Search Results (761)

This study investigates the crucial role energy leaders play in driving strategic energy management (SEM) and accelerating cost savings within a manufacturing organization and consequently, the industrial sector. Whereas energy efficiency can be seen as an innovative business practice with irrefutable cost benefits, its effective implementation requires strategic leadership and a structured approach. This research analyzes data collected from 120 participants representing 71 companies attending the Energy Bootcamp events organized by the U.S. Department of Energy’s (DOE) Better Plants program. The collected data focused on the state of SEM implementation, the presence and responsibilities of energy leaders, and the formation and function of energy teams. The findings reveal a significant gap between the perceived importance of SEM and its actual adoption, highlighting the need for strong leadership to drive behavioral changes by championing energy efficiency initiatives. Results indicate that effective energy leaders possess a diverse skill set, including the ability to secure top management buy-in, foster a culture of energy consciousness, and collaborate across departments. This study emphasizes the importance of empowering energy leaders with clearly defined roles and responsibilities as well as the authority to build and lead cross-functional energy teams. Furthermore, integrating energy management into existing organizational structures and leveraging readily available resources are identified as key factors for successful implementation. This research underscores how dedicated leadership and effective SEM practices help achieve industrial energy efficiency goals, providing practical insights for organizations seeking to improve performance and contribute to a resilient future. Full article

In the context of the ongoing digital revolution in manufacturing and the simultaneous advancement toward dual carbon objectives, this study investigates the role of intelligent technological advancements, particularly industrial robotics, in improving firm-level energy efficiency. Utilizing panel data from Chinese listed companies spanning the period 2012–2023, the research assesses the relationship between exposure to industrial robots and corporate energy efficiency metrics. The empirical analysis demonstrates that greater exposure to industry-level robotization substantially boosts corporate energy performance, verifying that intelligent modernization and green transition can be mutually reinforcing. This positive effect is particularly pronounced among superstar firms, in more competitive industries, and for capital-intensive enterprises. Mechanism analysis reveals that, first, robotization processes generate a scale effect that effectively dilutes the fixed energy consumption per unit of product. Second, the diffusion of robots intensifies market competition, creating a competition effect that compels all firms within the industry to optimize costs and management with a focus on energy conservation. This study demonstrates that enhancing human capital within organizations significantly amplifies the beneficial impact of robotic integration on energy efficiency metrics. By providing empirical data from an emerging market context, this research not only elucidates the role of industrial robots but also offers policy-relevant insights for developed economies navigating the concurrent challenges of industrial modernization and environmental sustainability. Full article

The global clean energy transition requires power conversion technologies that combine high efficiency, operational flexibility, and reduced environmental impact over their entire service life. Solid-state transformers (SSTs) have emerged as a promising alternative to conventional line-frequency transformers, offering bidirectional power flow, high-frequency isolation, and advanced control capabilities that support renewable integration and electrified infrastructures. This paper presents a comparative life cycle assessment (LCA) of conventional transformers and SSTs across representative power-system applications, including residential and industrial distribution networks, electric vehicle fast-charging infrastructure, and transmission–distribution interface substations. The analysis follows a cradle-to-grave approach and is based on literature-derived LCA data, manufacturer specifications, and harmonized engineering assumptions applied consistently across all case studies. The results show that, under identical assumptions, SST-based solutions are associated with indicative lifecycle CO₂ emission reductions of approximately 10–30% compared to conventional transformers, depending on power rating and operating profile (≈90–1000 t CO₂ over 25 years across the four cases). These reductions are primarily driven by lower operational losses and reduced material intensity, while additional system-level benefits arise from enhanced controllability and compatibility with renewable-rich and hybrid AC/DC grids. The study also identifies key challenges that influence the sustainability performance of SSTs, including higher capital cost, thermal management requirements, and the long-term reliability of power-electronic components. Overall, the results indicate that SSTs represent a relevant enabling technology for future low-carbon power systems, while highlighting the importance of transparent assumptions and lifecycle-oriented evaluation when comparing emerging grid technologies. Full article

