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Climate change persists as a pressing global issue due to high greenhouse gas emissions from fossil fuel-based energy sources. A transition to a greener energy matrix combined with carbon offsetting is imperative to mitigate the rate at which global temperature ascends. While countries have deployed faith in green hydrogen to accelerate worldwide decarbonization efforts, the concurrent rise of blockchain-operated crypto-applications, such as bitcoin, has exacerbated climate change concerns. In this study, we propose technological solutions that combine the green hydrogen infrastructure with bitcoin mining operations to catalyze environmental and socioeconomic sustainability in climate change mitigation strategies. Since the present state of crypto-operations undeniably contributes to worldwide carbon emissions, it becomes vital to explore opportunities for harnessing the widespread enthusiasm for bitcoin as an aid toward a sustainable and climate-friendly future. Our findings reveal that green hydrogen production, paired with crypto-operations, can accelerate the deployment of solar and wind power capacities to boost conventional mitigation frameworks. Specifically, leveraging the economic potential derived from green hydrogen and bitcoin for incremental investment in renewable energy penetration, this dynamic duo can enable capacity expansions of up to 25.5% and 73.2% for solar and wind power installations. Therefore, the proposed technological solutions that leverage green hydrogen and bitcoin mining, bolstered with appropriate policy interventions, can not only strengthen renewable power generation and carbon offsetting capacities but also contribute significantly to achieving climate sustainability. [ABSTRACT FROM AUTHOR]

The article analyzes the effects of reshoring advanced electric vehicle battery manufacturing on renewable energy transition and climate targets. Reshoring and ally-shoring can reduce carbon footprint and energy use, but transitioning to certain battery technologies may diminish benefits. Nickel adoption and technology advancements are crucial for a resilient supply chain.

In this work, we perform a comparative techno-economic and environmental analysis for manufacturing ethylene and propylene from naphtha and from shale gas with rich natural gas liquids (NGLs). We first propose two novel process designs for producing ethylene and propylene from NGLs-rich shale gas. These two designs employ steam co-cracking of an ethane-propane mixture and an integration of ethane steam cracking and propane dehydrogenation, respectively. For benchmarking, we also consider a conventional process design in which ethylene and propylene are produced via steam cracking of naphtha. Detailed process models are developed for all the three designs to obtain the mass and energy balances of each unit operation. On this basis, techno-economic analysis and life cycle analysis are conducted for each of the three designs in order to systematically compare the production costs and life cycle environmental impacts of ethylene and propylene manufactured from shale gas and naphtha based on the same conditions. The economic analysis indicates that manufacturing ethylene and propylene from NGLs-rich shale gas is more attractive than from naphtha. The environmental impact analysis shows that manufacturing ethylene and propylene from NGLs-rich shale gas results in lower life cycle water consumption but higher life cycle greenhouse gas emissions. [ABSTRACT FROM AUTHOR]

This paper addresses the optimal design and planning of the advanced hydrocarbon biofuel supply chain with the unit cost objective. Benefited from the drop-in properties of advanced hydrocarbon biofuels, the supply chain takes advantage of the existing petroleum infrastructure, which may lead to significant capital and transportation savings. A mixed-integer linear programming model is proposed to simultaneously consider the supply chain design, integration strategy selection, and production planning. A robust optimization approach which tradeoffs the performance and conservatism is adopted to deal with the demand and supply uncertainty. Moreover, the unit cost objective makes the final products more cost-competitive. The resulting mixed-integer linear fractional programming model is solved by tailored optimization algorithm. County level cases in Illinois are analyzed and compared to show the advantage of the proposed optimization framework. The results show that the preconversion to petroleum-upgrading pathway is more economical when applying the unit cost objective. [ABSTRACT FROM AUTHOR]

This paper addresses the optimal design and planning of biomass-to-liquids (BTL) supply chains under economic and environmental criteria. The supply chain consists of multisite distributed–centralized processing networks for biomass conversion and liquid transportation fuel production. The economic objective is measured by the total annualized cost, and the measure of environmental performance is the life cycle greenhouse gas emissions. A multiobjective, multiperiod, mixed-integer linear programming model is proposed that takes into account diverse conversion pathways and technologies, feedstock seasonality, geographical diversity, biomass degradation, infrastructure compatibility, demand distribution, and government incentives. The model simultaneously predicts the optimal network design, facility location, technology selection, capital investment, production planning, inventory control, and logistics management decisions. The problem is formulated as a bicriterion optimization model and solved with the ε-constraint method. The resulting Pareto-optimal curve reveals how the optimal annualized cost and the BTL processing network structure change with different environmental performances of the supply chain. The proposed approach is illustrated through a county-level case study for the state of Iowa. [ABSTRACT FROM AUTHOR]

