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DOE OSTI2022

2022 Swainson Hawk Summary Report

This summary report, prepared by Condor Country Consulting Inc. (CCCI), covers all construction monitoring of the Swainson’s hawk (Buteo swainsoni, SWHA) nest that occurred from April 2022 to July 2022 at the Sandia National Laboratories, California (SNL/CA). SNL/CA personnel first observed the SWHA pair near the pine tree along East Avenue that contained last year’s nest on April 19th, 2022. SWHA are a threatened species in the state of California and require a quarter-mile non-disturbance buffer. SNL/CA received approval from the California Department of Fish and Wildlife (CDFW) to continue work within the buffer area with a biological monitor on site to observe the SWHA for construction related disturbances. Monitoring began on April 20th, 2022 and concluded on July 1st, 2022.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2023

Design of an Integrated Solids Handling System to Maximize Syngas Process Reliability (Cooperative Research and Development Final Report)

The National Renewable Energy Laboratory (NREL), in partnership with Wonderful Renewable Energy (WRE) and Idaho National Laboratory (INL), plans to develop a general methodology for designing integrated biorefinery solids preprocessing, handling, and feeding systems based on the chemical, physical, and mechanical attributes of the starting biomass material. This attribute-driven approach will include detailed feedstock property measurements, iterative computational modeling, and bench-scale testing to design systems for preprocessing, handling, and reactor in-feed, up to and including the selection of the conversion reactor. The initial tests and system design will be conducted using waste material from almond and pistachio growing and production operations (shells, hulls, and wood), targeting the conversion of this material to syngas for electricity production. The methodology will then be generalized to other feedstocks. The purpose of this project is to design an integrated solids handling system to maximize the process reliability of converting almond and pistachio waste to electricity. The design methodology and workflow developed from this example will then be applied to the Feedstock Conversion Interface Consortium (FCIC) benchmark loblolly pine residues, thus demonstrating the robustness of the overall design approach and providing insight and guidance for future conversion systems. V-Grid Energy Systems was brought on as a subcontractor to provide gasifiers and labor to complete gasifier runs.

09 BIOMASS FUELS↗

DOE OSTI2023

How does Mercury Methylation Respond to Intensive Forest Management and the Creation of Anoxia in Floodplain Soils

This research endeavor, entitled “How does Mercury Methylation Respond to Intensive Forest Management and the Creation of Anoxia in Floodplain Soils?” represents a collaborative effort undertaken by the University of North Carolina at Greensboro, led by Principal Investigator Dr. James S. Coleman, the United States Forest Service Santee Experimental Forest (SEF) Station Director Dr. Carl C. Trettin as Co-Principal Investigator, and The Chinese University of Hong Kong Dr. Martin Tsz-Ki Tsui and Dr. Alex T. Chow serving as Co-Principal Investigators. The overarching objective of this proposed project was to capture and elucidate the immediate and short-term effects of forest disturbances, namely clear-cutting and thinning, on the biogeochemical cycling of mercury (Hg) within the SEF in Huger, South Carolina. To accomplish this objective, a two-year exploratory study was proposed to conduct soil sampling and measurements of Hg and toxic methylmercury (MeHg), and soil physiochemical data within the experimental watershed (WS77) at SEF, following the clear-cutting process initiated for the preparation of the native longleaf pine restoration project by the USFS. Based on our mercury and hydrological data assessment, we concluded that the implementation of forest management practices, particularly mechanical thinning and clear-cutting, appears to enhance the potential for Hg methylation. Furthermore, these forest management practices may disrupt and alter the storage, mobility, and transformations of atmospherically deposited Hg in these lowland forests. We advocate for further research to comprehensively evaluate the impact of forest management on Hg cycling within forest ecosystems.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Molecular Modeling to Increase Kraft Pulp Yield

