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Flow diagram of the 2012 Relocated (Baseline) Design Case to supply thermochemical conversion refineries (Searcy et al. 2012).
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Context 1
... the case of this data, a feedstock delivered at -50% relative ash content to the baseline (2.5% ash) would in essence qualify for a 3.16 $/dry T bonus on top of the baseline feedstock purchase price of 58.50 $/dry T. While this type of system has not yet been proposed for a commercial system, this type of financial motivation may provide further incentive for farmers to invest in single-pass technology as the material generated is truly of higher value to a conversion facility. Figure B2. Sensitivity of dockage to altering feedstock price relative to the base case of 58.50 $/dry T for a delivered feedstock with 10% ash . ...
Context 2
... demonstrate the importance of a proper feedstock procurement cost, the analysis was conducted over a range of feedstock prices, from 29.25 $/dry T to 87.75 $/dry T (-50% to 50% change, respectively) for a delivered feedstock with 10% ash ( Figure B2). The results clearly show the impact of feedstock price on off-spec dockage and ultimately total dockage, increasing the total dockage by 0.03 $/% relative change. ...
Similar publications
Citations
... Works that focus on evaluating the design parameters of the equipment used cover most of the literature related to biorefinery operations [32,33]. Other studies analyze the energy consumption of equipment as a function of its design parameters and biomass characteristics [34][35][36]. These studies are limited in scope since they do not capture the interactions between equipment and the impact of equipment on the system's performance. ...
Variations of physical and chemical characteristics of biomass reduce equipment utilization and increase operational costs of biomass processing. Biomass processing facilities use sensors to monitor the changes in biomass characteristics. Integrating sensory data into the operational decisions in biomass processing will increase its flexibility to the changing biomass conditions. In this paper, we propose a multi-stage stochastic programming model that minimizes the expected operational costs by identifying the inventory level and creating an operational decision policy for equipment speed settings. These policies take the sensory information data and the current biomass inventory level as inputs to dynamically adjust inventory levels and equipment settings according to the changes in the biomass’ characteristics. We ensure that a prescribed target reactor utilization is consistently achieved by penalizing the violation of the target reactor feeding rate. A case study is developed using real-world data collected at Idaho National Laboratory’s biomass processing facility. We show the value of multi-stage stochastic programming from an extensive computational experiment. Our sensitivity analysis indicates that updating the infeed rate of the system, the processing speed of equipment, and bale sequencing based on the moisture level of biomass improves the processing rate of the reactor and reduces operating costs.
... The TCI for a biorefinery which utilizes hydrolysate catalysis technology (biochemical distributions) is expected to be $771,732,000 USD (Davis et al. 2013) with an EAC of $123,293,039 USD. For a biorefinery which utilizes pyrolysis technology (thermochemical distributions), the expected TCI is $819,702,000 USD (Jones et al. 2013) with an EAC of $130,956,797 USD (assuming r =15%, t=20). The production capacity and conversion yield of the hydrolysate catalysis technology are 298,339,519 mg/year and 454 L/mg, respectively (Davis et al. 2013), whereas for the pyrolysis technology, the production capacity is 152,063,705 mg/year, and the conversion yield is 232 L/mg (Jones et al. 2013). ...
... For a biorefinery which utilizes pyrolysis technology (thermochemical distributions), the expected TCI is $819,702,000 USD (Jones et al. 2013) with an EAC of $130,956,797 USD (assuming r =15%, t=20). The production capacity and conversion yield of the hydrolysate catalysis technology are 298,339,519 mg/year and 454 L/mg, respectively (Davis et al. 2013), whereas for the pyrolysis technology, the production capacity is 152,063,705 mg/year, and the conversion yield is 232 L/mg (Jones et al. 2013). ...
