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Operational inefficiencies due to wide variability in physical and compositional attributes characteristic of existing corn stover supply systems threaten the development of a sustainable bioeconom...

Forest and biomass utilization is of great significance in the context of global change toward renewable energy and products. Supply chain management has been proven as an effective path to improve economic and environmental performance of forest and biomass products. However, the existing studies are fragmented and task-oriented. In this paper, we aim to systematically form a data-oriented modeling and analytics summary including the framework and toolbox for enhancement of forest and biomass supply chain management. With the coming of the big data era, data analytics methods and tools for forest and biomass supply chain management are progressively updated. Conventional analytical methods are facing challenges, though most of them are still currently dominant in practice. The big data era provides promising opportunities for data-driven methods and tools, based on which the data-oriented modeling framework is gradually emerging. Generally, rapid development of computing capability and algorithms greatly facilitates the application and accuracy of the simulations and optimization, while built-in databases play an important role in the data analytics. Macro-enabled spreadsheet-based models and tools are still popularly used in practices due to their ease-of-use. Specifically, development of advanced techniques improves upon the efficiency and accuracy of conventional time-motion study approaches. A statistically appropriate experimental design and the underlying assumption verification are essential to obtaining reliable results. Traditionally, suitability modeling has been the trusted approach for optimal facility siting, but the emerging probability modeling is a promising data-driven approach in the context of the big data era. Linear programming modeling is still dominating in the supply chain optimization, while non-linear programming modeling is emerging due to the rapid development of algorithm and computational capacity. Techno-economic analysis (TEA) and life cycle assessment (LCA) provide important results regarding the economic and environmental efficiency of various forest and biomass supply chain systems. However, TEA and LCA results can vary due to modeling approach, data availability, and differences among underlying methods and databases. Monte Carlo simulation could be a fundamental approach to examining the uncertainty issues in both TEA and LCA.

Agricultural waste was used as substrate for fermentation by Gluconobacter oxydans to produce lixiviant for rare earth element (REE) recovery from industrial waste materials, i.e., spent fluid cata...

The world is witnessing an unprecedented generation and accumulation of fiber-plastic wastes resulting in various challenges due to inconsistency, waste-stream heterogeneity, conveying issues, self-heating, and difficulty in pelletization. This study presents a novel pilot-scale system that integrates torrefaction and extrusion to convert mix fiber-plastic waste into fuel pellets. The produced pellets have low cost, high heating value, better uniformity, and low environmental impact. They can be used as solid fuels or as feedstock for pyrolysis and gasification. To evaluate the pellet cost and its environmental impact, we performed Techno-Economic Analysis (TEA) and Life Cycle Assessment (LCA). The TEA integrates research findings from the torrefaction-extrusion project with the techno-economic models and estimates the costs, energy consumption, and mass balances for pelletizing and torrefaction. The analysis indicates that the baseline cost of producing uniform pellets is about $55.28/dry tonne (2020$). LCA results indicate that the torrefied product has cradle-to-gate embodied greenhouse gas emissions that are net negative, although they are higher than a comparable forest-derived woodchip product. Fossil energy demand for the torrefied product is lower than the forest-derived chip, indicating the torrefied product has strong potential for use as an environmentally beneficial feedstock for future processing.

