Energy Supply Chain Research Articles

Towards resilient pipeline infrastructure: lessons learned from failure analysis

Understanding the mechanisms of pipeline failures is crucial for identifying vulnerabilities in gas transmission pipelines and planning strategies to enhance the reliability and resilience of energy supply chains. Existing studies and the American Society of Mechanical Engineers’ (ASME) Code for Pressure Piping primarily focus on corrosion, recommending inspections every 10 years to prevent incidents due to this time-dependent threat. However, these guidelines do not provide comprehensive regulation on the likelihood of incidents due to other causes, especially non-time-dependent events (i.e. do not provide any indication of the inspection frequency or the most likely time for an incident to occur). This study adopts an innovative approach adopting machine learning, particularly artificial neural networks (ANNs), to analyse historical pipeline failure data from 1970 to 2023. By analysing records from the US Pipeline & Hazardous Materials Safety Administration, the model captures the complexity of various degradation phenomena, predicting failure years and hazard frequencies beyond corrosion. This innovative approach allows adopting more informed preventive measures and response strategies, offering deep insights into incident causes, consequences, and patterns. The results provide practical insights for maintenance planning, offering an estimation of periods when a pipeline may be more susceptible to incidents based on various factors. However, since all models inherently present uncertainties, both in the data and the modelling process, these estimates should be interpreted as probabilistic assessments. This study provides operators with a strategic framework to prescriptively address potential vulnerabilities, thereby promoting sustained operational integrity and minimising the occurrence of unexpected events throughout the service life of pipelines. By expanding the scope of risk assessment beyond corrosion, this study significantly advances the field of pipeline safety and reliability, setting a new standard for comprehensive incident prevention.

Environmental life-cycle analysis of hydrogen technology pathways in the United States

Hydrogen is a zero-carbon energy carrier with potential to decarbonize industrial and transportation sectors, but its life-cycle greenhouse gas (GHG) emissions depend on its energy supply chain and carbon management measures (e.g., carbon capture and storage). Global support for clean hydrogen production and use has recently intensified. In the United States, Congress passed several laws that incentivize the production and use of renewable and low-carbon hydrogen, such as the Bipartisan Infrastructure Law (BIL) in 2021 and the Inflation Reduction Act (IRA) in 2022, which provides tax credits of up to $3/kg depending on the carbon intensity of the produced hydrogen. A comprehensive life-cycle accounting of GHG emissions associated with hydrogen production is needed to determine the carbon intensity of hydrogen throughout its value chain. In the United States, Argonne’s R&D GREET® (Greenhouse Gases, Regulated emissions, and Energy use in Technologies) model has been widely used for hydrogen carbon intensity calculations. This paper describes the major hydrogen technology pathways considered in the United States and provides data sources and carbon intensity results for each of the hydrogen production and delivery pathways using consistent system boundaries and most recent technology performance and supply chain data.

A Bibliometric Analysis of Green Shipping: Research Progress and Challenges for Sustainable Maritime Transport

This paper aims to systematically review and analyze the literature on green shipping, focusing on research trends, key areas, and future directions. A bibliometric analysis is conducted on 1339 articles published between 2000 and 2023, using data from the SCI-EXPANDED and SSCI core collections on the Web of Science platform. Co-occurrence analyses are performed for authors, countries, institutions, and keywords. The analysis reveals exponential growth in green shipping research over the past decade, with a significant increase since 2019. China leads in publication output and international collaboration. Key research areas identified include sustainable development, green energy, green ports, and green supply chain management. Green shipping research is rapidly expanding, emphasizing sustainable practices and technological advancements. Future research is expected to integrate artificial intelligence, big data analytics, and other advanced technologies to further promote sustainability. Strengthening international cooperation and interdisciplinary integration will be crucial for advancing the global green shipping agenda.