Kraft pulping is an important component of the pulp and paper industry and is the predominant technology for removing lignin from wood carbohydrates. However, kraft pulping is energy-intensive, expensive, and is limited by the degradation of cellulose and hemicellulose. Pretreatment increases yield by stabilizing cellulose against degradation. However, protection of galactoglucomannan (GGM), the primary hemicellulose component of softwood, is minimal when conventional pretreatments are used. Here we investigate the effectiveness of new pretreatment methods on southern pine wood chips under a range of experimental conditions. If successful, improved pretreatment methods will increase carbohydrate yield, reduce waste, reduce energy use, lower the cost of bleaching, and decrease the cost of air emission controls. The purpose of this CRADA was to combine industrial expertise in wood pulping with national laboratory expertise in high-performance computing, leading to improved understanding of molecular-scale processes that limit carbohydrate yield during pretreatment and pulping. A combined computational and experimental approach was used to investigate pretreatment effectiveness under relevant pulping conditions and then use molecular simulation techniques to provide complementary insight into structural and chemical factors that govern the observed behavior. In this report we summarize the accomplishments of the project.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

Bioblendstocks to Optimize Mixing Controlled Compression Ignition (MCCI) Engines

In this project, a team of researchers from the University of Massachusetts Lowell, the University of Maine, and Mainstream Engineering developed an integrated process for the product of bioblendstocks to optimize mixing controlled compression ignition (MCCI) engines. The objective was to improve the energy density, sooting propensity, and cetane number of base diesel fuel while maintaining cold weather behavior. The process converts woody biomass (e.g. sawmill residues) into bio-oil through selective fast pyrolysis; the bio-oil is then selectively upgraded to form selectively oxygenated, minimally-branched hydrocarbons using non-noble metal catalysts in combination with metal-catalyzed hydrogenation. Advanced predictive models, in conjunction with existing property databases, and experimental testing are used to evaluate overall bioblendstock properties and their impact on base diesel fuel. An iterative, targeted upgrading approach was implemented to optimize the proposed bioblendstock’s properties. Assessment methodologies included techno-economic analysis, life-cycle assessment, property testing, and engine testing. Ultimately, the project team successfully produced a viable bioblendstock while identifying critical process points related to scale-up efforts. It was found that producing pyrolysis oils at 500 degrees C and with pine particle sizes of 1-2 mm led to bio-oil with a higher yield (of approximately 45 wt%) and rich amounts of aromatic alcohols. The resultant pyrolysis oil was then upgraded using a sequence of mild hydrotreating, followed by catalytic etherification and esterification, followed by another final mild hydrotreating to produce a blendstock containing saturated species with a limited, but non-zero, amount of oxygen. The aromatic alcohols produced by pyrolysis were especially helpful in this regard, as the resulting bicycloethers and derivatives exhibited high cetane numbers. While most bulk properties of the bioblendstock met or exceeded targeted thresholds, viscosity and cloud point notably fell outside the expected range; this could be addressed by blending limits and/or through the use of additives that are commonplace in current refinding practices. Identification of a bioblendstock that can be produced economically at scale while improving the performance and emissions characteristics of internal combustion engines positively affects the economy by boosting domestic fuel production and the environment by decreasing harmful emissions and increasing efficiency.

09 BIOMASS FUELS↗

DOE OSTI2024

Tree rings reveal the transient risk of extinction hidden inside climate envelope forecasts

Given the importance of climate in shaping species’ geographic distributions, climate change poses an existential threat to biodiversity. Climate envelope modeling, the predominant approach used to quantify this threat, presumes that individuals in populations respond to climate variability and change according to species-level responses inferred from spatial occurrence data—such that individuals at the cool edge of a species’ distribution should benefit from warming (the “leading edge”), whereas individuals at the warm edge should suffer (the “trailing edge”). Using 1,558 tree-ring time series of an aridland pine (Pinus edulis) collected at 977 locations across the species’ distribution, we found that trees everywhere grow less in warmer-than-average and drier-than-average years. Ubiquitous negative temperature sensitivity indicates that individuals across the entire distribution should suffer with warming—the entire distribution is a trailing edge. Species-level responses to spatial climate variation are opposite in sign to individual-scale responses to time-varying climate for approximately half the species’ distribution with respect to temperature and the majority of the species’ distribution with respect to precipitation. These findings, added to evidence from the literature for scale-dependent climate responses in hundreds of species, suggest that correlative, equilibrium-based range forecasts may fail to accurately represent how individuals in populations will be impacted by changing climate. A scale-dependent view of the impact of climate change on biodiversity highlights the transient risk of extinction hidden inside climate envelope forecasts and the importance of evolution in rescuing species from extinction whenever local climate variability and change exceeds individual-scale climate tolerances.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Woody Feedstock 2022 State of Technology Report