A feasible alternative to the production of fossil fuels is the production of biofuels. In order to minimize the costs of producing biofuels, we developed a stochastic programming formulation that optimizes the inbound delivery of biomass. The proposed model captures the variability in the moisture and ash content in the biomass, which define its quality and affect the cost of biofuel. We propose a novel hub-and-spoke network to take advantage of the economies of scale in transportation and to minimize the effect of poor quality. The first-stage variables are the potential locations of depots and biorefineries, and the necessary unit trains to transport the biomass. The second-stage variables are the flow of biomass between the network nodes and the third-party bioethanol supply. A case study from Texas is presented. The numerical results show that the biomass quality changes the selected depot/biorefinery locations and conversion technology in the optimal network design. The cost due to poor biomass quality accounts for approximately 8.31\(\%\) of the investment and operational cost. Our proposed L-shaped with connectivity constraints approach outperforms the benchmark L-shaped method in terms of solution quality and computational effort by 0.6\(\%\) and 91.63\(\%\) on average, respectively.
... Bio-oil or pyrolysis oil is the result of rapid heating and quenching of biomass in the absence of air to form a liquid. Fast pyrolysis oil compounds contain up to 40% oxygen by weight and acidic and reactive compounds [17,18]. IP is performed at slower heating rates compared to FP and can produce more thermally stable bio-oil with less oxygen and acidic content [19][20][21]. ...
... FP bio-oil is unstable and tends to deteriorate over time due to polymerization and condensation [60,61]. To avoid detrimental effects on the generated oil, the bio-oil is immediately sent to hydro-processing units, where it undergoes dual-stage hydrotreating followed by hydrocracking to treat the highly corrosive and oxygenated raw FP bio-oil, as shown in Fig. 3. Other studies investigating pathways to convert raw FP bio-oil to transportation fuels have suggested the use of similar hydroprocessing units [18,27,35]. First, mild hydrotreating, at 240 • C and 150 atm pressure in the presence of a sulfided nickel- molybdenum catalyst, is performed to remove acidic compounds and stabilize the oil for further upgrading. ...
The conversion of municipal solid waste (MSW) to transportation fuels can be an attractive route to reduce greenhouse gas emissions from the transportation and municipal sectors. Thermochemical conversion routes like hydrothermal liquefaction (HTL), fast pyrolysis (FP), and intermediate pyrolysis (IP) have been shown to be adept at converting organic dominant MSW into bio-crude or bio-oil. However, to produce compatible transportation grade fuels, it is necessary to upgrade the intermediate product (bio-crude or bio-oil) from all the processes, the extent of which differs depending on the process. Moreover, depending on the conversion technique, the production configuration can be either centralized or decentralized. In a centralized system, feed is transported to a facility to produce the intermediate and upgrade it (on-site upgrading), while in a decentralized system, the intermediate is produced elsewhere and transported to an upgrading facility (off-site upgrading).. Four scenarios were developed and modeled to compare the cost of production of gasoline, diesel and jet fuel from bio-crudes produced from HTL, FP, and IP.. The scenarios are: 1) a centralized HTL plant (C-HTL); 2000 dry t per day; on-site upgrading, 2) a centralized FP plant (C-FP); 2000 dry t per day; on-site upgrading, 3) a decentralized FP plant (D-FP); 50 dry t per day; off-site upgrading, and 4) a decentralized IP plant; 12 dry t per day; off-site upgrading.. Jet fuel was the primary fuel for comparison and the production costs were calculated to be $ 0.72, $ 0.85, $ 1.04, and $ 0.81 per liter for the C-HTL, the C-FP, the D-FP, and the D-IP plants, respectively. Secondary products (gasoline and diesel) can be produced alongside in cost ranges of $ 0.97 - $ 1.40 per liter and $ 1.02 - $ 1.47 per liter, respectively. The information conveyed in this study helps to identify the potential of thermochemical conversion processes to produce transportation fuels at competitive prices. The critical barriers to adopt such large-scale production processes and the opportunities of small-scale decentralized production are also mentioned. The outcomes of this study can be used to direct research and investment to address the major roadblocks that are slowing the extensive development of these technologies.
