Abstract Artificial intelligence (AI) is an emerging technology that has great potential in reducing energy consumption, environmental burdens, and operational risks of chemical production. However, large-scale applications of AI are still limited. One barrier is the lack of quantitative understandings of the potential benefits and risks of different AI applications. This study reviewed relevant AI literature and categorized those case studies by application types, impact categories, and application modes. Most studies assessed the energy, economic, and safety implications of AI applications, while few of them have evaluated the environmental impacts of AI, given the large data gaps and difficulties in choosing appropriate assessment methods. Based on the reviewed case studies in the chemical industry, we proposed a conceptual framework that encompasses approaches from industrial ecology, economics, and engineering to guide the selection of performance indicators and evaluation methods for a holistic assessment of AI’s impacts. This framework could be a valuable tool to support the decision-making related to AI in the fundamental research and practical production of chemicals. Although this study focuses on the chemical industry, the insights of the literature review and the proposed framework could be applied to AI applications in other industries and broad industrial ecology fields. In the end, this study highlights future research directions for addressing the data challenges in assessing AI’s impacts and developing AI-enhanced tools to support the sustainable development of the chemical industry.
Wood is an attractive material for structural applications, but it usually works best as boards or sheets. Xiao et al. have developed a process for engineering hardwood that allows these sheets to be manipulated into complex structures (see the Perspective by Tajvidi and Gardner). The key is to manipulate the cell wall structure by shrinking and blasting open the fibers and vessels by drying and “water-shocking” them. This process creates a window wherein the wood can be manipulated without ripping or tearing. Honeycomb, corrugated, or other complex structures are locked in once the wood dries. —BG Closing and reopening the vessels and fibers in hardwood allows it to be molded into complex shapes. Wood is a sustainable structural material, but it cannot be easily shaped while maintaining its mechanical properties. We report a processing strategy that uses cell wall engineering to shape flat sheets of hardwood into versatile three-dimensional (3D) structures. After breaking down wood’s lignin component and closing the vessels and fibers by evaporating water, we partially re-swell the wood in a rapid water-shock process that selectively opens the vessels. This forms a distinct wrinkled cell wall structure that allows the material to be folded and molded into desired shapes. The resulting 3D-molded wood is six times stronger than the starting wood and comparable to widely used lightweight materials such as aluminum alloys. This approach widens wood’s potential as a structural material, with lower environmental impact for buildings and transportation applications.
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.
Renewable and biodegradable materials derived from biomass are attractive candidates to replace non-biodegradable petrochemical plastics. However, the mechanical performance and wet stability of biomass are generally insufficient for practical applications. Herein, we report a facile in situ lignin regeneration strategy to synthesize a high-performance bioplastic from lignocellulosic resources (for example, wood). In this process, the porous matrix of natural wood is deconstructed to form a homogeneous cellulose–lignin slurry that features nanoscale entanglement and hydrogen bonding between the regenerated lignin and cellulose micro/nanofibrils. The resulting lignocellulosic bioplastic shows high mechanical strength, excellent water stability, ultraviolet-light resistance and improved thermal stability. Furthermore, the lignocellulosic bioplastic has a lower environmental impact as it can be easily recycled or safely biodegraded in the natural environment. This in situ lignin regeneration strategy involving only green and recyclable chemicals provides a promising route to producing strong, biodegradable and sustainable lignocellulosic bioplastic as a promising alternative to petrochemical plastics.