Seasonal variation in microalgae productivity is a significant barrier to economical production of algae biofuels and chemicals. Summer production can be 3-5 times higher than in the winter resulting in uneven feedstock supplies at algae biorefineries. A portion of the summer production must be preserved for conversion in the winter in order to maintain a biorefinery running at capacity. Ensiling, a preservation process that utilizes lactic acid fermentation to limit microbial degradation, has been demonstrated to successfully stabilize algae biomass (20% solids) and algae-lignocellulosic blends (40% algae-60% lignocellulosic biomass, dry basis) for over six months, resulting in fuel production cost savings with fewer emissions. Preservation of algae as blends could be beneficial to biorefineries that utilize thermochemical approaches to fuel production as co-processing of algae and lignocellulosic biomass has been observed to enhance biocrude yield and improve oil quality. This study conducts a resource assessment of biomass residues in the southern United States to identify materials available during peak algae productivity and in sufficient quantity to meet the algae storage needs of an algae biofuel industry. Eight feedstocks met the quantity threshold but only three, distillers grains, haylage, and yard waste, were also available in season. Storage experiments utilizing both freshwater and marine strains of microalgae – Scenedesmus acutus, Chlorella vulgaris, Chlorella zofingiensis, Nannochloropsis gaditana, and Porphyridium purpureum – and yard waste were conducted for 30 days. Storage losses were less than 10% in all but one case, and the pH of all but one blend was reduced to less than 4.7, indicating that yard waste is a suitable feedstock for blending with algae prior to storage. To better understand whether the benefits to conversion realized by processing blends might be affected by storage, elemental analysis and bomb calorimetry of pre- and post-storage algae-yard waste blends were conducted to characterize changes occurring during storage. Storing algae biomass as blends with lignocellulosic biomass could be an effective method of mitigating seasonal variability in algae biomass production while retaining the synergistic effect of co-processing algae blends in thermochemical conversion.

Short rotation woody crops have many advantages as perennial bioenergy feedstocks, including high biomass yields, high carbohydrate and low ash contents, and marginal land utilization. Through short rotation coppicing management, these biomass resources can be harvested year round. The challenge of year round harvesting is feedstock quality variability due to leaf content during periods of non-senescence. The low quality leaf fraction results in higher ash and moisture contents and lower carbohydrate content. Mechanical techniques, such as air classification, provide an economically feasible process to separate heterogeneous biomass samples based on particle density, size, and shape. In this work high moisture (>45%) hybrid poplar and shrub willow short rotation crops were air classified using a series of fan speeds for anatomical fractionation of the material. Air classification using an air velocity of ∼4.7 m/s removed a majority of the leaf material while retaining 88% and 87% of the hybrid poplar and shrub willow, respectively. At this velocity, the ash content was reduced from 2.34% to 1.67% for hybrid poplar and 2.60% to 2.14% for shrub willow. Concurrently, the carbohydrate content increased from 56.32% to 60.62% and from 54.03% to 55.99% for these same materials. As drying is a cost intensive step for processing high moisture biomass materials, the cost benefits (∼$3/Mg dry biomass) for removing low quality, high moisture materials prior to drying were also demonstrated. ©Battelle Energy Alliance, LLC, contract manager for Idaho National Laboratory.

Within-field spatial variability reduces growers’ return on investment and overall productivity while potentially increasing negative environmental impacts through increased soil erosion, nutrient runoff, and leaching. The hypothesis that integrating energy crops into non-profitable segments of agricultural fields could potentially increase grain yield and biomass feedstock production was tested in this study using a statewide analysis of predominantly corn- and soy-producing counties in Iowa. Basic and rigorous controls on permissible soil and soil-carbon losses were imposed on harvest of crop residues to enhance year-to-year sustainability of crop and residue production. Additional criteria limiting harvesting costs and focus on large-area subfields for biomass production were imposed to reduce the impacts of energy crop integration on grain production. Model simulations were conducted using 4 years (2013–2016) of soil, weather, crop yield, and management practice data on all counties in Iowa. Miscanthus (Miscanthus x giganteus), switchgrass (Panicum virgatum), and crop-residue-based bioenergy feedstock systems were evaluated as biomass. Average energy crop and plant residue harvesting efficiencies were estimated at 50 and 60%, respectively. Because of higher potential yields, average logistics costs for miscanthus-based biomass production were 15 and 23% lower than switchgrass-based and crop residue-based biomass productions, respectively, under basic sustainability controls, and 17 and 26% lower under rigorous sustainability controls. Subfield shape, size, area, and harvest equipment size were the dominant factors influencing harvesting cost and efficiency suggesting that in areas where subfields are predominantly profitable or harvesting efficiencies low, other options such as prairie strips, buffer zones around fields, and riparian areas should be investigated for more profitable biomass production and sustainable farming systems.