... Most of the literature related to biorefinery operations focuses on the evaluation of design parameters of the equipment used [11,13]. Other studies analyze energy consumption of an equipment as a function of its design parameters and biomass characteristics [21,22,41]. These studies are limited in scope since they do not capture the interactions among equipment and the impact of equipment on the performance of the system as a whole. ...
Variations of physical and chemical characteristics of biomass lead to an uneven flow of biomass in a biorefinery, which reduces equipment utilization and increases operational costs. Uncertainty of biomass supply and high processing costs increase the risk of investing in the US’s cellulosic biofuel industry. We propose a stochastic programming model to streamline processes within a biorefinery. A chance constraint models system’s reliability requirement that the reactor is operating at a high utilization rate given uncertain biomass moisture content, particle size distribution, and equipment failure. The model identifies operating conditions of equipment and inventory level to maintain a continuous flow of biomass to the reactor. The sample average approximation method approximates the chance constraint and a bisection search-based heuristic solves this approximation. A case study is developed using real-life data collected at Idaho National Laboratory’s biomass processing facility. An extensive computational analysis indicates that sequencing of biomass bales based on moisture level, increasing storage capacity, and managing particle size distribution, increases utilization of the reactor and reduces operational costs.
... Genotype 185 had a lower ash content (1.5%) than variety 339 (2.0%), both belonging to the same taxa (TD), whereas genotype 230 (DM) has the highest ash content (2.1%). It is worth noting that the ash content of all four genotypes were higher than the recommended ash content (less than 1%) for thermochemical conversion by the Department of Energy [42]. The higher bark-wood ash contents (Table 3) compared to the wood-alone ash contents (Table 5) are consistent with the fact that ash content is higher in bark than it is in wood, therefore increasing ash contents would coincide with increasing bark contents of the samples. ...
Lignocellulosic biomass is an alternative source of energy that can reduce our dependency on fossil fuels and limit greenhouse gas emissions. Several techno-economic analyses have consistently shown that all the steps in biomass-to-bioproduct processes needs improvement. Simultaneous assessment of genotypes for multiple productivity characteristics and integrating information across production stages has seldom been the focus of research efforts. To address this gap, we first determined the agronomic performance of 10 poplar genotypes. Differences between genotypes in height, diameter at breast height (DBH), tree mass and yield were consistently observed. Correlation analyses revealed that height and DBH are positively correlated with tree mass and yield, whereas bark content is negatively correlated with tree mass, yield and disease incidence. Four highest-yielding genotypes were subjected to proximate, ultimate, targeted chemical analyses, along with assessment of sugar production by acid hydrolysis and enzymatic saccharification. Despite having only marginal changes in overall chemistry, the genotypes showed differential conversion efficiencies of enzymatic saccharification. Interestingly, the genotype that showed highest cellulose conversion efficiency had the lowest estimated sugar yields due to its low biomass yield, whereas the genotype with lowest conversion efficiency had the highest estimated sugar yields. These results show the importance of integrating information across the stages of biomass production and bioconversion. These results also demonstrate the complexity of biomass feedstock production and the need for future studies to assess whether these tradeoffs can be genetically separated to guide the selection of genotypes that can maximize the overall biomass feedstock production efficiency.
... This resource is currently underutilized as it is either left on-site or much less commonly burned for energy recovery [34,35]. There has been a growing interest in converting forest residues to biofuels to enhance the efficient utilization of forest resources, reduce the risks of forest wildfire, and bring additional revenue to landowners [36][37][38][39]. A few studies have conducted LCA for forest residue-derived biofuel and indicated a significant reduction (36-67%) of life-cycle GHG emissions compared to conventional fuels [10,14,15,32,33]. ...