Bleaching is an important industrial operation that has significant environmental impacts. Many new bleaching technologies have been developed; nonetheless, it is challenging to quantify their potential environmental impacts due to the lack of quantitative information and robust analysis methods across different bleaching agents. This study addresses this gap by developing a general Life Cycle Assessment (LCA) framework that integrates LCA with manufacturing process simulations and lab-scale bleaching experiments. The framework was applied to a case study of Peracetic Acid (PAA), a promising bleaching agent, used in the Total Chlorine-Free (TCF) technology for wood pulp production, compared with the traditional Elemental Chlorine-Free (ECF) using chlorine dioxide. Different PAA synthetic pathways (i.e., using acetic acid or triacetin) and bleaching charges were explored using scenario analysis. Results showed that PAA-based TCF achieves a brightness similar to the conventional ECF technology with lower life-cycle impacts in categories such as global warming and eutrophication. From a process perspective, PAA-based TCF reduces the consumption of energy, water, pulping chemicals, completely avoids the use of chlorinated compounds, and provides enhanced process safety. The source of PAA significantly affects the life-cycle environmental impacts of pulp bleaching. Using PAA synthesized from triacetin rather than acetic acid leads to higher environmental impacts; however, such impacts can be mitigated by reducing excessive use of triacetin (direction for future optimization) or using bio-based glycerin in the production of the triacetin feedstock for PAA production. Although this case study focuses on PAA bleaching for wood pulp, the framework has the potential to be used for other/same bleaching agents in different industrial sectors.
Abstract Bioenergy 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 2005-2019 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.
A better understanding of the drivers of the economic, environmental, and social sustainability of emerging (biobased) technologies and products in early development phases can help decision-makers to identify sustainability hurdles and opportunities. Furthermore, it guides additional research and development efforts and investment decisions, that will, ultimately, lead to more sustainable products and technologies entering a market. To this end, this study developed a novel techno-sustainability assessment (TSA) framework with a demonstration on a biobased chemical application. The integrated TSA compares the potential sustainability performance of different (technology) scenarios and helps to make better-informed decisions by evaluating and trading-off sustainability impacts in one holistic framework. The TSA combines methods for comprehensive indicator selection and integration of technological and country-specific data with environmental, economic, and social data. Multi-criteria decision analysis (MCDA) is used to address data uncertainty and to enable scenario comparison if indicators are expressed in different units. A hierarchical, stochastic outranking approach is followed that compares different weighting schemes and preference structures to check for the robustness of the results. The integrated TSA framework is demonstrated on an application for which the sustainability of a production and harvesting plant of microalgae-based food colorants is assessed. For a set of scenarios that vary with regard to the algae feedstock, production technology, and location, the sustainability performance is quantified and compared, and the underlying reasons for this performance are explored.
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.
Greenhouse gas (GHG) emission levels are causing concern as climate change risks are growing, emphasizing the importance of GHG research for better understanding of emission sources. Previous studies on GHG emissions for the pulp and paper industry have ranged in scope from global to regional to site-specific. This study addresses the present knowledge gap of how GHG emissions vary among paper grades in the US. A cradle-to-gate life cycle carbon analysis for 252 mills in the US was performed by integrating large datasets at the production line level. The results indicated that one metric ton of paper product created a production weighted average of 942 kg of carbon dioxide equivalent (kg CO2eq) of GHG emissions. Greenhouse gas emissions varied by pulp and paper grade, from 608 kg CO2eq per metric ton of product to 1978 kg CO2eq per metric ton of product. Overall, fuels were the greatest contributor to the GHG emissions and should be the focus of emission reduction strategies across pulp and paper grades. © 2020, North Carolina State University.
Understanding the environmental implications of activated carbon (AC) produced from diverse biomass feedstocks is critical for biomass screening and process optimization for sustainability. Many studies have developed Life Cycle Assessment (LCA) for biomass-derived AC. However, most of them either focused on individual biomass species with differing process conditions or compared multiple biomass feedstocks without investigating the impacts of feedstocks and process variations. Developing LCA for AC from diverse biomass is time-consuming and challenging due to the lack of process data (e.g., energy and mass balance). This study addresses these knowledge gaps by developing a modeling framework that integrates artificial neural network (ANN), a machine learning approach, and kinetic-based process simulation. The integrated framework is able to generate Life Cycle Inventory data of AC produced from 73 different types of woody biomass with 250 characterization data samples. The results show large variations in energy consumption and GHG emissions across different biomass species (43.4-277 MJ/kg AC and 3.96-22.0 kg CO2-eq/kg AC). The sensitivity analysis indicates that biomass composition (e.g., hydrogen and oxygen content) and process operational conditions (e.g., activation temperature) have large impacts on energy consumption and GHG emissions associated with AC production. Copyright © 2019 American Chemical Society.