Variable‐weight combined forecasting model with causal analysis and clustering for refined oil sales forecasting

AbstractForecasting refined oil sales is essential in energy supply chain management. However, accurate forecasting is limited by several factors, including multiple influences of external features, heterogeneity of different gasoline stations, and difficulty in balancing linear and nonlinear forecasting. To address these issues, we propose a novel variable‐weight combined forecasting model. In the first stage, the model incorporates causal analysis and clustering methods to provide a quantitative description of multiple effects of external features and highly correlated aggregation of homogeneous data. Subsequently, based on the patterns of external feature influences learned from historical data, variable‐weight combined forecasting is realized to balance linear and nonlinear forecasting dynamically. Experiments based on real sales data procured from several regions demonstrate that the proposed model outperforms other benchmark and widely used models in terms of forecasting accuracy and statistical significance. The ablation experimental results confirm the significance of causal analysis, clustering, and variable‐weight combined forecasting in improving the balance between linear and nonlinear forecasting. Moreover, our results indicate that improving the quality of clustering can yield greater benefits than improving the amount of training data. Finally, we also explore whether the forecasting superiority translates into better inventory control, and our results show that the proposed optimization model can effectively balance inventory cost and service level, while also better suppress the bullwhip effect.

Innovative Solutions for Sustainable Logistics in Food Manufacturing Industries overcome Barriers and Challenges

This paper explores innovative solutions for sustainable logistics in the food manufacturing industry, focusing on how these strategies address key challenges. It examines how advancements in technology, logistics strategies, and eco-friendly practices contribute to sustainability while maintaining efficiency and profitability. The study analyzes data from companies that have adopted modern logistics solutions, such as AI-driven optimization, renewable energy, and circular supply chain models, supplemented by expert interviews to gain practical insights. The findings reveal that these solutions help reduce carbon emissions, minimize food waste, and optimize resource use. Companies that have implemented these strategies have successfully overcome common barriers such as high costs, infrastructure complexity, and regulatory challenges. While the research focuses on large-scale manufacturers, smaller companies remain under explored, suggesting the need for future studies on scalability and policy frameworks to support wider adoption. This paper offers a unique contribution by combining current innovations in sustainable logistics with practical industry challenges, providing actionable insights for food manufacturers and laying the foundation for further research into overcoming financial and technological barriers.

Sustainability and circularity assessment of biomass-based energy supply chain

Climate change and other environmental consequences of socio-economic activities require a more sustainable and circular growth. At the same time, the limitation of the earth resource demands industries to improve resource efficiency and increase the rate of recycling of materials. There are several sustainable and circular alternatives that the industries may adopt. However, the question is that among these alternatives, which one should be selected for implementation for the highest sustainable and circular benefits. This study introduces a novel tool for assessing the sustainability and circularity of biomass-based energy supply chains, integrating multi-criteria decision-making methods with life cycle thinking approach. It evaluates five alternatives using a sustainability and circularity indicators, offering new insights into the deloyment of circular business models at companies in biomass-based energy supply chain. The tool is also applied to a specific rice straw supply chain in Italy, to assess the sustainability and circularity of five alternatives and outrank them. The results indicated that not all the alternatives are better in terms of supporting sustainable development and circular economy, compared to the baseline business model. In this supply chain, the extended lifetime for digestate from the aerobic digestion plant is the most ‘sustainable and circular’ alternative, while the capture of carbon dioxide from the same plant and its use for microalgae cultivation is the least ‘sustainable and circular’ alternative. A sensitivity analysis was conducted on different weighting sets during the assessment. It indicated that the priority of the decision makers can slightly change the outrank of the alternatives and the magnitude of the outranks.

Russo-Ukraine War: Ethics Cum Impact On the Food and Energy Supply Chain in Nigeria

Russo-Ukraine War: Ethics Cum Impact On the Food and Energy Supply Chain in Nigeria

A Bibliometric Review to Understanding the Supply Chain of Renewable Energy

This bibliometric analysis investigates the evolving research landscape in renewable energy supply chains. The study uses VOSviewer software to examine influential articles, authors, countries, journals, and keywords from 2015 to 2023. Notably, 2019 marked a significant shift with a surge in publications and citations, suggesting a transition toward more impactful and higher-quality research. In subsequent years, they have witnessed fluctuations in citation counts, potentially reflecting changes in research focus and publication quality. Geographically, the United States and China stand out as leading contributors, with the United Kingdom, Germany, Italy, and Iran also making substantial contributions. The analysis of journals and publishers reveals variations in research impact and influence, with Elsevier emerging as a significant publisher. The keyword analysis underscores the prevalence of "Supply chains," "renewable energy resources," and "biomass" as central themes. This study offers valuable insights into the research trends and dynamics in the renewable energy supply chain field.