Background
Woody biomass has been considered as a promising feedstock for biofuel production via thermochemical conversion technologies such as fast pyrolysis. Extensive Life Cycle Assessment studies have been completed to evaluate the carbon intensity of woody biomass-derived biofuels via fast pyrolysis. However, most studies assumed that woody biomass such as forest residues is a carbon–neutral feedstock like annual crops, despite a distinctive timeframe it takes to grow woody biomass. Besides, few studies have investigated the impacts of forest dynamics and the temporal effects of carbon on the overall carbon intensity of woody-derived biofuels. This study addressed such gaps by developing a life-cycle carbon analysis framework integrating dynamic modeling for forest and biorefinery systems with a time-based discounted Global Warming Potential (GWP) method developed in this work. The framework analyzed dynamic carbon and energy flows of a supply chain for biofuel production from pine residues via fast pyrolysis.
Results
The mean carbon intensity of biofuel given by Monte Carlo simulation across three pine growth cases ranges from 40.8–41.2 g CO 2 e MJ ⁻¹ (static method) to 51.0–65.2 g CO 2 e MJ ⁻¹ (using the time-based discounted GWP method) when combusting biochar for energy recovery. If biochar is utilized as soil amendment, the carbon intensity reduces to 19.0–19.7 g CO 2 e MJ ⁻¹ (static method) and 29.6–43.4 g CO 2 e MJ ⁻¹ in the time-based method. Forest growth and yields (controlled by forest management strategies) show more significant impacts on biofuel carbon intensity when the temporal effect of carbon is taken into consideration. Variation in forest operations and management (e.g., energy consumption of thinning and harvesting), on the other hand, has little impact on the biofuel carbon intensity.
Conclusions
The carbon temporal effect, particularly the time lag of carbon sequestration during pine growth, has direct impacts on the carbon intensity of biofuels produced from pine residues from a stand-level pine growth and management point of view. The carbon implications are also significantly impacted by the assumptions of biochar end-of-life cases and forest management strategies.
... Additionally, large variations in feedstock quality (e.g., moisture content, carbon content, ash content) often challenge the conversion economics in a large-scale biorefinery [15][16][17]. Previous studies have proposed two alternative strategies to address those challenges, blending feedstocks [18][19][20] and using decentralized preprocessing facilities [9,14,19,21]. Assessing the economic effectiveness of these two strategies is an essential first step for the development of commercial biorefineries. These alternative feedstock supply scenarios also create an opportunity for increasing the scale of the biorefinery, and thus take advantage of lower capital costs per annual gallon of biofuel. ...
... In this study, two depot preprocessing technologies were explored, conventional pelleting process (CPP), and high moisture pelleting process (HMPP) that was first proposed by the Idaho National Laboratory (INL) [21] and Lamers et al. [9]. Fig. 3 shows the process flows of CPP and HMPP technologies investigated in this study for 100% pine residues (baseline) and blended biomass feedstock (pine residues and switchgrass with various blended ratios). ...
... However, pre-drying is not needed when the depot uses 25% pine residues and 75% switchgrass, as the moisture content of the blended feedstock, 29% (wb), is already below the 30% target. The HMPP uses a two-stage grinding process based on fractional milling [21]. For pine residues, the first-stage grinding to 152 mm (6 inches) is conducted in the forest. ...