A comprehensive understanding of the waste cotton supply chain and different end-of-life options is essential to promote cotton recycling and reuse. This study analyzed global and US data to understand the quantity, current sources, and destinations of waste cotton. Globally, 11.6 million metric tons of waste cotton are generated per year during cotton garment production. This study also reviewed different options for recycling both pre-consumer and post-consumer cotton waste via chemical and mechanical processes. Different applications of waste cotton were compared to their virgin counterparts from technical, environmental, and economic perspectives. Unlike most previous studies, this research included applications that are not traditional textile products (e. g., biofuels and composites), shedding light on potential new markets for waste cotton that will not compete with virgin cotton.
This 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 that use decentralized preprocessing sites (i.e., depots that produce pellets). Two biomass resources in the Southeastern U.S., i.e., 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–\$4.71/GGE. In contrast, a traditional centralized biorefinery operating at 80% of designed capacity marks an MFSP between \$4.72–\$5.28.
Life 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 1-ha forest land basis, the net greenhouse gas (GHG) emissions ranges from -954 to -1445 metric tonne CO2 eq. for a high forest productivity scenario compared to -609 to -919 for a low forest productivity scenario. All scenarios showed significant GHG emissions from forest residues decay, demonstrating the strong need 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 1 m3 basis with a cradle-to-gate system boundary to be compared with literature. The results, 113–375 kg CO2 eq./m3 across all scenarios, 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.
As food demand increases, it is critical to develop effective strategies and evaluate their potential in reducing Greenhouse Gas (GHG) emissions and other environmental footprints of large-scale agricultural systems. This study addresses the challenge by developing a dynamic system modeling framework integrating Life Cycle Assessment (LCA), Agent-Based Modeling (ABM), and Techno-Economic Analysis (TEA). LCA and TEA were coupled with dynamic simulation models of crop yields, costs, and prices, allowing for the estimation of life-cycle environmental impacts and profitability of crop planting activities under changing climate and economic conditions. The framework was demonstrated by a case study for an agricultural system, including 1,000 farms in the United States over a 30-year time frame. The results indicated that information exchange among farmers, farmers’ environmental awareness, access to environmental information, and farm size are key factors driving the system’s environmental impacts. The results can provide a broad range of stakeholders (e.g., policymakers, nonprofits, agriculture companies) with insightful information to tailor their strategies for effectively managing the environmental footprints of large-scale agricultural systems. The integrated modeling framework has the potential to address sustainability challenges in other systems that are dynamic, involve human behaviors, and have complex interactions among human and nature systems. © 2019 Elsevier Ltd
Activated carbon (AC) is an adsorbent material with broad industrial applications. Understanding and predicting the yield and quality of AC produced from different feedstock is critical for biomass screening and process design. In this study, multi-layer feedforward artificial neural network (ANN) models were developed to predict the total yield and surface area of AC produced from various biomass feedstock using pyrolysis and steam activation. In total, 168 data samples identified from experiments in literature were used to train, validate, and test the ANN models. The trained ANN models showed high accuracy (R2 > 0.9) and demonstrated good alignment with the independent experimental data. The impacts of using datasets based on different biomass characterization methods (i.e., ultimate analysis and proximate analysis) were evaluated and compared. Finally, a contribution analysis was conducted to understand the impact of different process factors on AC yield and surface area. © 2019 Society of Chemical Industry and John Wiley & Sons, Ltd. © 2019 Society of Chemical Industry and John Wiley & Sons, Ltd
Life Cycle Assessment (LCA) has been used to assess the environmental implications of emerging technologies in different manufacturing sectors. However, it is challenging to use the traditional LCA method to model the relationships between Life Cycle Inventory (LCI) data and key technical parameters, preventing further analysis for understanding key driving factors and determining priorities for researchand technology development. Furthermore, the sensitivity analysis of traditional LCA could be misleading for decision making or strategic planning given that the potential/possibility of improving specific parameters are commonly not taken into consideration. In this work, a novel parametric analysis framework was developed to address the methodological challenge. The modeling framework integrates process-based engineering models with LCA, Life Cycle Cost analysis (LCC), and optimization. The framework is demonstrated through a case study of additive manufacturing (AM). © 2019 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license
Biofuel supply chain (BSC) design is crucial for the sustainable production and distribution of biofuel. Many modeling techniques such as optimization and simulation have been employed to BSC at different regional and temporal scales. This chapter reviews the research efforts made in BSC since 2005 to highlight status quo, challenges, and issues related to BSC modeling and design. The basic concept and components of BSC are first introduced and followed by the review of different modeling techniques at various decision levels of BSC design. Challenges and issues identified throughout this review work are highlighted at the end and future research directions are discussed. © 2020 Elsevier Inc. All rights reserved.
