Search Results (1,571)

A short-term performance test of blended biodiesel (FAME), green diesel (HVO), and diesel was experimentally assessed in a 100 kW Cummins 6BTAA5.9-G12 diesel engine under multiple load conditions. The objective was to determine the technical feasibility, operational trade-offs, and optimal blend formulations for renewable energy deployment in diesel power plants. All tested blends operated stably without engine modification, confirming the “drop-in capability” of FAME–HVO mixtures for existing diesel engines. Specific fuel consumption (SFC) increased notably at high loads, with penalties up to 15.15% for B30D20 and B35D15 relative to neat diesel, although overall efficiency improved with load. Among the ternary fuels, B30D10 and B30D20 provided the most balanced compromise between combustion reactivity and flow properties. Exhaust gas temperatures rose with load for all fuels, with FAME-rich blends exhibiting higher temperatures than neat diesel, while coolant-side analysis showed D100 and D50 as thermally favorable and B50–B100 imposing the highest cooling demand. The results emphasize the need for injection system recalibration on an energy basis for HVO-rich fuels, and for strengthened filtration and maintenance practices for FAME-rich blends to avoid filter clogging and injection instability. Considering performance, operability, and system stability up to 100 kW, B30D10 and B35D15 are identified as optimal compromise blends. The study highlights the necessity of future work on long-term durability, fuel system compatibility, supply chain robustness, and techno-economic viability to safely scale green diesel use in Indonesian stationary power generation. Full article

This study aims to address the need to operationalize Industry 5.0 (I5.0) by developing a comprehensive Assessment Framework for the adoption of the Human Centricity, Environmental Sustainability, and Industrial Resilience pillars. While existing models largely focus on technological maturity, they fail to provide measurable tools for evaluating I5.0 adoption. To bridge this gap, the paper proposes an Assessment Framework based on a structured set of Key Performance Indicators (KPIs) developed within the EU-funded PROSPECTS 5.0 project. The methodology combines an extensive literature review, a workshop with relevant stakeholders, a Delphi survey with experts, and empirical refinement conducted through workshops involving 14 companies across multiple sectors and of varying sizes. The results highlight that organizations predominantly measure traditional indicators such as health and safety, energy consumption, and supply chain robustness, while underestimating emerging dimensions such as human empowerment, social inclusion, circularity, and advanced human–machine collaboration. The framework introduces a set of KPIs for each of the I5.0 pillars, supporting structured assessment across different industrial contexts while allowing sector-specific adaptation. The findings reveal a gap between the perceived importance of several sustainability and human-centric metrics and their actual implementation. This framework allows organizations to self-assess their practices, guide strategic decisions, and align technological growth with societal and environmental goals. Full article

The increasing adoption of renewable energy sources introduces significant variability in power generation, requiring effective strategies to ensure maintain grid stability. Incentive-based demand response programs provide a practical solution for balancing supply and demand, however disputes may arise over energy data integrity. The existing solutions frequently rely on centralized authorities, exposing a single point of failure, or high costs and privacy limitation of recording granular data on-chain. To address this challenge, we propose a decentralized framework that separates cloud storage from integrity certification. This system employs a community aggregator to collect high-frequency energy measurements, store the raw data in the cloud, while anchors unique cryptographic hashes for batch of raw data to a public blockchain. This process creates an auditable and tamper-evident record of data. By recording only hashes on chain, our approach achieves privacy and scalability. Evaluation using a real-world Australian dataset confirms that the system enables transparent dispute resolution, with blockchain transaction costs consistently representing less than 0.10% of the total incentives awarded to participants. Full article