The U.S. Department of Energy promotes production of advanced liquid transportation fuels from lignocellulosic biomass by funding fundamental and applied research that advances the state of technology (SOT). As part of its involvement in this mission, Idaho National Laboratory completes an annual SOT report for n th -plant and 1 st -plant woody biomass feedstock logistics. The purpose of the SOT is to provide the status of feedstock supply system technology development for woody biomass to biofuels relative to technical targets and cost goals from specific design cases, based on data and experimental results. Conventional feedstock supply systems need to be modified to meet the demands of conversion pathways, specifically to have the ability to adjust the quality of the raw biomass materials. Advanced systems incorporate innovative methods of material handling, preprocessing and supply chain configuration. In advanced designs, variability of the raw biomass can be reduced to produce feedstocks of a uniform format, moving toward biomass commoditization. Against this backdrop, the 2022 Woody SOT for low-ash woody feedstocks utilizes feedstock fractionation by incorporating technologies that can separate the biomass into its anatomical fractions (wood, bark, needle, and extrinsic ash) to reduce impurities and attempt to maximize the retention of usable fractions that satisfy downstream quality considerations. By using a series of air classification steps, this strategy can reduce the extrinsic ash in forest residues, separate out a majority of the incoming needles (which can be supplied to alternate markets), and maximize the retention of whitewood in the usable fraction. The fractionated forest residues are then mixed with clean-pine chips in a 50-50 blend to prepare the feedstock for the desired conversion pathway. The n th -plant analysis estimated the delivered cost for the feedstock at $\$$69.23/dry ton (2016$\$$) which represents a $\$$6.64/dry ton decrease compared to the cost estimate of the 2021 Woody SOT supply system for low-ash woody feedstocks. The quality requirements in the 2022 Woody SOT were identical to those of the 2021 Woody SOT at = 1.00 wt % ash and = 50.51 wt% carbon. The cost savings derive primarily from reductions in dry matter losses during air classification. The GHG emissions for the n th -plant analysis were estimated at 178.39 kg CO2e/dry ton compared to 178.71 kg CO 2 e/dry ton in the 2021 Woody SOT, a decrease of 0.32 kg CO2e/dry ton. The small change stems from an increase in emissions attributed to preprocessing and slightly larger savings in emissions from transportation. In the 1 st -plant analysis of the 2022 Woody SOT system, the average throughput was estimated to be approximately 2,128 dry tons/day or 96.51% of the name plate capacity. During the simulation the daily throughput ranged from 1,090 dry tons/day to 2,200 dry tons/day, or 49.43% to 99.75% of the daily nameplate capacity. After the year of operation 722,403 tons of processed feedstock were produced in total without regard to quality considerations (99.64% of the annual nameplate capacity). The variability in throughput was primarily caused by equipment failures in the system. Regular failures, downtime caused by routine maintenance per manufacturer guidelines, contributed to a majority 62.50% of failures and 62.60% of downtime. Failures due to wear were the other cause of disruption within the system, impacting the rotary shear and orbital screen and accounting for 37.50% of the failures and 37.40% of the total downtime. Ultimately the system was on stream for 87.84% during the simulation period, which is only 2.16 percentage points below the nth-plant assumption for on-stream time. The production cost of the system averaged $\$$71.66/dry ton. The costs ranged from a minimum of $\$$71.23/dry ton to a maximum of $\$$2,115.30/dry ton. When dry matter losses (disposed low-quality fractions as well as other losses such as in grinders) were considered the costs increased to an average of $\$$75.11/dry ton with a minimum of $\$$74.69/dry ton and a maximum of $\$$2,136.86/dry ton...

09 BIOMASS FUELS↗

DOE OSTI2026

TES: Modelling Microbes to Predict Post-Fire Carbon Cycling in the Boreal Forest across Burn Severities (Exploratory Proposal)

Wildfires in boreal ecosystems represent a globally significant ecological process that is expected to be sensitive to changing climate as well as forest management strategies, but is currently insufficiently understood and represented in models. We sought to determine whether linking belowground microbial community composition, size, and activity to aboveground properties of burn severity and plant community composition (upland jack pine and upland spruce) would allow us to better model post-fire soil CO 2 fluxes using the Carbon, Organisms, Rhizosphere and Protection in the Soil Environment (CORPSE) model. We used an integrated modelling experimental approach, with mechanistic laboratory experiments designed to inform models.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