This critical review explores the transformative impact of artificial intelligence (AI), particularly machine learning (ML) and computer vision (CV), on scanning electron microscopy (SEM) and energy dispersive spectroscopy (EDS) for metallic microstructure analysis, spanning research from 2010 to 2025. It critically evaluates how AI techniques balance automation, accuracy, and scalability, analysing why certain methods (e.g., Vision Transformers for complex microstructures) excel in specific contexts and how trade-offs in data availability, computational resources, and interpretability shape their adoption. The review examines AI-driven techniques, including semantic segmentation, object detection, and instance segmentation, which automate the identification and characterisation of microstructural features, defects, and inclusions, achieving enhanced accuracy, efficiency, and reproducibility compared to traditional manual methods. It introduces the Microstructure Analysis Spectrum, a novel framework categorising techniques by task complexity and scalability, providing a new lens to understand AI’s role in materials science. The paper also evaluates AI’s role in chemical composition analysis and predictive modelling, facilitating rapid forecasts of mechanical properties such as hardness and fracture strain. Practical applications in steelmaking (e.g., automated inclusion characterisation) and case studies on high-entropy alloys and additively manufactured metals underscore AI’s benefits, including reduced analysis time and improved quality control. Extending prior reviews, this work incorporates recent advancements like Vision Transformers, 3D Convolutional Neural Networks (CNNs), and Generative Adversarial Networks (GANs). Key challenges—data scarcity, model interpretability, and computational demands—are critically analysed, with representative trade-offs from the literature highlighted (e.g., GANs can substantially augment effective dataset size through synthetic data generation, typically at the cost of significantly increased training time). Full article

Large-diameter butterfly valves are essential control components in high-flow hydraulic systems, where blade geometry directly impacts operational reliability, energy efficiency, and lifecycle cost. This study presents an integrated structural–hydraulic optimization of a DN3000 Boving butterfly valve blade rated for a maximum operating pressure of 10 bar with comparative analysis of a conventional flat blade and an optimized curved blade. The work applies a CFD–FEM framework specifically to DN3000 Southern African valves, which is rare in the literature. Numerical simulations evaluated stress distribution, deformation, pressure losses, and flow stability under design and hydrostatic test conditions. The curved blade achieved a 58.6% reduction in peak von Mises stress, a 50% reduction in weight, a 22% reduction in load loss, and a 33% reduction in actuation torque requirements, while maintaining seal integrity. Cost analysis revealed a 50% reduction in material costs and simplification of manufacturing. The results confirm that the introduction of curvature significantly improves structural strength and hydraulic efficiency, thus providing a reproducible framework for the design of lighter and more economical valves in hydropower, municipal and industrial applications. Full article

This research examines the feasibility of recovering and recycling condensate water, a waste byproduct generated by Atlas Copco ZR315 FF industrial air compressors utilizing oil-free rotary screw technology with integrated dryers. Given the growing severity of global water scarcity, finding alternative water sources is essential for sustainable industrial practices. This study specifically evaluates the potential of capturing and treating compressed air condensate as a viable method for water recovery. The investigation analyzes both the quantity and quality of condensate water produced by the ZR315 FF unit. It contrasts this recovery approach with traditional water production methods, such as desalination and atmospheric water generation (AWG) via dehumidification. The findings demonstrate that recovering condensate water from industrial air compressors is a cost-effective and energy-efficient substitute for conventional water production, especially in water-stressed areas like Morocco. The results show a significant opportunity to reduce industrial water usage and provide a sustainable source of process water. This research therefore supports the application of circular economy principles in industrial water management and offers practical solutions for overcoming water scarcity challenges within manufacturing environments. Full article

Kiln-fired clay bricks are energy-intensive and carbon-heavy. This study develops and validates kiln-free, pressure-free, and ambient-cured geopolymer (GPM) bricks made from uncalcined clay and Class F fly ash. A two-stage experimental program screened 33 mixes (12–16 M NaOH and 396 cubes tested at 14–90 days) and then scaled six optimized mixes to 90 full-size bricks for mechanical, durability, and microstructural evaluation. Bricks with an optimal mix of 20–30% clay and 70–80% fly ash achieved a compressive strength of up to 32.5 MPa, satisfying ASTM C62 (for severe weathering) requirements. Relative to fired clay units, GPM bricks delivered +61% average compressive strength (up to +91%), +56.5% average modulus of rupture (up to +103%), 6–29% lower water absorption, and 42–84% higher UPV while their strength losses after 28-day immersion in 5% H₂SO₄ or 3.5% NaCl were only ~3–5%. SEM confirmed a dense N-A-S-H gel matrix with reduced porosity. Eco-efficiency analysis showed ~95% lower embodied CO₂ (0.26–0.31 vs. 5.5 kg eCO₂ per brick) and ~35% lower cost per MPa of strength than fired clay bricks. The findings demonstrate a practical, low-carbon brick manufactured without mechanical pressing or heat curing, delivering verified performance and durability under ambient conditions. Full article