Natural gas metering is shifting from volume-based measurement to energy-based assessment as gas sources diversify, pipeline networks become more interconnected, and gas quality varies more strongly across time and space. This review examines the key technologies required for natural gas energy metering and evaluates how they support full-chain traceability from production to end use. The reviewed topics include flow measurement, gas composition analysis, calorific value determination, temperature-pressure compensation, state correction, uncertainty evaluation, intelligent data acquisition, and metrological traceability. The literature shows that individual technologies have advanced substantially. Ultrasonic flowmeters, rapid gas-quality sensing methods, dynamic calorific value allocation models, high-accuracy equations of state, and digital metering platforms have improved the technical basis of energy metering. However, these advances remain more mature at the level of individual links than at the level of the complete metering chain. Under multi-source supply, gas-quality fluctuation, hydrogen blending, and digitalized operation, the main challenge is to maintain consistency, uncertainty control, online verification, data credibility, and auditability across different metering stages. Future development should therefore focus on dynamic calorific value allocation, robust state correction under variable gas quality, full-chain uncertainty propagation, online verification, and secure data management for traceable natural gas energy metering. Full article

Background: Egypt is one of the top steel producers in the Middle East and Africa, yet it faces acute water scarcity and rising energy costs, making it a critical context for studying trade-offs among carbon emissions, water ecological effects, and operational cost in steel supply chain. Methods: Using a multi-objective optimization model based on real data from a major Egyptian steel manufacturer, this study evaluates trade-offs among cost, tardiness, and environmental impact measured by carbon emissions and water ecological effects. Unlike prior studies, this study demonstrates that dedicated warehousing enables batch-level traceability of returned scrap while reducing material handling travel time and carbon emissions. The AUGMECON method generates Pareto-optimal solutions, and sensitivity analysis is conducted on six parameters: scrap take-back rate, demand variability, raw material price, energy cost, production capacity, and carbon tax. Results: Demand and raw material prices dominate performance: a 5% demand increase raises cost by 8.6%, and a 15% raw material price increase raises cost by 32.7%. The knee-point solution achieves 58.18 billion EGP, 0.99 months tardiness, and 2096 million kg CO₂ over nine months. Conclusions: This study quantifies the impact of the circular economy and operational parameters on steel supply chain performance under a dedicated warehousing policy. Full article

The rapid expansion of solar photovoltaic (PV) deployment has led to increasing concerns regarding end-of-life module management and the sustainability of material supply chains, where waste volumes are projected to reach 3.3–5.6 million tons by 2045. This study evaluates the environmental and economic impact of advanced photovoltaic recycling in Germany, focusing on high-value material recovery from crystalline silicon modules. A Full Recovery of End-of-Life Photovoltaics (FRELP) pathway is developed, integrating light-pulse delamination and molten salt etching, and a comparative life cycle assessment and economic assessment framework is applied. The results indicate that advanced recycling achieves high recovery rates for silicon, silver, aluminum, copper and low-iron glass, yielding around €1174.88 per ton of panels recycled. Economic analysis shows that manufacturing PV modules from recycled materials reduces costs by approximately 60–77% compared to virgin material production, mainly due to avoided energy-intensive upstream processes. From an environmental perspective, the recycling-based pathway yields net benefits across impact categories, as avoided impacts from primary material extraction outweigh additional burdens associated with recycling. Overall, PV recycling in Europe is shown to be environmentally and economically favorable; however, technological maturity and policy constraints remain key barriers to large-scale implementation and a holistic overall recycling process, indicating the need for targeted policy support. Full article