National summary for agricultural crops and residues, 2041

This dataset contains national summary data on agricultural crop and residue production, which include agricultural crop, agricultural residue, herbaceous energy crop, and woody energy crop. Each tab in the Excel file contains information corresponding to a resource category, and within each tab, there is a structured table presenting resource production (in dry short ton) by offered price (in USD per dry short ton) across multiple scenarios (mature-market low, mature-market medium, and mature-market high). Consistent with the 2023 Billion-Ton report for agricultural resources, these scenarios represent potential production scenarios for 2041. List of the resources shown in this dataset: - Agricultural crop: Barley, Corn, Cotton, Grain sorghum, Hay, Oats, Rice, Soybeans, Wheat - Agricultural residue: Barley straw, Corn stover, Oats straw, Sorghum stubble, Wheat straw - Herbaceous energy crop: Biomass sorghum, Energy cane, Miscanthus, Switchgrass - Woody energy crop: Eucalyptus, Pine, Poplar, Willow

agricultural crop↗

DOE OSTI2026

Agricultural crop production by county - mature-market medium scenario

This dataset contains data on agricultural crop production by county from 2022 to 2041. The agricultural crop in this dataset includes barley, biomass sorghum, corn, cotton, energy cane, eucalyptus, grain sorghum, hay, miscanthus, oats, pine, poplar, rice, soybean, switchgrass, wheat, and willow. The dataset was obtained from the database of the BT23 (Davis et al., 2024) for the mature-market medium scenario with biomass market prices from $30 to $130 per dry ton.

agricultural crop↗

DOE OSTI2024

Agricultural crop and residue production by county - mature-market medium scenario at $70

This dataset contains data on agricultural crop and residue production by county from 2022 to 2041. The agricultural crop in this dataset includes barley, biomass sorghum, corn, cotton, energy cane, eucalyptus, grain sorghum, hay, miscanthus, oats, pine, poplar, rice, soybean, switchgrass, wheat, and willow, and the agricultural residue includes barley straw, corn stover, oats straw, sorghum stubble, and wheat straw. The dataset was obtained from the database of the BT23 (Davis et al., 2024) for the mature-market medium scenario with biomass market prices of up to $70 per dry ton.

agricultural production↗

DOE OSTI2022

Performance and Economic Analysis of Organosolv Softwood and Herbaceous Lignins to Activated Carbons as Electrode Materials in Supercapacitors

In this work, yellow pine (YP, softwood) and switchgrass (SG, grass) lignins were extracted as a coproduct of an organosolv γ-valerolactone (GVL) biorefinery that also produces biofuels and furfural. The extracted lignins were converted to carbon precursors for synthesizing porous activated carbon electrodes for high energy-density supercapacitors. This research details the impact of lignin composition on the derived porous structures and electrochemical properties of activated carbons. Lignin precursors with various syringyl (S) to guaiacyl (G) contents were characterized using 31 P nuclear magnetic resonance (NMR) and two-dimensional 1 H‒ 13 C NMR. A two-step activation process, using steam and carbon dioxide as the activating agents, enabled the formation of porous carbons structures with high surface area. The capacitive behavior of supercapacitors was systematically characterized by cyclic voltammetry, charge-discharge cycling, and electrochemical impedance spectroscopy. The specific capacitance of YP and SG capacitors reached 367 and 221 F g −1 , respectively. Both types of capacitors demonstrated remarkably stable capacitance (capacitance retention >90%) along with excellent Coulombic efficiency (>99%) over 10,000 cycles. Compared to SG electrode, the better electrochemical performance achieved with YP electrodes was mainly due to shorter diffusion path, improved ionic mobility, and increased active surface area. The inexpensive lignin-based porous electrodes synthesized in this work can be used for various electrochemical devices for improved performance, decreased cost, and enhanced durability. This work also demonstrates that the selection of feedstock and appropriate processing conditions can tailor the structure of carbon composites for targeted applications. Techno-economic analysis indicates that YP and SG activated carbons can be produced at a minimum selling price of $8,493 and $6,670 per ton, respectively, which is competitive with the commercially available supercapacitor-grade activated carbons.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Factors that influence the activity of biomass-degrading enzymes in the presence of ionic liquids—a review