In this paper, we address the optimization of industrial gas distribution systems, which consist of plants and customers, as well as storage tanks, trucks, and trailers. A mixed-integer linear programming (MILP) model is presented to minimize the total capital and operating costs, and to integrate short-term distribution planning decisions for the vehicle routing with long-term inventory decisions for sizing storage tanks at customer locations. In order to optimize asset allocation in the industrial gas distribution network by incorporating operating decisions, the model also takes into account the synergies among delivery schedule, tank sizes, customer locations, and inventory profiles. To effectively solve large-scale instances, we propose two fast computational strategies. The first approach is a two-level solution strategy based on the decomposition of the full-scale MILP model into an upper level route selection−tank sizing model and a lower level reduced routing model. The second approach is based on a continuous approximation method, which estimates the operational cost at the strategic level and determines the trade-off with the capital cost from tank sizing. Three case studies including instances with up to 200 customers are presented to illustrate the applications of the models and the performance of the proposed solution methods. [ABSTRACT FROM AUTHOR]

In this article, we consider inventory-distribution planning under uncertainty for industrial gas supply chains by extending the continuous approximation solution strategy proposed in part I of this work. A stochastic inventory approach is proposed and incorporated into a multiperiod two-stage stochastic mixed-integer nonlinear programming (MINLP) model to handle uncertainty in demand and loss or addition of customers. This nonconvex MINLP formulation takes into account customer synergies and simultaneously predicts the optimal sizes of customers’ storage tanks, the safety stock levels, and the estimated delivery cost for replenishments. To globally optimize this stochastic MINLP problem with modest computational time, we develop a tailored branch-and-refine algorithm based on successive piecewise-linear approximation. The solution from the stochastic MINLP is fed into a detailed routing model with a shorter planning horizon to determine the optimal deliveries, replenishments, and inventories. A clustering-based heuristic is proposed for solving the routing model with reasonable computational effort. Three case studies including instances with up to 200 customers are presented to demonstrate the effectiveness of the proposed stochastic models and solution algorithms. [ABSTRACT FROM AUTHOR]

An important challenge for most chemical companies is to simultaneously consider inventory optimization and supply chain network design under demand uncertainty. This leads to a problem that requires integrating a stochastic inventory model with the supply chain network design model. This problem can be formulated as a large-scale combinatorial optimization model that includes nonlinear terms. Since these models are very difficult to solve, they require exploiting their properties and developing special solution techniques to reduce the computational effort. In this work, we analyze the properties of the basic model and develop solution techniques for a joint supply chain network design and inventory management model for a given product. The model is formulated as a nonlinear integer programming problem. By reformulating it as a mixed-integer nonlinear programming (MINLP) problem and using an associated convex relaxation model for initialization, we first propose a heuristic method to quickly obtain good-quality solutions. Further, a decomposition algorithm based on Lagrangean relaxation is developed for obtaining global or near-global optimal solutions. Extensive computational examples with up to 150 distribution centers and 150 retailers are presented to illustrate the performance of the algorithms and to compare them with the full-space solution. [ABSTRACT FROM AUTHOR]

The article highlights the life cycle energy use and environmental implications of high-performance perovskite tandem solar cells. It performs holistic life cycle assessments on the energy payback time, carbon footprint, and environmental impact scores for perovskite-silicon and perovskite-perovskite tandems benchmarked against state-of-the-art commercial silicon cells.

Providing accurate crop yield estimations at large spatial scales and understanding yield losses under extreme climate stress is an urgent challenge for sustaining global food security. While the data-driven deep learning approach has shown great capacity in predicting yield patterns, its capacity to detect and attribute the impacts of climatic extremes on yields remains unknown. In this study, we developed a deep neural network based multi-task learning framework to estimate variations of maize yield at the county level over the US Corn Belt from 2006 to 2018, with a special focus on the extreme yield loss in 2012. We found that our deep learning model hindcasted the yield variations with good accuracy for 2006-2018 (R² = 0.81) and well reproduced the extreme yield anomalies in 2012 (R² = 0.79). Further attribution analysis indicated that extreme heat stress was the major cause for yield loss, contributing to 72.5% of the yield loss, followed by anomalies of vapor pressure deficit (17.6%) and precipitation (10.8%). Our deep learning model was also able to estimate the accumulated impact of climatic factors on maize yield and identify that the silking phase was the most critical stage shaping the yield response to extreme climate stress in 2012. Our results provide a new framework of spatio-temporal deep learning to assess and attribute the crop yield response to climate variations in the data rich era. [ABSTRACT FROM AUTHOR]