The global transition toward renewable energy has intensified interest in dispatchable low-carbon sources that can support reliability-critical infrastructure in smart grid systems. Data centers represent one of the fastest-growing electricity loads globally, yet their compatibility with biomass-based energy systems as a dispatchable renewable source within smart grid architectures remains poorly understood. This study presented a comprehensive review of biomass power generation, data center energy management, and smart grid integration, drawing on a corpus of 347 peer-reviewed sources. A staged analytical design separated demand characterization from supply evaluation, ensuring that data center energy requirements emerged independently of supply-side assumptions. Using Latent Dirichlet Allocation topic modeling validated with BERTopic and VOSviewer network analysis, the study identified four distinct thematic clusters and found no single topic spanning data center reliability requirements, biomass supply dynamics, and smart grid integration simultaneously, a pattern that points to an underexplored cross-domain space in the literature. A demand–supply–grid alignment framework was introduced to illustrate compatibility conditions across temporal resolution, reliability requirements, and grid management dimensions. The alignment framework and illustrative simulation developed here are offered as analytical starting points to guide future engineering and empirical investigation rather than as demonstrations of operational readiness. An illustrative application demonstrated that biomass feedstock logistics constraints create persistent availability gaps at data center operational timescales, suggesting that supply chain resilience and grid-mediated buffering are likely necessary conditions for viable integration, a proposition that warrants empirical validation through full-scale engineering studies. The findings indicate that integration constraints reflect temporal and operational misalignment rather than technological infeasibility, providing a new analytical perspective for evaluating renewable energy integration in reliability-critical digital infrastructure. Full article

This study develops novel mathematical models to capture the complexities of international land logistics by extending the classical Traveling Salesman Problem (TSP) within a symmetry-aware optimization framework. A focused review of literature provides the theoretical basis for model formulation and highlights the limitations of conventional distance-only approaches. In international transport, shorter routes are often assumed to reduce energy use; however, this assumption overlooks the decisive influence of travel time and traffic variability. In this context, symmetry offers a useful analytical lens, as balanced relationships among distance, time, and fuel consumption can reveal more efficient logistics structures. Accordingly, two models are proposed: the Traditional Traveling Salesman Problem in terms of Distance Concentration (TTSPD), which minimizes route length, and the Extended Traveling Salesman Problem in terms of Distance and Time Concentration (ETSPDT), which jointly considers distance, travel time, and fuel consumption. Furthermore, TTSPD was employed to validate ETSPDT, since it is based on the traditional TSP. Both models are solved exactly using the Solver Add-in in Microsoft Excel 2024 with data derived from Google Maps. The results show that ETSPDT achieves superior energy efficiency and average speed, demonstrating the practical value of multidimensional, symmetry-informed optimization for sustainable supply chain and logistics management. Full article

Hydrogen (H₂) is regarded as a promising option for sustainable energy systems; however, its large-scale use in electricity supply remains limited. This study develops a stochastic network optimization model to examine the applicability of H₂-based electricity generation. The proposed Hydrogen Supply Chain (HSC) model evaluates cost and emission performance under uncertainty by considering disaster conditions, transmission losses, depreciation, and the time value of money. The Marmara Region of Türkiye is divided into 24 grid nodes, and a single-period model for 2023 is solved using Mixed-Integer Linear Programming (MILP). The HSC is allowed to meet 10–40% of electricity demand and to replace collapsed grid lines by supplying critical public centers (CPCs) during disasters. The results show that the HSC can meet 24.82% of demand, although at costs approximately 3.9 times higher than power grid (PG) electricity, while producing 3.44 MtCO₂/year compared to 65.96 MtCO₂/year from the PG. The model is then extended to a multi-period structure (2023–2053) and solved by Variable Neighborhood Search (VNS). Over time, H₂ costs decline, and their share rises from 19% to 35%, while electricity costs decrease from 408 USD/MWh to 170 USD/MWh. These findings suggest that H₂-based electricity supply can support long-term sustainability and resilience objectives in regional energy planning. Full article