The commercial development of biofuels and bioproducts depends on whether renewable biomass feedstock is available while not directly competing with the production of food. Farmers are one of the most important stakeholders in the biofuel supply chain and confront a range of uncertainties while entering the bioenergy market. Their decision-making process is extremely complex and rarely purely rational. Modeling farmer behavior requires considering a wide range of individual-level factors, socio-temporal dynamics, institutional settings, and their interactions. These characteristics make agent-based modeling a suitable framework for evaluating such systems. We developed a model to simulate farmer bioenergy crop adoption behavior across a 50-county study region in Nebraska, Kansas, and Colorado. The analysis considers adoption decisions for two bioenergy feedstocks, crop residues and energy crops. We examine the influence of individual and farm characteristics, market structure, social networks, and media influence on farmer adoption decisions. Our results indicate that different factors can have varied impacts on the speed of adoption for the crop residues and energy crops. Identifying levers that have the most impact on grower adoption can inform the design of interventions both from policy and private sector standpoints with important implications for the future the bioenergy industry.

The sustainability of the biofuel industry depends on the development of a mature conversion technology on a national level that can take advantage of the economies of scale: the nth-plant. Defining the future location and supply logistics of conversion plants is imperative to ultimately transform the nation’s renewable biomass resources into cost-competitive, high-performance feedstock for production of biofuels and bioproducts. Since the US has put restrictions on production levels of conventional biofuels from edible resources, the nation needs to plan for the widespread accessibility and development of the cellulosic biofuel scenario. Conventional feedstock supply systems will be unable to handle cellulosic biomass nationwide, making it essential to expand the industry with an advanced feedstock supply system incorporating a distributed network of preprocessing depots and conversion plants, or biorefineries. Current studies are mostly limited to designing supply systems for specific regions of the country. We developed a national database with potential locations for depots and biorefineries to meet the nation’s target demand of cellulosic biofuel. Blended feedstock with switchgrass and corn stover (harvested by either a two- or three-pass method) are considered in a Mixed Integer Linear Programming model to deliver on-spec biomass that considers both, a desired quantity and quality at the biorefinery. The model solves for a network of varying size depots that supply to biorefineries of 725,000 dry tons/year. A total delivered feedstock cost that is less than $79.07/dry tons (2016$) is evaluated for years 2022, 2030, and 2040. In 2022, 124 depots and 59 biorefineries could be supplied with 42.8 million dt of corn stover and switchgrass. In 2030 and 2040, the total accessible biomass could increase to 215% and 393% respectively when compared to 2022. However, an $8/dry tons reduction in targeted delivery cost could reduce total accessible biomass by 67%. Kansas, Nebraska, South Dakota and Texas were identified as potential states with a strong biofuel economy given that they had six or more biorefineries located in all scenarios. In some scenarios, Colorado, Alabama, Georgia, Minnesota, Mississippi and South Carolina would greatly benefit from a depot network as these could only deliver to a biorefinery in a nearby state. To elaborate the impact of a nationwide consideration, the findings were compared with existing literature for different US regions. We also present results for biorefinery capacities that are double, triple and quadruple in size.

The seasonal variability in algal biomass production and its susceptibility to rapid degradation increases uncertainty in algal productivity and increases risks to feedstock supply for conversion. During summer months when algal biomass productivity is highest, production could exceed conversion capacity, resulting in delayed processing and risk of biomass degradation. Drying algae for preservation is energy-intensive and can account for over 50% of the total energy demand in algae preprocessing. In comparison, wet stabilization of algae eliminates the need for drying, and blending with herbaceous biomass allows for the utilization of the silage industry's existing harvest, handling, transportation, and storage infrastructure. A storage facility co-located with the algae production and conversion operations was designed in this study to stabilize algal biomass produced in excess of conversion capacity during summer months for use in the winter when algal biomass production is reduced. Techno-economic assessment of ensiling algae and corn stover blends suggest it to be a cost effective approach compared to drying. In a high algal biomass productivity scenario, costs of wet storage ($/liter diesel) were only 65% of the cost of drying. When a reduced algal biomass productivity scenario was considered, the stored blend was able to cost-effectively provide sufficient biomass such that winter production in the algal ponds could cease, meanwhile incurring only 91% of the costs of drying; such an approach would facilitate algal biomass production in northern latitudes. Furthermore, the wet storage approaches require only 8–10% of the total energy consumption and release only 20–25% of the greenhouse gases when compared to a natural-gas based drying approach for microalgae stabilization.