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. © 2019 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license
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. © 2019 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim
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. © 2018 Elsevier Ltd
Assessing the life-cycle energy and environmental impacts of emerging technologies is critical to promote sustainable chemical production because such assessment can provide policy makers with useful insights for future investment and technology deployment; it also provides manufacturers and researchers with quantitative understandings of technology potential, possible bottlenecks, and future Research, Development, and Deployment directions. However, this is a challenging task for emerging technologies due to the lack of inventory data. In this paper, a general and flexible modeling framework was developed to generate the inventory data for new technologies in the chemical industry from a life cycle perspective. The modeling framework is applied to an emerging technology in the U.S. ethylene industry, ethane oxidative dehydrogenation, for demonstration. Broadly, the work described in this paper provides a decision-support method that can be further used by researchers, environment/energy analysts, and policy makers to evaluate the net benefits of innovative technologies, guide early-stage technology development and investment decisions, and conduct strategic planning for meeting energy and emissions reduction goals of the U.S. chemical industry. © 2017 Elsevier Ltd
The methanol economy concept, which promises to replace fossil fuels as means of energy storage, transportation fuels, and feedstocks of chemical products, has existed for decades, but large-scale applications have been elusive. Currently, China is providing policy support for methanol fuels, thereby taking steps towards the methanol economy concept in the fuels and chemical industries. As China’s methanol focus continues, there are two key questions relevant to policymakers, manufacturers and environmental communities: (1) can methanol fuels realize their expected environmental benefits compared to conventional gasoline when adopted at large scales; (2) are there technology and policy options that should be pursued to ensure the potential environmental benefits associated with methanol manufactured from multiple feedstocks in China? In this study, we developed robust estimates of the primary energy use, greenhouse gas (GHG) emissions, water consumption, and air emissions (SO2 and NOx) associated with methanol fuel life cycle in China. Based on the results, both long-term and short-term implications for promoting methanol fuel in China are discussed. The results and discussions presented in this work provide manufacturers and policymakers with more holistic views of the environmental costs and benefits associated with a potential methanol transition in China, as well as actionable guidance to reduce environmental impacts from a life-cycle perspective. © 2017 Elsevier Ltd
In this study, a bottom-up technology assessment model is constructed and applied to evaluate potential changes in the cradle-to-gate primary energy consumption and greenhouse gas (GHG) emissions of U.S. ethylene production in the future. Three chemical pathways are modeled: conventional natural gas to ethylene, shale gas to ethylene, and crude-oil-based naphtha to ethylene. State-of-the-art technology and five emerging technologies for the production of ethylene from natural gas are evaluated at the process and national levels. The results quantify the primary energy and GHG emissions reductions achievable with state-of-the-art and emerging technologies, highlight the key parameters influencing their reduction potentials, and shed light on the implications of possible feedstock and technology shifts for U.S. ethylene production over the next several decades. The generalized and flexible modeling framework presented can be further used by energy, policy, and environmental analysts for assessing the savings potential of different technologies, making decisions in research and development investment, and strategic planning for meeting energy and emissions reduction goals. © 2015 American Chemical Society.