An agent-based cooperative co-evolutionary framework for optimizing the production planning of energy supply chains under uncertainty scenarios

Nowadays, energy and power companies compete to get the raw materials and equipment they need on time, as project times lengthen, costs spiral, stock-out continues to plague plans to a decarbonized energy future. The risks reflect the impact of uncertainty and volatility on the resilience of the supply chains. Therefore, there is a need for the enhancement of the production planning in Energy Supply Chains (ESCs), as it enables affordable energy supplies and supports the companies transition to a clean, secure and sustainable energy mix. This study aims to understand the interactive behavior among individuals and optimize their production planning under uncertainty scenarios. In particular, we propose a novel framework to couple an Agent-based Modelling (ABM) and a Co-evolutionary Algorithm (CEA), to realize its capacity to solve a Many-objective Optimization Problem (MaOP) where the profits of multiple agents are concurrently maximized in their interactive transaction processes under normal conditions and uncertain disruption events.For demonstration, we illustrate the proposed approach by considering a five-layer oil and gas ESC model, where uncertainties from multiple sources and the structural dynamics challenge the balance between supply and demand. The results obtained by an integration of a Cooperative Co-evolutionary Particle Swarm Optimizer (CCPSO) algorithm into ABM show the pricing and orders of the target agents are optimized while the loss of ESC resilience is minimized under uncertainty scenarios, proving its capacity of improving the diversity and the convergence, compared to the classic evolutionary algorithms.

A hybrid machine learning solution for redesigning sustainable circular energy supply chains

Sustainability development goals require decision-makers to incorporate social and environmental indicators in their economic models using innovative solutions, such as a sustainable circular economy. This paper presents an innovative integrated production and logistic model for a circular economy using multi-objective optimization. Empirical data includes a renewable energy supply chain. We assess the sustainability performance of the proposed decision-making model by simultaneously considering production and logistics costs, carbon emissions, and the number of jobs created. The case study is optimized with an exact method, and a hybrid machine-learning algorithm solves large-scale numerical examples. The paper’s main contributions include movable manufacturers, uncertain parameters, a hybrid machine learning algorithm, and empirical data in the proposed decision-making model. The findings show that a moveable facility can substantially decrease total cost and carbon emissions. Sensitivity analysis shows that changes in moveable capacity and percent yield considerably impact the objectives. Findings show that decision-makers can achieve cost parity with fossil-based sources when employing circular supply chain management.

Performance Analysis of a Renewable‐Powered Multi‐Gas Floating Storage and Regasification Facility for Ammonia Vessels With Reconversion to Hydrogen

ABSTRACTNatural gas and renewable energy carriers play critical roles in the energy supply chain due to rising energy consumption demands and a significant shift toward cleaner energy. However, the requirement to liquefy and regasify liquefied natural gas (LNG) and renewable energy carriers for transportation makes the entire process expensive and challenging. Hence, a floating storage and regasification unit (FSRU) plant provides a solution to the aforementioned problems with the additional benefit of being more affordable, time‐efficient, and having less land footprint requirement than the conventional onshore facility. The proposed integrated FSRU in this study, is powered by renewable energy, including solar and ocean thermal energy. The subsystems of integrated FSRU consist of parabolic dish collectors (PDC), Rankine cycle, organic Rankine cycle (ORC), multi‐stage flashing (MSF) desalination unit, decomposition, reliquefication, and regasification plants, which provide valuable commodities such as freshwater, electricity, hydrogen, and heating. It can also cater to standard multi‐gas harboring vessels for storage and regasification of sustainable energy carriers. The study assesses the performance of the proposed system thermodynamically by analyzing mass, energy, entropy, and exergy balance equations using the engineering equation solver (EES) software. Furthermore, parametric studies were conducted to understand the interlinkage among various variables. The analytical results show that the proposed system is able to produce 1.82 MW of electricity, 2056 kg/day of fresh water, and 338.3 kg/day of hydrogen, achieving an overall system energy efficiency of 32.7% and exergy efficiency of 79.3%. This approach aims to foster energy diversification, enhance energy security, and support the transition toward sustainable energy systems, as well as further the advancement of maritime transport systems.

