferrous sulfate pretreated corn stover showed even more dramatic improvement, increasing WHSV from 1 h –1 to 10 h –1 compared to acid pretreated corn stover under autothermal operation. Fermentable sugar yields from the pyrolysis of corn stover increased from 0.9 wt% to 11.8 wt% on a biomass basis, a 13-fold increase as a result of the ferrous sulfate pretreatment. These advantages combine to increase volumetric sugar productivity from 62 g L –1 h –1 for conventional pyrolysis of untreated corn stover to 2041 g L –1 h –1 for autothermal pyrolysis of ferrous sulfate treated corn stover.

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Investigating biomass composition and size effects on fast pyrolysis using global sensitivity analysis and CFD simulations

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.

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Modelling a compressible packed bed flow-through washing and deacetylation reactor for corn stover pretreatment

The deacetylation process is a key step in the lignocellulosic biomass pretreatment process that currently is done in batch reactors. A flow-through diffuser type reactor is a promising approach to make the process more efficient and economically favorable. A washing step before deacetylation can further reduce chemical usage and increase the process efficiency. However, the highly compacted biomass particles can cause significant pressure-drop for fluid flow through the bed, which in the worst case can cause self-enhancing pressure-drop leading to reactor blocking. Detailed understanding of the fundamentals of flow through a compressible packed bed is necessary to scale up the washing and deacetylation process. This work closely examines the classical Kozeny Carman equation and proposes modifications to accurately predict the pressure-drop at different compaction states. Here, a compressible packed bed model is further developed based on the interaction between solid mechanics and fluid dynamics. Experimental measurements of water flow through a packed bed of corn stover show good agreement with the developed model. Corn stover washing process scale up simulations show that smaller reactor diameter, higher friction lining material, lower process throughput, and higher process temperature is favorable in stabilizing the bed from extensive compression when the bed height is fixed.

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Atomistic origins of biomass recalcitrance in organosolv pretreatment

Separation of lignocellulosic biomass components for use in sustainable energy and biomaterials applications remains challenging, since biopolymers such as cellulose and hemicellulose carbohydrates and polyaromatic lignin are found in close proximity within the plant secondary cell wall. Organic solvents have been used to pretreat recalcitrant biomass and separate the interacting polymers, solubilizing the lignin fraction for lignin-first valorization approaches. However, no single organosolv pretreatment approach has proven superior for heterogeneous biomass samples. Here we present a complementary atomic view of interactions between biomass components using molecular simulations, specific to lignin and cellulose, resolving mechanistic hypotheses for how lignin-cellulose composition influences separation processes. Further, using lignin-cellulose binding free energy calculated using molecular simulations, we find polar protic solvents such as methanol and ethanol separate lignin from cellulose, irrespective of the charge on the lignin monomer. Aprotic organic solvents (DMSO, THF, 1,4-dioxane) separate uncharged lignin (phenols and monolignols) from cellulose, while charged lignin (cinnamates) prefer binding to cellulose.

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Optimal control to handle variations in moisture content and reactor in-feed rate

The variations in feedstock characteristics, such as moisture and particle size distribution, lead to an inconsistent flow of feedstock from the biomass pre-processing system to the reactor in-feed system. These inconsistencies result in low on-stream times at the reactor in-feed equipment. This research develops an optimal process control method for a biomass pre-processing system comprised of milling and densification operations to provide the consistent flow of feedstock to a reactor's throat. This method uses a mixed-integer optimization model to identify optimal bale sequencing, equipment in-feed rate, and buffer location and size in the biomass pre-processing system. This method, referred to as the hybrid process control (HPC), aims to maximize throughput over time. We compare HPC with a baseline feed forward process control. Our case study based on switchgrass finds that HPC reduces the variation of a reactor's feeding rate by up to 100% without increasing the operating cost of the biomass pre-processing system for biomass with moisture ranging from 10 to 25%. Additionally, HPC reduces the cost of processing biomass by 0.36%–2.22%, and reduces processing time by 0.35%-2.24%. Furthermore, a biorefinery can adapt HPC to achieve its design capacity.

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