This study presents a gate-to-gate life cycle assessment (LCA) of an industrial sweet cherry sorting and packing facility in Greece, directly addressing environmental sustainability in agri-food supply chains through data-driven impact quantification and improvement pathways in post-harvest operations. The assessment focuses on a gate-to-gate system boundary encompassing all processes inside the cherry sorting and packing facility, while upstream cherry production and downstream waste management are modeled and reported separately to provide system-level context. Core-stage hotspots are then analyzed in detail in the Results section, highlighting the dominant role of electricity use compared with packaging materials. The functional unit is defined as 1 kg of packed, market-ready cherries at the factory gate. Primary data are obtained from high-resolution, batch-level measurements of mass flows, energy use, water consumption, packaging materials and waste streams over a full processing season, structured as virtual sensor outputs. These sensor-informed operational data are combined with secondary life cycle inventory information from established databases to quantify climate change impacts and identify environmental hotspots across materials, energy, water, and waste, thereby delivering a quantified picture of environmental performance in the post-harvest stage. The results show that corrugated cardboard and associated packaging components are among the main contributors within the facility-level, gate-to-gate system, while the Core stage accounts for 28.43% of total GWP100. Upstream cherry production dominates the overall Upstream–Core–Downstream climate footprint with 70.61% of total impacts. Moreover, practical mitigation scenarios are modeled, including packaging optimization, partial substitution of grid electricity with photovoltaic generation, and increased water recirculation. Ιn the combined mitigation scenario, where packaging optimization, low-carbon electricity and improved water management are implemented simultaneously, total GWP100 decreases from 114,207.32 to 92,500.27 kg CO₂-eq (−19.0%) relative to the baseline, providing actionable sustainability improvements for industry stakeholders and supporting Sustainable Development Goals (SDGs) related to climate action and resource efficiency. In addition, the proposed virtual sensor architecture and data workflow support continuous monitoring, eco-efficiency management and near-real-time LCA implementation in post-harvest agri-food systems, enabling operational sustainability. Full article

Tourism destinations consume vast quantities of energy, water, food, and materials, yet these resource flows remain largely invisible in destination planning practice. The aim of this paper is to develop a conceptual framework that reconceptualises tourism destinations as industrial ecosystems and makes their material and energy flows visible, quantifiable, and amenable to destination-scale planning. Existing frameworks prioritise governance and demand management, leaving the material dimension of sustainability unaddressed. To this end, the paper proposes a multi-scale resource-flow framework grounded in industrial ecology. This is a conceptual framework paper: it develops analytical architecture for destination-scale resource accounting rather than reporting empirical measurements. The framework organises four analytical components—actors, flows, structural configurations, and feedback mechanisms—across macro, meso, and micro scales. Three planning capabilities are advanced: supply-chain-complete environmental accounting, resource hotspot detection, and policy design along the full causal chain from structural arrangement to environmental outcome. Material flow analysis, life cycle assessment, and industrial symbiosis mapping are presented as operational tools, illustrated through reference to high-intensity coastal tourism systems. Industrial symbiosis is positioned as a structural mechanism through which by-product valorisation reduces destination-level resource throughput. The study contributes a bridging framework between governance-oriented tourism planning and the material accounting rigour of industrial ecology, distinguishing it from circular economy models that supply a design principle but no material accounting, from urban metabolism approaches that assume temporally stable flows, and from regenerative development that is values-based rather than quantitative. The framework offers a foundation for more integrated and resource-efficient destination sustainability planning. Full article

Green hydrogen is increasingly discussed as an energy carrier that can link electricity, gas, heat, and transport sectors. However, many existing reviews address this topic from separate viewpoints, such as hydrogen production technologies, Artificial Intelligence (AI) applications, or system integration, with less attention to how policy and market conditions affect deployment. This review brings these related aspects together in one structured discussion. The paper first reviews the hydrogen supply chain, including production, storage, transport, and utilization. It then discusses an integrated multi-energy architecture in which hydrogen interacts with electricity, natural gas, heat, and cooling networks. Policy instruments in five major economies, including the European Union, the United States, China, Japan, and India, are compared. The review also summarizes the main barriers to large-scale deployment, including high production costs, limited infrastructure, technological challenges, regulatory uncertainty, and supply-chain constraints. In addition, the current market structure and selected large-scale hydrogen projects planned in the United States are reviewed. The paper also examines the role of artificial intelligence in green hydrogen systems. AI applications are grouped into four main stages of the hydrogen value chain: forecasting renewable energy generation, improving electrolyzer design and operation, optimizing storage and distribution, and supporting system-level techno-economic assessment. Recent Machine Learning (ML) studies are compared based on their methods and their contributions to operation and planning. Overall, this review highlights the role of AI in enabling green hydrogen integration within multi-energy systems. Full article