Sourcing renewable energy components: building resilient supply chains, reducing dependence on foreign suppliers, and enhancing energy security

The transition to renewable energy is essential for mitigating climate change and ensuring long-term energy security. As global demand for renewable energy technologies grows, establishing resilient supply chains becomes crucial. This paper examines strategies to build robust supply chains, reduce reliance on foreign suppliers, and enhance energy security. A major challenge in renewable energy is the dependence on a limited number of international suppliers, which risks supply disruptions due to geopolitical tensions, trade restrictions, or natural disasters. Diversifying suppliers can mitigate these risks by encouraging alternative suppliers and fostering competition (Smith, 2020). Local manufacturing is another critical strategy, promoting supply chain resilience, economic growth, and job creation. Countries like the United States and Germany have incentivized local manufacturing of solar panels and wind turbine components (Jones, 2021), ensuring a steady supply of parts and aligning with broader industrial policies. Maintaining strategic reserves of critical materials, such as rare earth elements and lithium, is essential for buffering against supply disruptions and price volatility, ensuring continuous production of renewable energy technologies (Brown, 2019). Strategic planning and investment in reserves are vital for a comprehensive energy security strategy. Technological innovation enhances supply chain resilience. Advances in materials science, manufacturing processes, and recycling technologies can reduce dependence on scarce materials and improve renewable energy system efficiency. Developing alternative materials for battery storage, for example, can reduce reliance on lithium and cobalt sourced from unstable regions (White, 2022). Public-private partnerships (PPPs) are crucial for advancing the renewable energy sector. By leveraging both sectors' strengths, PPPs can accelerate technology deployment, improve infrastructure, and foster innovation. Successful examples include government-private collaborations on offshore wind farms and smart grid technologies (Green & Associates, 2021). Favorable trade policies facilitate the global exchange of renewable energy technologies and components. Reducing tariffs, streamlining customs, and fostering international cooperation can enhance supply chain efficiency and reliability (Lee, 2020). This study advocates for trade agreements tailored to the renewable energy sector's needs. Integrated energy systems and smart grid technologies optimize renewable energy use and distribution. Advanced monitoring and control systems improve grid stability, reduce energy waste, and enhance renewable source integration (Adams, 2019). Investment in smart grid infrastructure is crucial for a resilient energy strategy. Robust policy frameworks are necessary to support these initiatives. Governments must enact policies providing clear guidance, incentives, and support for resilient renewable energy supply chains, including ambitious renewable energy targets, financial incentives for R&D, and regulatory stability (Martinez, 2021).

Renewable energy-powered water desalination and treatment network under wind power and water demand uncertainty: A possibilistic chance-constrained programming

Given the scarcity of freshwater resources, the growing significance of desalination is undeniable. It holds immense potential, particularly in regions grappling with severe water shortages. However, desalination's Achilles heel lies in its voracious energy appetite, requiring roughly ten times more energy than wastewater treatment. Moreover, the prevalent use of fossil fuels in desalination plants poses concerning issues like environmental pollution, fossil fuel depletion, and rising costs. The present study has designed an integrated Water desalination and treatment Network that includes a number of desalination facilities, storage centers, wind farms, and wastewater treatment facilities. The water desalination and treatment network has been structured using a Mixed-Integer Linear Programming (MILP) model, considering uncertainties in wind power and water demand. Employing a chance constraint probabilistic programming approach, this model ensures robustness and balances conservatism with investment attractiveness. It aims to enhance resilience against fluctuations in wind energy and water demand within the water and energy supply chain network. The study applied this model to optimize the locations of desalination plants, treatment centers, and storage facilities. This integrated model ensures autonomy, eliminating the need for external water and energy sources while reliably meeting regional demands. In the context of the Makran coasts case study, our comprehensive mathematical model demonstrates an optimal allocation with 96.67 % attributed to fixed costs and only 3.33 % to variable costs. Moreover, this model precisely optimizes the locations of two desalination centers, two storage facilities, and ten water treatment centers, effectively managing the need for external water resources. Ultimately, through a rigorous sensitivity analysis, we unveiled that the chance constraint parameters have a significant impact on the variable costs.

