• Energy Research

AbstractThis study performs techno‐economic analysis and Monte Carlo simulations (MCS) to explore the effects that variations in biomass feedstock quality have on the economic feasibility of fast pyrolysis biorefineries using decentralized preprocessing sites (i.e., depots that produce pellets). Two biomass resources in the Southeastern United States, that is, pine residues and switchgrass, were examined as feedstocks. 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, followed by a comparison with the traditional centralized system. MCS results show that, with depot preprocessing, variations in the feedstock moisture and feedstock ash content can be significantly reduced compared with a traditional centralized system. For a biorefinery operating at 100% of its designed capacity, the minimum fuel selling price (MFSP) of the decentralized system is $3.97–$4.39 per gallon gasoline equivalent (GGE) based on the mean value across all scenarios, whereas the mean MFSP for the traditional centralized system was $3.79–$4.12/GGE. To understand the potential benefits of highly flowable pellets in decreasing biorefinery downtime due to feedstock handling and plugging problems, this study also compares the MFSP of the decentralized system at 90% of its designed capacity with a traditional system at 80%. The analysis illustrates that using low ash pellets mixed with switchgrass and pine residues generates a more competitive MFSP. Specifically, for a biorefinery designed for 2,000 oven dry metric ton per day, running a blended pellet made from 75% switchgrass and 25% pine residues with 2% ash level, and operating at 90% of designed capacity could make an MFSP between $4.49 and $4.71/GGE. In contrast, a traditional centralized biorefinery operating at 80% of designed capacity marks an MFSP between $4.72 and $5.28.

AbstractBioenergy is widely considered a sustainable alternative to fossil fuels. However, large‐scale applications of biomass‐based energy products are limited due to challenges related to feedstock variability, conversion economics, and supply chain reliability. Artificial intelligence (AI), an emerging concept, has been applied to bioenergy systems in recent decades to address those challenges. This paper reviewed 164 articles published between 2005 and 2019 that applied different AI techniques to bioenergy systems. This review focuses on identifying the unique capabilities of various AI techniques in addressing bioenergy‐related research challenges and improving the performance of bioenergy systems. Specifically, we characterized AI studies by their input variables, output variables, AI techniques, dataset size, and performance. We examined AI applications throughout the life cycle of bioenergy systems. We identified four areas in which AI has been mostly applied, including (1) the prediction of biomass properties, (2) the prediction of process performance of biomass conversion, including different conversion pathways and technologies, (3) the prediction of biofuel properties and the performance of bioenergy end‐use systems, and (4) supply chain modeling and optimization. Based on the review, AI is particularly useful in generating data that are hard to be measured directly, improving traditional models of biomass conversion and biofuel end‐uses, and overcoming the challenges of traditional computing techniques for bioenergy supply chain design and optimization. For future research, efforts are needed to develop standardized and practical procedures for selecting AI techniques and determining training data samples, to enhance data collection, documentation, and sharing across bioenergy‐related areas, and to explore the potential of AI in supporting the sustainable development of bioenergy systems from holistic perspectives.

Abstract Life Cycle Assessment (LCA) has been used to evaluate the life-cycle Greenhouse Gas (GHG) emissions of pulp and paper production, and most previous studies rely on process-based models for specific product types (e.g., printing paper), industry-average data, or information from a few mills. In this work, a data-driven approach is used to quantify GHG emissions intensities of different paper products manufactured by the U.S. mills. Facility-level emission data collected from publically available governmental databases and mill-level production data collected from the private sector were integrated to track the GHG emissions for different product lines and paper products in mills (in total, 165 mills were matched and analyzed). The results highlight the ranges of GHG emissions intensities by different product groups and categories, and can be used as a transparent data source for LCA practitioners, policymakers, and the pulp and paper industry to perform further analysis on carbon accounting and strategic planning for GHG mitigation.