This study evaluated the economic feasibility of fast pyrolysis biorefineries fed with blended pine residues and switchgrass in the Southeastern U.S. with different supply chain design. Previous techno-economic analyses (TEA) have focused on either blended biomass or decentralized preprocessing without investigating the impacts of varied process parameters, technology options, and real-world biomass distribution. This study fills the literature gap by modeling scenarios for different biomass blending ratios, biorefinery and preprocessing site (so-called depot) capacities, and alternative preprocessing technologies. High-resolution, real-world geospatial data were analyzed using Geographic Information Systems to facilitate supply chain design and TEA. For a decentralized system, the minimum fuel selling price (MFSP) of biofuel was $3.92–$4.33 per gallon gasoline equivalent (GGE), while the MFSP for the centralized biorefinery at the same capacities ranged between $3.75–$4.02/GGE. Implementing a high moisture pelleting process depot rather than a conventional pelleting process lowered the MFSP by $0.03–$0.17/GGE. Scenario analysis indicated decreased MFSP with increasing biorefinery capacities but not necessarily with increasing depot size. Medium-size depots (500 OMDT/day) achieved the lowest MFSP. This analysis identified the optimal blending ratios for two preprocessing technologies at varied depot sizes. Counterintuitively, increasing the proportion of higher cost switchgrass reduced the MFSP for large biorefineries (>5000 ODMT/day), but increased the MFSP for small biorefineries (1000–2500 ODMT/day). Although the decentralized systems have a higher MFSP based on current analysis, it has other potential benefits such as mitigated supply chain risks and improved feedstock quality that are difficult to be quantified in this TEA.
... Most of the literature related to biorefinery operations focuses on the evaluation of design parameters of the equipment used [11,13]. Other studies analyze energy consumption of an equipment as a function of its design parameters and biomass characteristics [21,22,41]. These studies are limited in scope since they do not capture the interactions among equipment and the impact of equipment on the performance of the system as a whole. ...
... -Constraints (21), (22), (27), and (32) represent the flow calculations for processing and storage equipment. These constraints correspond to constraints (10) in the succinct formulation (P ) in the main document. ...
... These constraints correspond to constraints (10) in the succinct formulation (P ) in the main document. For example, constraints (22) calculate the biomass flow with respect to the moisture and dry matter losses during the grinding process. ...
Variations of physical and chemical characteristics of biomass lead to an uneven flow of biomass in a biorefinery, which reduces equipment utilization and increases operational costs. Uncertainty of biomass supply and high processing costs increase the risk of investing in the US's cellulosic biofuel industry. We propose a stochastic programming model to streamline processes within a biorefinery. A chance constraint models system's reliability requirement that the reactor is operating at a high utilization rate given uncertain biomass moisture content, particle size distribution, and equipment failure. The model identifies operating conditions of equipment and inventory level to maintain a continuous flow of biomass to the reactor. The Sample Average Approximation method approximates the chance constraint and a bisection search-based heuristic solves this approximation. A case study is developed using real-life data collected at Idaho National Laboratory's pilot biomass processing facility. An extensive computational analysis indicates that sequencing of biomass bales based on moisture level, increasing storage capacity, and managing particle size distribution increase utilization of the reactor and reduce operational costs.
... Experience from the pellet industry has shown that high-density pellets alleviate many of these problems Tumuluru, 2019). Blending biomass feedstocks and using decentralized preprocessing sites (socalled depots) can also reduce the delivered cost and increase overall feedstock quality (Edmunds et al., 2018;Kenney et al., 2014;Kenney et al., 2013;Thompson et al., 2013;Wells et al., 2016). At a decentralized depot, biomass feedstocks are preprocessed to produce flowable pellets that have a uniform moisture content (MC) and composition, which can then be transported to the biorefinery for biofuel production . ...
... In this study, the high moisture pelleting process (HMPP), as proposed by the U.S. Idaho National Laboratory (INL) (Kenney et al., 2014) and , was used as the preprocessing technology in the depot. These studies report energy and cost savings relative to the conventional pelleting process . ...
... There are two common methods for lowering the pellet ash content. One is to reduce entrained ash by adopting feedstock pre-selection, best management practices in harvesting (e.g., optimal cut height), collecting, handling, and onsite trommel screening (Kenney et al., 2013(Kenney et al., , 2014. The other one is controlling structural ash by selecting the plant maturity for harvest and choosing proper fertilization practices (Adler et al., 2006;Davidsson et al., 2002). ...