Effect of Governmental Subsidies on Green Energy Supply Chains: A Perspective of Meteorological Disasters

The energy sector, serving as a pivotal propellant within the macroeconomic framework, finds its production, distribution, and consumption aspects considerably influenced by climatic variations. In this study, a two-tier Stackelberg model for the green energy supply chain is developed, which is employed to analyze the profit disparities between suppliers and retailers both in the absence and presence of meteorological disasters. Furthermore, the research delves into the optimal supply chain strategies under three distinct scenarios in the context of meteorological disasters. A comparative analysis is conducted to examine the decision-making variances and the shifts in the interests of each supply chain participant under these scenarios. The findings reveal two critical insights: First, meteorological disasters detrimentally affect the environmental friendliness of energy products, as well as the profits of both retailers and suppliers. Second, the selection of the optimal strategy varies depending on the evaluation criterion used. When product greenness is the metric, subsidies aimed at promoting sales costs emerge as the superior strategy. Conversely, for retailers and suppliers, subsidies that target the environmental friendliness of energy products prove to be the most effective. Based on the conclusions, the paper also makes recommendations for governments and companies.

Global Geopolitical Changes and New/Renewable Energy Game

This study analyzes the impact of global geopolitical changes on new/renewable energy (NRE) policies and their roles in enhancing national energy security, elevating international stature, and influencing the global energy market. Using game theory, it reveals how NRE policies promote technological innovation, diversify energy supply, and strengthen international collaboration, thus advancing the global energy system towards a low-carbon transition and improving international energy governance. NRE policies significantly enhance national energy security by reducing dependency on single energy sources, facilitate the global shift to low-carbon energy, and intensify international cooperation. The effectiveness of these policies in driving energy transformation is notable, and they are expected to remain crucial for global energy security and sustainable transition. Recommendations include strengthening the stability and security of energy supply chains through enhanced oversight, increasing investment in R&D and innovation to reduce costs, fostering international cooperation for better policy coordination, and implementing diversified energy policies to encourage the adoption of NRE. These measures will address challenges from global geopolitical dynamics and drive the global energy system towards sustainability and efficiency.

Performance linkage in renewable energy supply chain: A comparative analysis with coal power and the entire industry

Establishing an efficient renewable energy supply chain is crucial for mitigating climate change, contingent upon policy influence and market complementarity. Accordingly, this study 1) Draws on the characteristics of “market mechanism” and “price regulation” within China's renewable energy industry to compare the supply chain with the entire industry and the coal power industry, laying the groundwork for an analytical framework to assess performance linkage. 2) Examines the role of markets and policies in moderating performance linkage within the renewable energy supply chain based on cash flow inflow and final commodity pricing. Empirical findings indicate that: 1) China's renewable energy supply chain exhibits weaker market circulation mechanisms than the overall industry, but it has not yet shown zero-sum game characteristics akin to the coal power supply chain. 2) Cash flow inflow and final commodity pricing are identified as pivotal factors enabling China's renewable energy industry to circumvent zero-sum games while still lacking a robust positive cycle. This research aids policymakers in dynamically assessing market-based circulation levels within the renewable energy supply chain and facilitates quantifying price regulation and market funding roles in updating industrial policies.

Ex-Vivo 13C NMR Spectroscopy of Rodent Brain: TNF Restricts Neuronal Utilization of Astrocyte-Derived Metabolites.

Tumor necrosis factor (TNF) has well-established roles in neuroinflammatory disorders, but the effect of TNF on the biochemistry of brain cells remains poorly understood. Here, we microinjected TNF into the brain to study its impact on glial and neuronal metabolism (glycolysis, pentose phosphate pathway, citric acid cycle, pyruvate dehydrogenase, and pyruvate carboxylase pathways) using 13C NMR spectroscopy on brain extracts following intravenous [1,2-13C]-glucose (to probe glia and neuron metabolism), [2-13C]-acetate (probing astrocyte-specific metabolites), or [3-13C]-lactate. An increase in [4,5-13C]-glutamine and [2,3-13C]-lactate coupled with a decrease in [4,5-13C]-glutamate was observed in the [1,2-13C]-glucose-infused animals treated with TNF. As glutamine is produced from glutamate by astrocyte-specific glutamine synthetase the increase in [4,5-13C]-glutamine reflects increased production of glutamine by astrocytes. This was confirmed by infusion with astrocyte substrate [2-13C]-acetate. As lactate is metabolized in the brain to produce glutamate, the simultaneous increase in [2,3-13C]-lactate and decrease in [4,5-13C]-glutamate suggests decreased lactate utilization, which was confirmed using [3-13C]-lactate as a metabolic precursor. These results suggest that TNF rearranges the metabolic network, disrupting the energy supply chain perturbing the glutamine-glutamate shuttle between astrocytes and the neurons. These insights pave the way for developing astrocyte-targeted therapeutic strategies aimed at modulating effects of TNF to restore metabolic homeostasis in neuroinflammatory disorders.