This study is situated at the critical stage of comprehensive implementation of China’s territorial spatial planning system, addressing the strategic need for planning evaluation and optimization. We innovatively construct a Computable General Equilibrium Model for China’s Territorial Spatial Planning (CTSPM-CHN) that integrates dual factors of construction land costs and energy consumption costs. Through designing two policy scenarios of rigid constraints and structural optimization, we systematically simulate and evaluate the dynamic impacts of different territorial spatial governance strategies on macroeconomic indicators, residents’ welfare, and carbon emissions, revealing the multidimensional effects and operational mechanisms of territorial spatial planning policies. The findings demonstrate the following: First, strict implementation of land use scale control from the National Territorial Planning Outline (2016–2030) could reduce carbon emission growth rate by 12.3% but would decrease annual GDP growth rate by 0.8%, reflecting the trade-off between environmental benefits and economic growth. Second, industrial land structure optimization generates significant synergistic effects, with simulation results showing that by 2035, total GDP under this scenario would increase by 4.8% compared to the rigid constraint scenario, while carbon emission intensity per unit GDP would decrease by 18.6%, confirming the crucial role of structural optimization in promoting high-quality development. Third, manufacturing land adjustment exhibits policy thresholds: moderate reduction could lower carbon emission peak by 9.5% without affecting economic stability, but excessive cuts would lead to a 2.3 percentage point decline in industrial added value. Based on systematic multi-scenario analysis, this study proposes optimized pathways for territorial spatial governance: the planning system should transition from scale control to a structural optimization paradigm, establishing a flexible governance mechanism incorporating anticipatory constraint indicators; simultaneously advance efficiency improvement in key sector land allocation and energy structure decarbonization, constructing a coordinated “space–energy” governance framework. These findings provide quantitative decision-making support for improving territorial spatial governance systems and advancing ecological civilization construction. Full article

The expansion of green hydrogen requires technologies that are both manufacturable at a GW-to-TW power scale and adaptable for decentralized, renewable-driven energy systems. Recent advances in proton exchange membrane, alkaline, and solid oxide electrolysis reveal persistent bottlenecks in catalysts, membranes, porous transport layers, bipolar plates, sealing, and high-temperature ceramics. Emerging fabrication strategies, including roll-to-roll coating, spatial atomic layer deposition, digital-twin-based quality assurance, automated stack assembly, and circular material recovery, enable high-yield, low-variance production compatible with multi-GW power plants. At the same time, these developments support decentralized hydrogen systems that demand compact, dynamically operated, and material-efficient electrolyzers integrated with local renewable generation. The analysis underscores the need to jointly optimize material durability, manufacturing precision, and system-level controllability to ensure reliable and cost-effective hydrogen supply. This paper outlines a convergent approach that connects critical-material reduction, high-throughput manufacturing, a digitalized balance of plant, and circularity with distributed energy architectures and large-scale industrial deployment. Full article

With the growing consumer demand for enhanced driving dynamics in vehicles, optimizing powertrain configurations to balance performance, energy efficiency, and cost has become a critical challenge. Traditional internal combustion engine vehicles (ICEVs) suffer from significant energy consumption and cost penalties when improving acceleration performance. This study systematically evaluates the trade-offs between dynamic performance, energy consumption, and direct manufacturing costs across six powertrain configurations: ICEV, 48 V mild hybrid (48 V), hybrid electric vehicle (HEV), plug-in hybrid electric vehicle (PHEV), range-extended electric vehicle (REV), and battery electric vehicle (BEV). By developing a comprehensive parameterized model, we quantify the impacts of acceleration improvement on vehicle mass, energy consumption, and costs. Key findings reveal that electrified powertrains (PHEV, REV, BEV) exhibit superior cost-effectiveness and energy efficiency. For instance, improving 0–100 km/h acceleration time from 9 to 5 s reduces direct manufacturing costs by only 5.72% for BEV versus 13.38% for ICEV, while PHEV achieves a balanced compromise with 3.40% lower fuel consumption and 10.43% cost increase compared to conventional counterparts. Mechanistic analysis attributes these advantages to higher power density of electric motors and simplified energy transmission in electrified systems. This work provides data-driven insights for consumers and automakers to prioritize powertrain technologies under dynamic performance requirements, highlighting PHEV with driving range of 50 km as the optimal choice for harmonizing driving experience, energy economy, and affordability. The results of this study assist automakers in optimizing the technology pathways of vehicle powertrain, within the consumer demand for dynamic performance. This plays a crucial role in advancing the automotive industry’s overall fuel consumption and energy consumption, thereby contributing to sustainable development. Full article