Water, energy, and food are lifelines for modern societies. The continuously rising world population, growing desires for higher living standards, and inextricable links among the three sectors make the water-energy-food (WEF) nexus a vibrant research pursuit. For the integrated delivery of WEF systems, quantifying WEF connections helps understand synergies and trade-offs across the water, energy, and food sectors, and thus is a critical initial step toward integrated WEF nexus modeling and management. However, current WEF interconnection quantifications encounter methodological hurdles. Also, existing calculation results are scattered across a wide collection of studies in multiple disciplines, which increases data collection and interpretation difficulties. To advance robust WEF nexus quantifications and further contribute to integrated WEF systems modeling and management, this study: (i) summarizes the estimate results to date on WEF interconnections; (ii) analyzes methodological and practical challenges associated with WEF interconnection calculations; and (iii) points out opportunities for enabling robust WEF nexus quantifications in the future. © 2016 by the authors.
Recent growth in U.S. ethylene production due to the shale gas boom is affecting the U.S. chemical industrys energy and greenhouse gas (GHG) emissions footprints. To evaluate these effects, a systematic, first-principles model of the cradle-to-gate ethylene production system was developed and applied. The variances associated with estimating the energy consumption and GHG emission intensities of U.S. ethylene production, both from conventional natural gas and from shale gas, are explicitly analyzed. A sensitivity analysis illustrates that the large variances in energy intensity are due to process parameters (e.g., compressor efficiency), and that large variances in GHG emissions intensity are due to fugitive emissions from upstream natural gas production. On the basis of these results, the opportunities with the greatest leverage for reducing the energy and GHG footprints are presented. The model and analysis provide energy analysts and policy makers with a better understanding of the drivers of energy use and GHG emissions associated with U.S. ethylene production. They also constitute a rich data resource that can be used to evaluate options for managing the industrys footprints moving forward. © 2015 American Chemical Society.
China is the world’s largest manufacturer of multi-crystalline silicon photovoltaic (mc-Si PV) modules, which is a key enabling technology in the global transition to renewable electric power systems. This study presents a hybrid life-cycle inventory (LCI) of Chinese mc-Si PV modules, which fills a critical knowledge gap on the environmental implications of mc-Si PV module manufacturing in China. The hybrid LCI approach combines process-based LCI data for module and poly-silicon manufacturing plants with a 2007 China IO-LCI model for production of raw material and fuel inputs to estimate ‘cradle to gate’ primary energy use, water consumption, and major air pollutant emissions (carbon dioxide, methane, sulfur dioxide, nitrous oxide, and nitrogen oxides). Results suggest that mc-Si PV modules from China may come with higher environmental burdens that one might estimate if one were using LCI results for mc-Si PV modules manufactured elsewhere. These higher burdens can be reasonably explained by the efficiency differences in China’s poly-silicon manufacturing processes, the country’s dependence on highly polluting coal-fired electricity, and the expanded system boundaries associated with the hybrid LCI modeling framework. The results should be useful for establishing more conservative ranges on the potential ‘cradle to gate’ impacts of mc-Si PV module manufacturing for more robust LCAs of PV deployment scenarios. © 2014 IOP Publishing Ltd.
Purpose: This article summarizes student performance and survey data from a recent massive open online course (MOOC) on life cycle assessment (LCA). Its purpose is to shed light on student learning outcomes, challenges, and success factors, as well as on improvement opportunities for the MOOC and the role of online courses in LCA education in general.Methods: Student survey data and course performance data were compiled, analyzed, and interpreted for 1257 students who completed a pre-course survey and 262 students who completed a post-course survey. Both surveys were designed to assess student learning outcomes, topical areas of difficulty, changing perceptions on the nature of LCA, and future plans after completing the MOOC. Results and discussion: Results suggest that online courses can attract and motivate a large number of students and equip them with basic analytical skills to move on to more advanced LCA studies. However, results also highlight how MOOCs are not without structural limitations, especially related to mostly “locked in” content and the impracticality of directly supporting individual students, which can create challenges for teaching difficult topics and conveying important limitations of LCA in practice. Conclusions: Online courses, and MOOCs in particular, may present an opportunity for the LCA community to efficiently recruit and train its next generations of LCA analysts and, in particular, those students who might not otherwise have an opportunity to take an LCA course. More surveys should be conducted by LCA instructors and researchers moving forward to enable scientific development and sharing of best practice teaching methods and materials. © 2014, The Author(s).