A modeling process was developed to examine the economic and environmental benefits of utilizing energy crops for bioenergy products in the northeastern United States. Three energy crops (hybrid willow, switchgrass, and Miscanthus) that can potentially grow on marginal agricultural land or abandoned mine land in the region were considered in the analytical process for the production of biofuels, biopower, and pellet fuel. The supply chain components for the economic analysis and life cycle modeling processes included feedstock establishment, harvest, transportation, storage, preprocessing, conversion, distribution, and final usage of the bioenergy products. Sensitivity analysis was conducted to assess the effects of energy crop yield, transportation distance, conversion rate, facility capacity, and internal rate of return (IRR) on the production of bioenergy products. The required selling price (RSP) ranged from $7.7/GJ to $47.9/GJ for different bioenergy products. The production of biopower had the highest RSP and pellet fuel had the lowest RSP. The results also indicated that bioenergy production using hybrid willow demonstrated lower RSP than the other two perennial grasses. Pellet production had presented the lowest greenhouse gas (GHG) emissions (less than 10 kg CO2 eq per 1000 MJ) and fossil energy consumption (less than 150 MJ per 1000 MJ). The production of biofuel resulted in the highest GHG emissions. Sensitivity analysis indicated that IRR was the most sensitive factor to RSP, followed by conversion rate for biofuel and biopower production. Fuel conversion efficiency and feedstock transport distance had a significant effect on the life cycle environmental impact of the bioenergy products studied. © 2017 Society of Chemical Industry and John Wiley & Sons, Ltd

Biomass such as agricultural residues, energy crops and yard waste has significant potential to be used as renewable feedstocks for production of fuels, chemicals and energy. However, in a given location, biomass availability, cost and quality can vary markedly. Strategies to manage these traits must be identified and implemented so that consistent low-cost and high-quality feedstocks can be delivered to biorefineries year round. In this study, we examine air classification as a method to mitigate high ash concentrations in corn stover, switchgrass, and grass clippings. Formulation techniques were then used to produce blends that met ash quality and biomass quantity specifications at the lowest possible cost for biopower and biochemical conversion applications. It was found that air classification can separate the biomass into light fractions which contain concentrated amounts of elemental ash components introduced through soil contamination such as sodium, alumina, silica, iron and titania; and heavy fractions that are depleted in these components and have relatively lower total ash content. Light fractions of corn stover and grass clippings were found to be suitable for combustion applications since they had less propensity to slag than the whole biomass material. The remaining heavy fractions of corn stover or grass clippings could then be blended with switchgrass to produce blends that met the 5% total ash specifications suggested for biochemical conversions. However, ternary blends of the three feedstocks were not possible due to the high ash content of grass clippings. It was determined that air classification by itself was not suitable to prepare these feedstocks for pyrolysis due to high ash content.

Field operations planning has become critical for the operational efficiency of agricultural activities, in terms of time and cost. To address this type of planning problem, we (1) developed a modeling capability built using a Discrete Event Simulation (DES) approach and object-oriented prgramming, and (2) propose a methodology extending field data to allow spatial operations on various geometric types. The simulation model developed using the Discrete Event Specification System (DEVS) formalism, captures the equipment characteristics and tracks the evolution of its movement across the field via state transition tables. The model is data driven and spatially and temporally detailed, which are key features to accurately estimate field efficiency. Our modeling approach offers more rigor in the component behavior definition and more flexibility enabling the handling of fields and equipment of various shapes and physical characteristics, respectively. The validity of the model was tested, with an error margin of 15%, by comparing its performance with empirical data collected from switchgrass harvesting equipment.