This paper presents comparative technoeconomic and environmental analyses of three ethylene manufacturing pathways based on ethane-rich shale gas, corn stover, and corn grain. The shale-gas-based pathway includes two processing steps, namely, shale gas processing to produce ethane and ethane steam cracking to manufacture ethylene. The two biomass-based pathways also contain two processing steps each, namely, bioethanol production via fermentation and ethylene manufacturing via bioethanol dehydration. A distributed-centralized processing network that consists of distributed ethane/bioethanol production and centralized ethylene manufacturing is employed for each of the three pathways. Detailed process simulation models are developed for major processing steps, and the three pathways are then modeled on five different ethylene production scales. On the basis of the detailed mass and energy balances and life cycle inventory results, we conduct technoeconomic and life cycle analyses to systematically compare the economic and environmental performances of the three ethylene manufacturing pathways. The results indicate that the shale-gas-based pathway is the most attractive due to the lowest break even ethylene prices ($0.32/kg to $1.67/kg); however, it leads to the highest greenhouse gas emissions of about 1.4 kg CO2-equiv/kg ethylene. On the contrary, the corn-stover-based pathway results in the lowest greenhouse gas emissions of around -1.0 kg CO2-equiv/kg ethylene but the highest break even ethylene prices ($2.0/kg to $4.1/kg). Sensitivity analyses are performed to systematically investigate the influences of parameter deviations on the economic and environmental performances of the three ethylene manufacturing pathways. [ABSTRACT FROM AUTHOR]

To investigate postcombustion capture of CO2 in the presence of water, we developed a pressure/vacuum swing adsorption (P/VSA) cycle model consisting of a system of partial differential algebraic equations incorporating mass and energy balances, the Ergun equation for pressure changes, competitive Langmuir isotherms, and the linear driving force model. Four potential adsorbents, zeolites 13X and 5A and the MOFs HKUST-1 and Ni-MOF-74, are investigated, evaluated, and compared. Using this simulation, a two-stage Skarstrom cycle, coupled with an upstream dehydration unit and a downstream compression unit, is optimized using a nondominant sorting genetic algorithm, NSGA-II, to minimize the overall cost of capturing 90% of CO2 from flue gas at a purity of 90% and compressing it for pipeline transportation at 110 bar. The results show that under dry flue gas conditions, zeolite 13X is the best performing adsorbent with an overall cost of $32.1/ton of CO2. Under humid flue gas conditions, zeolites 13X and 5A performed equally well with overall costs of capturing CO2 of approximately $34.1/ton of CO2. [ABSTRACT FROM AUTHOR]

By combining life cycle assessment (LCA) with multi-objective optimization (MOO), the life cycle optimization (LCO) framework holds the promise not only to evaluate the environmental impacts for a given product but also to compare different alternatives and identify both ecologically and economically better decisions. Despite the recent methodological developments in LCA, most LCO applications are developed upon process-based LCA, which results in system boundary truncation and underestimation of the true impact. In this study, we propose a comprehensive LCO framework that seamlessly integrates MOO with integrated hybrid LCA. It quantifies both direct and indirect environmental impacts and incorporates them into the decision making process in addition to the more traditional economic criteria. The proposed LCO framework is demonstrated through an application on sustainable design of a potential bio-ethanol supply chain in the UK. Results indicate that the proposed hybrid LCO framework identifies a considerable amount of indirect greenhouse gas emissions (up to 58.4%) that are essentially ignored in process-based LCO. Among the biomass feedstock options considered, using woody biomass for bio-ethanol production would be the most environmentally preferable choice, while the mixed use of wheat and straw as feedstocks would be the most cost-effective one. [ABSTRACT FROM AUTHOR]

Combating the COVID-19 pandemic has raised the demand for and disposal of personal protective equipment in the United States. This work proposes a novel waste personal protective equipment processing system that enables energy recovery through producing renewable fuels and other basic chemicals. Exergy analysis and environmental assessment through a detailed life cycle assessment approach are performed to evaluate the energy and environmental sustainability of the processing system. Given the environmental advantages in reducing 35.42% of total greenhouse gas emissions from the conventional incineration and 43.50% of total fossil fuel use from landfilling processes, the optimal number, sizes, and locations of establishing facilities within the proposed personal protective equipment processing system in New York State are then determined by an optimization-based site selection methodology, proposing to build two pre-processing facilities in New York County and Suffolk County and one integrated fast pyrolysis plant in Rockland County. Their optimal annual treatment capacities are 1,708 t/y, 8,000 t/y, and 9,028 t/y. The proposed optimal personal protective equipment processing system reduces 31.5% of total fossil fuel use and 35.04% of total greenhouse gas emissions compared to the personal protective equipment incineration process. It also avoids 41.52% and 47.64% of total natural land occupation from the personal protective equipment landfilling and incineration processes. • An efficient process design to produce renewable fuels from waste PPEs. • The proposed waste PPE processing system mitigates plastic pollution. • A novel detailed life cycle assessment to evaluate environmental performances. • Climate change and fossil fuel use reduction is realized in the processing system. • An application to treat waste PPEs in the New York State. [ABSTRACT FROM AUTHOR]