Ionic liquids (ILs) are seen as a more sustainable alternative to volatile organic solvents that are accelerating innovations in many industries such as energy storage, separations, and bioprocessing. The ability to effectively deconstruct lignocellulosic biomass is a significant hurdle in the biorefining/bioprocessing industry and presents limitations towards the commercial production of bioproducts (such as biofuels, biomaterials, etc. ). Certain ILs have been shown to promote effective lignin removal, cellulose recovery, and sugar yields from various biomass feedstocks such as corn stover, wheat straw, sugarcane bagasse, sorghum, switchgrass, miscanthus, poplar, pine, eucalyptus, and certain mixtures of municipal solid waste. However, these improvements are often counteracted by the limited biocompatibility of ILs, which results in an IL-induced reduction in enzyme activity and stability—an important downstream step in the conversion of biomass to biofuels/bioproducts. As a result, significant efforts have been made to discover and engineer compatible enzyme-IL systems and to improve our understanding on the effect that these ILs have on these systems. This review seeks to examine the impact of ionic liquids on enzymes involved in lignocellulosic biomass deconstruction, with a specific focus on their relevance in the context of pretreatment. Beyond presenting an overview of the ionic liquid pretreatment landscape, we outline the main factors that influence enzyme activity and stability in the presence of ILs This data is consolidated and analyzed to apply this body of knowledge towards new innovations that could lead to improvements in the processing of biomass to biofuels and bioproducts.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Pyrolyzed Substrates Induce Aromatic Compound Metabolism in the Post-fire Fungus, Pyronema domesticum

Wildfires represent a fundamental and profound disturbance in many ecosystems, and their frequency and severity are increasing in many regions of the world. Fire affects soil by removing carbon in the form of CO2 and transforming remaining surface carbon into pyrolyzed organic matter (PyOM). Fires also generate substantial necromass at depths where the heat kills soil organisms but does not catalyze the formation of PyOM. Pyronema species strongly dominate soil fungal communities within weeks to months after fire. However, the carbon pool (i.e., necromass or PyOM) that fuels their rise in abundance is unknown. We used a Pyronema domesticum isolate from the catastrophic 2013 Rim Fire (CA, United States) to ask whether P. domesticum is capable of metabolizing PyOM. Pyronema domesticum grew readily on agar media where the sole carbon source was PyOM (specifically, pine wood PyOM produced at 750°C). Using RNAseq, we investigated the response of P. domesticum to PyOM and observed a comprehensive induction of genes involved in the metabolism and mineralization of aromatic compounds, typical of those found in PyOM. Lastly, we used 13 C-labeled 750°C PyOM to demonstrate that P. domesticum is capable of mineralizing PyOM to CO 2 . Collectively, our results indicate a robust potential for P. domesticum to liberate carbon from PyOM in post-fire ecosystems and return it to the bioavailable carbon pool.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

Convolutional Neural Network for Segmenting Micro-X-ray Computed Tomography Images of Wood Cellular Structures

To further enhance the performance of wood products, improved tools are needed to study in situ cellular scale phenomena like mechanical deformations and moisture swelling. Micro-X-ray computed tomography (μXCT) using brilliant synchrotron light sources now has the spatial and temporal resolution for real-time visualization of phenomena in three-dimensional cellular structures. However, the tradeoff for speed includes the loss of intensity contrast between different types of materials within the imaged structure, such as cell wall and air in wood. This loss of contrast prevents traditional histogram-based segmentation methods from being used effectively. A new convolutional neural network (CNN) approach was therefore developed to segment fast μXCT images of wood into cell wall and air volumes. The fast μXCT and segmentation were demonstrated in the study of moisture swelling in loblolly pine (Pinus taeda) earlywood and latewood cellular structures conditioned at 0%, 33%, 75%, and 95% relative humidity (RH). The CNN segmentation results had a mean intersection over union (IoU) metric accuracy of 96%. Initial analysis of the swelling in the latewood revealed cell walls swelled about 25% when conditioned from 0% to 95% RH. Additionally, the widths of ray cell lumina in the transverse plane of latewood could be observed to increase at higher RH. The segmentation method presented here will facilitate future quantitative analyses in in situ μXCT studies of wood and other similar cellular materials.