A mixed-integer linear programming model was developed to optimize the multiple biomass feedstock supply chains, including feedstock establishment, harvest, storage, transportation, and preprocessing. The model was applied for analyses of multiple biomass feedstocks at county level for 13 states in the northeastern United States. In the base case with a demand of 180,000 dry Mg/year of biomass, the delivered costs ranged from $67.90 to $86.97 per dry Mg with an average of $79.58/dry Mg. The biomass delivered costs by county were from $67.90 to 150.81 per dry Mg across the northeastern U.S. Considered the entire study area, the delivered cost averaged $85.30/dry Mg for forest residues, $84.47/dry Mg for hybrid willow, $99.68 for switchgrass and $97.87 per dry Mg for Miscanthus. Seventy seven out of 387 counties could be able to deliver biomass at $84 per dry Mg or less a target set by US DOE by 2022. A sensitivity analysis was also conducted to evaluate the effects of feedstock availability, feedstock price, moisture content, procurement radius, and facility demand on the delivered cost. Our results showed that procurement radius, facility capacity, and forest residue availability were the most sensitive factors affecting the biomass delivered costs.

Perennial grasses can serve as a material for bioenergy, biomaterials, animal feed or bedding, or other purposes. Researchers suggest that planting perennial grasses on marginal parts of agricultural fields can provide sustainability benefits; however, planting the marginal areas may result in complex shapes and changes to field areas and thus be difficult to harvest. Harvesting costs can be a limiting factor in widely adopting perennial grasses, so the more efficient the machinery operations are, the more viable perennial crops become for farmers and bioenergy stakeholders. This study explores the relationship between field shape and size and empirically derived harvesting efficiency to support assumptions relative to predicting harvesting cost, greenhouse gas (GHG) emissions, labor demands, and other factors impacting willingness to cultivate energy crops, which can be impacted by field efficiency. Switchgrass (Panicum virgatum L.) mowing data were used in the study. The regression analysis showed that the natural log transformation of the perimeter-to-area (P/A) ratio was the most accurate predictor for field efficiency (FE) (R2 = 71%) among shape descriptors as it takes both field size and shape into account. Optimizing field designs that combine annual crops and perennial grasses for lower P/A ratios will decrease operation costs and, thus, improve the rate of practice adoption of perennial grasses in agriculture.

Process economics and greenhouse gas (GHG) emissions are evaluated for the conversion of eleven (11) biomass feedstocks to produce transportation fuels via fast pyrolysis and then pyrolysis oil upgrading via hydrodeoxygenation. The biomass consists of six (6) pure feeds (pine, tulip poplar, hybrid poplar, switchgrass, corn stover, oriented strand board) and five (5) blends. Feedstock blends combine pine, tulip poplar, hybrid poplar, switchgrass, and oriented strand board in various ratios. The assumptions used in this analysis are based on a unique experimental data set. They are “field to wheel” data generated from a multistep experiment that started from biomass preparation and was followed by biomass feedstock conversion to pyrolysis oil, and then finished by pyrolysis oil upgrading to produce hydrocarbon liquid fuel. Process economic results are reported in US dollars per gasoline gallon equivalent (gge) at the minimum fuel selling price (MFSP). For all eleven feedstocks, the largest cost contribution to MFSP is capital-related costs, which is approximately 30%–40% of the MFSP and includes capital depreciation, income tax, and return on investment. Feedstock cost (approximately 30% of MFSP), hydrotreating catalyst cost (13%–18% of MFSP) and labor cost (12%–15% of MFSP) also contribute significant cost to MFSP. Final fuel product yield is one of the most significant parameters affecting the process economics. The highest product yield is from pine feedstock, while switchgrass feedstock gives the lowest yield. Using blends (woody mixed with herbaceous), with which the product yield is still reasonable, may further improve process economics compared to pure feedstocks because of the lower feedstock price for blends. Blend feedstocks cost less than pure feedstock because of greater diversity in feedstock choices, lower risk, and lower transportation costs. GHG reduction does not necessarily align with favorable economics. Process parameters that most significantly affect the life cycle GHG emissions (from feedstock production to vehicle operation) are (1) natural gas used for hydrogen production and electricity usage in fast pyrolysis and pyrolysis oil upgrading, (2) energy usage in the feedstock harvesting, transport, and preprocessing operations, and (3) nitrogen fertilizer usage for growing biomass.