AbstractLife cycle assessment (LCA) has been used to understand the carbon and energy implications of manufacturing and using cross-laminated timber (CLT), an emerging and sustainable alternative to concrete and steel. However, previous LCAs of CLT are static analyses without considering the complex interactions between the CLT manufacturing and forest systems, which are dynamic and largely affected by the variations in forest management, CLT manufacturing, and end-of-life options. This study fills this gap by developing a dynamic life-cycle modeling framework for a cradle-to-grave CLT manufacturing system across 100 years in the Southeastern United States. The framework integrates process-based simulations of CLT manufacturing and forest growth as well as Monte Carlo simulation to address uncertainty. On a 1-ha forest land basis, the net greenhouse gas (GHG) emissions range from −954 to −1445 metric tonne CO2eq. for a high forest productivity scenario compared to −609 to −919 metric tonne CO2eq. for a low forest productivity scenario. All scenarios showed significant GHG emissions from forest residues decay, demonstrating the strong needs to consider forest management and their dynamic impacts in LCAs of CLT or other durable wood products (DWP). The results show that using mill residues for energy recovery has lower fossil-based GHG (59%–61% reduction) than selling residues for producing DWP, but increases the net GHG emissions due to the instantaneous release of biogenic carbon in residues. In addition, the results were converted to a 1 m3basis with a cradle-to-gate system boundary to be compared with literature. The results, 113–375 kg CO2eq. m−3across all scenarios for fossil-based GHG emissions, were consistent with previous studies. Those findings highlight the needs of system-level management to maximize the potential benefits of CLT. This work is an attributional LCA, but the presented results lay a foundation for future consequential LCAs for specific CLT buildings or commercial forest management systems.

Enhanced rock weathering (ERW) is a carbon dioxide removal (CDR) strategy for combating climate change. The CDR potentials of ERW have been assessed at the process and national/global levels, but the environmental and economic implications of ERW have not been fully quantified for U.S. applications with real-world supply chain considerations. This study develops an optimization-based, integrated life cycle assessment and techno-economic analysis framework for ERW, which is demonstrated by a case study applying mining waste to croplands in the Midwestern U.S. The case study explores maximum transportation distances for intermodal transportation at varied mineral CDR yields and costs, informing supply chain design for economically viable ERW. ERW costs (US$45 to 472/tonne of net CO2e captured) and cradle-to-farm gate GHG emissions (41 to 359 kg CO2e/tonne of CO2e captured) are estimated based on a range of CDR yields and by transportation distances to and from two Midwest port destinations: Chicago and Duluth. Our sensitivity analysis identifies CDR yields, and transportation modes and distances as driving factors for result variations. Our study reveals the importance of ERW supply chain design and provides an example of U.S. CDR implementation. Our framework and findings can be applied to other regional ERW projects.

Abstract Carbon capture (CC) technology is receiving increasing attention as a critical technology for climate change mitigation. Most previous studies focus on the application of CC technology in the power generation sector, while fewer studies have analyzed applications in the refining industry, which is one of the largest greenhouse gas (GHG) emissions sources in the U.S. industrial sector. Unlike the power generation sector, the refining industry has highly distributed CO2 emission sources. In this paper, bottom-up modeling and techno-economic analysis approaches are integrated to quantify the national CO2 emission reduction potential and costs of three types of CC technologies applied to U.S. refineries: (1) pre-combustion, (2) post-combustion, and (3) oxyfuel-combustion. Two scenarios are developed to compare different design strategies for CC systems; one is a distributed design scenario for post-combustion technology, the other is a centralized design scenario for pre-combustion and oxyfuel-combustion technology. The results of the two scenarios are compared, and the trade-offs between different design strategies are highlighted. The results shown in this study provide an intuitive and quantitative understanding of the potential of CC technology to reduce CO2 emissions from the U.S. refining industry. Such information is helpful to policymakers, oil companies, and energy/environmental analysts for strategic planning and systems design to manage future CO2 emissions of refineries.

Various technologies and strategies have been proposed to decarbonize the chemical industry. Assessing the decarbonization, environmental, and economic implications of these technologies and strategies is critical to identifying pathways to a more sustainable industrial future. This study reviews recent advancements and integration of systems analysis models, including process analysis, material flow analysis, life cycle assessment, techno-economic analysis, and machine learning. These models are categorized based on analytical methods and application scales (i.e., micro-, meso-, and macroscale) for promising decarbonization technologies (e.g., carbon capture, storage, and utilization, biomass feedstock, and electrification) and circular economy strategies. Incorporating forward-looking, data-driven approaches into existing models allows for optimizing complex industrial systems and assessing future impacts. Although advances in industrial ecology–, economic-, and planetary boundary–based modeling support a more holistic systems-level assessment, more efforts are needed to consider impacts on ecosystems. Effective applications of these advanced, integrated models require cross-disciplinary collaborations across chemical engineering, industrial ecology, and economics.

AbstractBackgroundWoody 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.ResultsThe mean carbon intensity of biofuel given by Monte Carlo simulation across three pine growth cases ranges from 40.8–41.2 g CO2e MJ−1(static method) to 51.0–65.2 g CO2e 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 CO2e MJ−1(static method) and 29.6–43.4 g CO2e MJ−1in 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.ConclusionsThe 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.