Arzola-Villegas, Xavier (ORCID:0000000305369766)↗

DOE OSTI2024

Stand Age and Climate Change Effects on Carbon Increments and Stock Dynamics

Carbon assimilation and wood production are influenced by environmental conditions and endogenous factors, such as species auto-ecology, age, and hierarchical position within the forest structure. Disentangling the intricate relationships between those factors is more pressing than ever due to climate change’s pressure. We employed the 3D-CMCC-FEM model to simulate undisturbed forests of different ages under four climate change (plus one no climate change) Representative Concentration Pathways (RCP) scenarios from five Earth system models. In this context, carbon stocks and increment were simulated via total carbon woody stocks and mean annual increment, which depends mainly on climate trends. We find greater differences among different age cohorts under the same scenario than among different climate scenarios under the same age class. Increasing temperature and changes in precipitation patterns led to a decline in above-ground biomass in spruce stands, especially in the older age classes. On the contrary, the results show that beech forests will maintain and even increase C-storage rates under most RCP scenarios. Scots pine forests show an intermediate behavior with a stable stock capacity over time and in different scenarios but with decreasing mean volume annual increment. These results confirm current observations worldwide that indicate a stronger climate-related decline in conifers forests than in broadleaves.

Forestry↗

DOE OSTI2023

Transcriptomics of Temporal- versus Substrate-Specific Wood Decay in the Brown-Rot Fungus Fibroporia radiculosa

Brown-rot fungi lack many enzymes associated with complete wood degradation, such as lignin-attacking peroxidases, and have developed alternative mechanisms for rapid wood breakdown. To identify the effects of culture conditions and wood substrates on gene expression, we grew Fibroporia radiculosa in submerged cultures containing Wiley milled wood (5 days) and solid wood wafers (30 days), using aspen, pine, and spruce as a substrate. The comparative analysis revealed that wood species had a limited effect on the transcriptome: <3% of genes were differentially expressed between different wood species substrates. The comparison between gene expression during growth on milled wood and wood wafer conditions, however, indicated that the genes encoding plant cell wall-degrading enzymes, such as glycoside hydrolases and peptidases, were activated during growth on wood wafers, confirming previous reports. On the other hand, it was shown for the first time that the genes encoding Fenton chemistry enzymes, such as hydroquinone biosynthesis enzymes and oxidoreductases, were activated during submerged growth on ground wood. This illustrates the diversity of wood-decay reactions encoded in fungi and activated at different stages of this process.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

Utilization of Synthetic Near-Infrared Spectra via Generative Adversarial Network to Improve Wood Stiffness Prediction

Near-infrared (NIR) spectroscopy is widely used as a nondestructive evaluation (NDE) tool for predicting wood properties. When deploying NIR models, one faces challenges in ensuring representative training data, which large datasets can mitigate but often at a significant cost. Machine learning and deep learning NIR models are at an even greater disadvantage because they typically require higher sample sizes for training. In this study, NIR spectra were collected to predict the modulus of elasticity (MOE) of southern pine lumber (training set = 573 samples, testing set = 145 samples). To account for the limited size of the training data, this study employed a generative adversarial network (GAN) to generate synthetic NIR spectra. The training dataset was fed into a GAN to generate 313, 573, and 1000 synthetic spectra. The original and enhanced datasets were used to train artificial neural networks (ANNs), convolutional neural networks (CNNs), and light gradient boosting machines (LGBMs) for MOE prediction. Overall, results showed that data augmentation using GAN improved the coefficient of determination (R 2 ) by up to 7.02% and reduced the error of predictions by up to 4.29%. ANNs and CNNs benefited more from synthetic spectra than LGBMs, which only yielded slight improvement. All models showed optimal performance when 313 synthetic spectra were added to the original training data; further additions did not improve model performance because the quality of the datapoints generated by GAN beyond a certain threshold is poor, and one of the main reasons for this can be the size of the initial training data fed into the GAN. LGBMs showed superior performances than ANNs and CNNs on both the original and enhanced training datasets, which highlights the significance of selecting an appropriate machine learning or deep learning model for NIR spectral-data analysis. The results highlighted the positive impact of GAN on the predictive performance of models utilizing NIR spectroscopy as an NDE technique and monitoring tool for wood mechanical-property evaluation. Further studies should investigate the impact of the initial size of training data, the optimal number of generated synthetic spectra, and machine learning or deep learning models that could benefit more from data augmentation using GANs.

59 BASIC BIOLOGICAL SCIENCES↗