The article focuses on second life and recycling and energy and environmental sustainability perspectives for high-performance lithium-ion batteries. Topics include the second life and recycling of retired automotive lithium-ion batteries (LIBs) have drawn growing attention, as large volumes of LIBs will retire in the coming decade, and the use and recycling can influence the energy and environmental sustainability of LIBs.

Powder-based additive manufacturing technologies are developing rapidly. To assess their applicability, comparison of performance and environmental impacts between additive technologies and conventional techniques must be performed. Toyota manufactures over two million aluminum four-cylinder engines in the U.S. each year via die casting. The dies used in this process are traditionally repaired via tungsten inert gas (TIG) welding and only last an average of 20.8% of the number of cycles of the original die life before another repair is needed. A hybrid repair process involving machining away the damaged areas and then rebuilding them additively via powder-blown directed energy deposition (DED) has been developed. The die repaired via DED resulted in the same life as the original die. The use of DED repair eliminated the need for emergency repairs and nonscheduled downtime on the line because the DED repaired dies last for as many cycles as the original die before another repair is needed. Life cycle analyses were conducted comparing the traditional welding repair process to the DED repair process. The results show that the DED repair process results in significantly less damage to the assessed impact categories except for ionizing radiation. Therefore, it can be concluded that the DED repair process could lessen most environmental impacts compared to traditional welding repair. Further work toward increasing energy and material efficiencies of the method could yield further reductions in environmental impacts. [ABSTRACT FROM AUTHOR]

Cyber-physical systems (CPS) enable unprecedented communication between product designers and manufacturers. Effective use of these technologies both enables and requires a new paradigm of methods and models to identify the most profitable and environmentally friendly production plans for a manufacturing network. The operating system for cyber-physical manufacturing (OSCM) and the paired network operations administration and monitoring (NOAM) software are introduced. These technologies guide our development of a mixed integer bilevel programming (BP) model that models the hierarchy between designers and manufacturers as a Stackelberg game while considering multiple objectives for each of them. Designers select and pay manufacturers, while manufacturers decide how to execute the order with the payment provided by the designer. To solve the model, a tailored solution method combining a decomposition-based approach with approximation of the lower level Pareto-optimal solution set is proposed. The model is applied to a case study based on a network of manufacturers in Wisconsin and Illinois. With the proposed model, designers and manufacturers alike can take full advantage of CPS to increase profits and decrease environmental impacts. [ABSTRACT FROM AUTHOR]

Computational high-throughput screening was carried out to assess a large number of experimentally reported metal-organic frameworks (MOFs) and zeolites for their utility in hexane isomer separation. Through the work, we identified many MOFs and zeolites with high selectivity (SL+M > 10) for the group of n-hexane, 2-methylpentane, and 3-methylpentane (linear and monobranched isomers) versus 2,2-dimethylbutane and 2,3-dimethylbutane (dibranched isomers). This group of selective sorbents includes VICDOC (Fe2(BDP)3), a MOF with triangular pores that is known to exhibit high isomer selectivity and capacity. For three of these structures, the adsorption isotherms for a 10-component mixture of hexane and heptane isomers were calculated. Subsequent simulations of column breakthrough curves showed that the DEYVUA MOF exhibits a longer process cycle time than VICDOC MOF or MRE zeolite, which are previously reported, high-performing materials, illustrating the importance of capacity in designing MOFs for practical applications. Among the identified candidates, we synthesized and characterized a MOF in a new copper form with high predicted adsorbent capacity (qL+M > 1.2 mol/L) and moderately high selectivity (SL+M ≈ 10). Finally, we examined the role of pore shape in hexane isomer separations, especially of triangular-shaped pores. We show through the potential energy surface and three-dimensional siting analyses that linear alkanes do not populate the corners of narrow triangular channels and that structures with nontriangular pores can efficiently separate hexane isomers. Detailed thermodynamic analysis illustrates how differences in the free energy of adsorption contribute to shape-selective separation in nanoporous materials. [ABSTRACT FROM AUTHOR]