The chemical industry is poised for significant growth and investment, which presents an opportunity for adoption of greener chemical technologies. This article reviews available and emerging technologies for reducing the fossil fuel demand associated with the ammonia, ethylene, methanol, propylene, and benzene, toluene, and xylenes (BTX) industries. These few energy-intensive commodity chemicals (EICCs) account for around half of the energy use and greenhouse gas (GHG) emissions of the global chemical industry. Available data are harmonized to characterize potential energy use and GHG emissions savings, while technical and economic barriers to adoption are discussed. This information sheds light on the status of future technological options for reducing the impacts of the chemicals industry, and provides quantitative data to industry analysts and policy makers seeking a greater understanding of such options for EICCs. © 2014 Elsevier B.V. All rights reserved.
Solar energy is one of the most promising renewable energy alternatives for the replacement of traditional fossil fuels. CdTe photovoltaics (PVs) are thin-film solar cells that have the highest market share among all thin-film technologies. Previous LCA studies of CdTe PVs were based on the data from countries that have similar level of industrialization and strict environmental policies. Thus, to date, no LCA results have explored impacts of dramatic geographic diversity on environmental performance of CdTe PVs. Furthermore, few LCAs for CdTe PVs have taken uncertainty, which is an often overlooked but important aspect, into consideration. In this paper, we apply a “Cradle to Gate” LCA to two scenarios in China and the U.S. respectively and calculate the corresponding energy payback time and life cycle environmental impacts. Then, an uncertainty analysis is undertaken through Monte Carlo simulation. Both deterministic and uncertainty-based results indicate that geographic diversity can drastically change performance of CdTe PVs on environmental sustainability. However, this diversity of production locations has no correlation with other uncertain parameters. Results of uncertainty analysis indicate the influence of each parameter and provide guidance for future optimization of CdTe technology. Finally, comparison between CdTe and other PV technologies is displayed and discussed. © 2013 Elsevier B.V.
In this work we propose a bi-criterion, multi-period, stochastic mixed-integer linear programming model that address the optimal design and planning of hydrocarbon biorefinery supply chains under supply and demand uncertainties. The model accounts for diverse conversion technologies, feedstock seasonality and fluctuation, geographical diversity, biomass degradation, demand variation, government incentives and risk management. The objective is simultaneous minimization of the expected annualized cost and the financial risk. The financial risk is measured by conditional value-at-risk. The model simultaneously determines the optimal network design, technology selection, capital investment, production planning, and logistics management decisions. Multi-cut L-shaped decomposition approach is implemented to circumvent the computational burden of solving large scale problems. The capabilities of the proposed modeling framework and solution algorithm are illustrated through the optimal design of the hydrocarbon biorefinery supply chain in the State of Illinois. © 2012 IEEE.
A bicriterion, multiperiod, stochastic mixed-integer linear programming model to address the optimal design of hydrocarbon biorefinery supply chains under supply and demand uncertainties is presented. The model accounts for multiple conversion technologies, feedstock seasonality and fluctuation, geographical diversity, biomass degradation, demand variation, government incentives, and risk management. The objective is simultaneous minimization of the expected annualized cost and the financial risk. The latter criterion is measured by conditional value-at-risk and downside risk. The model simultaneously determines the optimal network design, technology selection, capital investment, production planning, and logistics management decisions. Multicut L-shaped method is implemented to circumvent the computational burden of solving large scale problems. The proposed modeling framework and algorithm are illustrated through four case studies of hydrocarbon biorefinery supply chain for the State of Illinois. Comparisons between the deterministic and stochastic solutions, the different risk metrics, and two decomposition methods are discussed. The computational results show the effectiveness of the proposed strategy for optimal design of hydrocarbon biorefinery supply chain under the presence of uncertainties. © 2012 American Institute of Chemical Engineers (AIChE).