Assessment of energy, economic and environmental aspects of the methanol-fueled Allam cycle in cross-border renewable energy supply chain

This study addresses the challenge of uneven renewable energy distribution by proposing a renewable energy supply chain using MeOH-fueled Allam Cycle for power generation. With the escalating demand for renewable energy, regions with a surplus can produce MeOH using green H2 and recycled CO2, which can then be transported to energy-deficient regions. In these importing regions, MeOH is used for electricity generation via the Allam Cycle, with captured CO2 recycled for MeOH synthesis, creating a closed-loop carbon system. This study evaluates the energy, economic and environmental aspects of this supply chain. It projects that electricity costs in importing regions could decrease from USD 233.02/MWhimpt to USD 101.33/MWhimpt, and MeOH costs could drop from USD 585.39/t to USD 173.40/t, assuming a reduction in renewable electricity costs in exporting regions from USD 45/MWhexpt to USD 15/MWhexpt. Comparative analysis shows acceptable energy conversion and transportation costs, with an electricity conversion rate of 30.65 %-37.07 %. This design offers a promising, cost-effective solution for a sustainable future, with the onshore wind supply chain exhibiting lower emissions of 111.3–97.2 kg/MWhimpt.

Energy uncertainty, geopolitical conflict, and militarization matters for Renewable and non-renewable energy development: Perspectives from G7 economies

Conflict can increase the risk profile of fossil fuel-oriented projects due to disruptions in the energy supply chain and potentially impact the clean energy transition in G7 economies. Thus, the paper investigates the unexplored connections between energy uncertainty, geopolitical conflict, and the militarization index with renewable and non-renewable energy in G7 (1997–2022). The application of advanced econometric algorithms, notably the Methods of Moment Quantile Regression, reveals that energy uncertainty has a positive effect on renewable energy consumption and a negative effect on non-renewable energy consumption. Furthermore, the current geopolitical landscape poses unfavorable conditions for both the expansion of renewable energy usage and the utilization of non-renewable energy sources. The global militarism index relies on non-renewable rather than renewable energy sources. The study employed the Impulse Response Function using Bayesian vector autoregression. The study discovered that variations in energy uncertainty initially enhance the usage of renewable energy but ultimately have adverse consequences for renewable and non-renewable energy consumption. Geopolitical risk unfavorably affects both types of energy, but trade positively influences the growth of renewable energy use in G7. The study recommends transparent economic strategies as essential for governments to reduce uncertainty and facilitate a transition to clean energy.

The Procedure of Extracting Natural Gas liquids in Cryosift Method

The extraction of Natural Gas Liquids (NGLs) plays a pivotal role in the modern energy landscape, serving as both a significant economic driver and a critical component of energy supply chains. This project investigates the comprehensive processes involved in NGL extraction, which includes the separation of ethane, propane, butanes, and natural gasoline from natural gas and crude oil. Key techniques such as cryogenic distillation, absorption, and fractionation are explored in depth, highlighting their operational principles, efficiency, and technological advancements. In addition to the technical aspects, this project examines the environmental impacts of NGL extraction and the strategies employed to mitigate these effects. Innovations in technology and process optimization are discussed, emphasizing their role in reducing carbon footprints and enhancing sustainability. The economic dimension of NGL extraction is also analyzed, with a focus on market trends, supply chain logistics, and the factors influencing global demand and pricing. Case studies from leading NGL extraction facilities are used to illustrate best practices, challenges, and the implementation of cutting-edge technologies. Through a multi-faceted approach, this project aims to provide a detailed understanding of the NGL extraction industry. It underscores the importance of NGLs in the global energy market, the technological advancements driving the industry forward, and the economic and environmental considerations that shape its future. The findings offer valuable insights for stakeholders, policymakers, and industry professionals seeking to navigate the complexities of NGL extraction and leverage its benefits for sustainable development. Key Words: Cryogenic Distillation, Absorption, Fractionation