This study performed Techno-Economic Analysis and Monte Carlo simulations (MCS) to explore the effects of variations in biomass feedstock quality on the economic feasibilities of fast pyrolysis biorefineries that use decentralized preprocessing sites (so-called depots which produce pellets). Two biomass resources that could be grown in the Southeastern U.S., i.e., pine residues and switchgrass were studied as feedstocks. To more fully understand the impacts, a scenario analysis was conducted for an array of different combinations, including different pellet ash control levels, feedstock blending ratios, different biorefinery capacities, and different biorefinery on-stream capacities, and compared with the traditional centralized system. The MCS results show that with depot preprocessing, variations of the feedstock moisture and ash content of the feedstock could be significantly reduced compared to the traditional centralized system. For a centralized biorefinery operating at 100% designed capacity, the minimum fuel selling price (MFSP) of the decentralized system is $3.97–$4.39 per gallon gasoline equivalent (GGE) by mean value across all scenarios, while the mean MFSP for the traditional centralized system was $3.79–$4.12/GGE. To understand the potential benefits of using highly flowable pellets to decrease biorefinery downtime due to feedstock handling and plugging problems, this study also compares the MFSP of the decentralized system at 90% of designed capacity with a traditional system at 80%. Overall, this work identified the benefits of choosing using low ash pellets made from switchgrass and pine residues to generate a low and stable MFSP. For example, a biorefinery designed for 2,000 oven dry metric ton/day running on a blended pellet made from 75% switchgrass/25% pine residues, and with 2% ash level and operating at 90% of designed capacity leads to an MFSP between $4.49–$4.71/GGE compared to a traditional centralized biorefinery operating at 80% of designed capacity which give an MFSP between $4.72–$5.28.
... In this study, two depot capacities of 250 and 500 ODT per day with three biorefinery capacities (1000, 1500, and 2000) were explored in the scenario analysis (see Section 2.2). Two alternative preprocessing systems for the decentralized depots were identified based on studies from the US Department of Energy (US DOE) Idaho National Laboratory (INL) [60] and Lamers et al. [9] The first alternative was the conventional pelleting process (CPP), one of the most commonly used preprocessing technologies in the biomass industry. [9] The second alternative was the high-moisture pelleting process (HMPP), which is an emerging technology and expected to have higher energy efficiency. ...
... mm in hammer mills. [60] Then, the biomass was densified in pellet mills at an MC of 9% (wet basis). The density of final product pellets usually ranges from 550 to 700 kg m À3 . ...
... The energy consumption of each unit process in the CPP was calculated by the thermodynamic models developed by the authors and is documented in Section 5.1 and 5.3, Supporting Information. Other LCI data were collected from the literature [9,[60][61][62][63][64][65][66][67] and documented in Section 5.4, Supporting Information. ...
Blending biomass feedstock is a promising approach to mitigate supply chain risks that are common challenges for large‐scale biomass utilization. Understanding the potential environmental benefits of biofuels produced from blended biomass and identifying driving parameters are critical for the supply chain design. Herein, a cradle‐to‐gate life cycle analysis model for fast pyrolysis biorefineries converting blended feedstocks (pine residues and switchgrass) with traditional centralized and alternative decentralized preprocessing sites, so‐called depots, is explained. Different scenarios are developed to investigate the impacts of parameters such as feedstock blending ratios, biorefinery and depot capacities, preprocessing technologies, and allocation methods. The life‐cycle energy consumption and global warming potential (GWP) of biofuel production with depots vary between 0.7–1.1 MJ MJ−1 and 43.2–76.6 g CO2 eq. MJ−1, respectively. The results are driven by biorefinery processes and depot preprocesses. A decentralized design reduces the energy consumption of the biorefinery but increases the overall life‐cycle energy and GWP. Such increases can be significantly mitigated by increasing switchgrass content as the energy consumption at the depot is driven largely by the higher moisture content of pine feedstocks. Allocation methods also have a large impact on the results but do not change the major trends and overall conclusions.