This study presents a comprehensive life cycle assessment (LCA) of the modernization of an existing 460 MW coal-fired power unit into a hybrid system incorporating a high-temperature gas-cooled reactor (HTGR). The analysis was conducted from a cradle-to-grave perspective using a functional unit of 1 MWh of net electricity, based on the ecoinvent 3.9 database and the ReCiPe 2016 Midpoint method. The results indicate that the modernized system achieves a global warming potential (GWP) of 18.2 g CO₂-eq/kWh, representing a 93.5% reduction compared to a supercritical coal-fired unit. The largest contribution to the total environmental burden is associated with the upstream uranium supply chain, accounting for approximately 42% of GWP. In contrast, the operational phase exhibits a negative contribution due to the application of environmental credits resulting from the avoidance of emissions related to coal combustion. The findings also confirm a significant improvement in resource efficiency, including reduced primary energy demand and waste generation compared to the reference system. Sensitivity analysis demonstrated the robustness of the results with respect to variations in key economic and thermodynamic parameters, particularly CAPEX (capital expenditures) and operating temperature. Overall, the results suggest that hybrid retrofitting of coal-fired power plants with HTGR technology may serve as a viable transitional pathway supporting the decarbonization of the Polish energy sector. Full article

The growing volatility and complexity of global food supply chains have intensified the need for integrated analytical frameworks capable of supporting anticipatory and data-driven decision-making. This article examines how logistics sector observatories can function as strategic intelligence infrastructures for identifying structural tensions and supporting resilience in cold-chain logistics systems. The article introduces the concept of logistics sector observatories as strategic intelligence infrastructures and examines its empirical relevance through a longitudinal analysis of the Spanish cold-chain logistics sector. Empirically, the research draws on a multi-source dataset constructed through the ALDEFE Observatory in collaboration with industry stakeholders over the core study period 2021–2025, encompassing storage capacity, consumption dynamics, energy costs, international logistics indices, and macroeconomic variables. Complementary energy benchmark data for 2019–2025 are used to contextualize electricity cost volatility. Methodologically, the study combines qualitative insights from stakeholder interviews with exploratory quantitative longitudinal analysis. The results suggest severe structural tensions driven by the interaction between rigid capacity constraints and energy cost volatility. The analysis identifies a pattern of persistently high storage occupancy despite substantial energy-price fluctuations. This finding is consistent with the structural inelasticity of cold-chain demand, which reduces operational slack and affects system resilience. Beyond operational resilience, the study highlights the potential contribution of sector observatories to the energy sustainability transition through future sector-level indicators related to energy intensity, refrigeration efficiency, and carbon performance. The study contributes a sector-level, data-driven perspective on visibility, coordination, and anticipatory governance in complex logistics environments. Full article

Background: Container terminals are crucial nodes in global supply chains, but they also contribute significantly to environmental pollution. The analysis of sustainability in port logistics through carbon footprint offers crucial knowledge on how to reduce environmental impact in logistics. Methods: This systematic review uses a PRISMA-based research flow to extract key facts about energy consumption and greenhouse gas emissions, particularly CO₂, which are still prevalent in terminal operations and logistics. Results: The paper analyses strategies and technologies adopted to reduce the carbon footprint, such as efficient infrastructure, electrification, automation, digitalisation, and AI-powered port logistics. It highlights the potential of sustainable logistics solutions, such as real-time cargo tracking, intelligent robotics and data analytics, to make container terminals more eco-friendly. Conclusions: Beyond analysing sustainability assessment models for the ecological efficiency and operational performance of container terminals, this paper highlights the need for future applied research into how investments in sustainable practices, as demonstrated by the most successful Asian port examples, can further reduce container terminal environmental footprint. Full article