This study investigates how economic development interacts with sustainability performance in the European Union, focusing on the structural and technological factors that shape progress in the green transition. Using Eurostat data for 27 EU member states over the period 2015–2023, the analysis employs panel econometric models (Pooled Ordinary Least Squares, Fixed Effects, and Random Effects) to explore how circular economy performance, innovation capacity, human capital, and renewable energy use influence environmental and economic outcomes across member states. The results show that R&D intensity and skilled human resources are key drivers of sustainability. Higher levels of circular material use and resource productivity contribute to long-term competitiveness. In contrast, uneven progress in renewable energy deployment points to persistent regional disparities and possible structural constraints that limit convergence. Northern and Western Europe record the strongest advances in innovation and environmental efficiency, whereas Southern and Eastern regions remain affected by industrial legacies and lower absorptive capacity. The findings highlight that, in the short term, renewable energy expansion may involve adjustment costs and potential trade-offs with economic competitiveness in less technologically developed economies. This study provides new comparative evidence on the differentiated pathways of the green transition across the EU. Policy implications suggest the need to reinforce R&D investment, expand circular manufacturing, and support an inclusive technological transition consistent with the European Green Deal and the United Nations 2030 Agenda. Full article

In the classic energy sector, as well as in the manufacturing and process industries, Programmable Logic Controller (PLC) systems are used for electrical substation control. However, PLCs frequently do not support the communication protocols defined on the standard International Electrotechnical Commission (IEC) 61850. Therefore, this paper presents a vendor-independent method for the integration of Protection and Control (P&C) Intelligent Electronic Devices (IEDs), components of the substation bay level, in PLCs from the substation station level. The method can be used with legacy and modern controllers that offer an open communication interface, where the use of Transmission Control Protocol/Internet Protocol (TCP/IP) is supported. Since many legacy systems offer an open communication interface, this method makes it possible to reuse PLCs, bringing cost efficiency and ecological benefits. The method can be used in a single or redundant way since redundancy is always required in power distribution control. A prototype was developed for the integration over IEC 61850 Manufacturing Message Specification (MMS), and its functional validation is presented in this paper. This solution, besides reducing hardware and software acquisition costs, also contributes to a reduction in electronic waste (E-Waste) and the achievement of Sustainable Development Goals (SDGs). Full article

Per- and polyfluoroalkyl substances (PFAS) represent a class of highly persistent environmental contaminants with exceptional chemical stability. Efficient removal of PFAS from water poses a significant challenge for the chemical industry and constitutes a critical requirement for sustainable environmental development. Membrane technology has demonstrated considerable potential in water treatment due to its low energy consumption and environmentally friendly characteristics. This review comprehensively summarizes recent advances in emerging metal–organic framework (MOF)-, covalent organic framework (COF)-, and hydrogen-bonded organic framework (HOF)-based membranes for highly efficient separation and catalytic degradation of PFAS. We provide a detailed analysis of design strategies for various organic framework membranes (OFMs) and their synergistic separation mechanisms, including size exclusion, electrostatic interactions, adsorption, as well as catalytic degradation based on advanced oxidation processes. Furthermore, we systematically evaluate the performance and applicability of these membranes in practical aquatic environments. Finally, this review outlines future directions toward developing integrated “separation-degradation” membrane processes for practical applications by discussing current challenges concerning material stability, manufacturing costs, and long-term operational efficiency. This review aims to provide theoretical guidance and technical insights for developing next-generation high-performance membranes for PFAS removal. Full article

This study investigates whether Battery Electric Vehicles (BEVs) or Fuel Cell Electric Vehicles (FCEVs) represent the superior alternative to conventional vehicles for last-mile delivery, with a particular focus on large enterprises that prioritize both economic feasibility and environmental performance. Life Cycle Assessment and Life Cycle Cost methodologies are applied to evaluate both technologies across the full cradle-to-grave life cycle within a unified framework. The functional unit is defined as one kilometer traveled by a BEV or FCEV in last-mile transportation, and the system boundary includes vehicle manufacturing, operation, maintenance, and end-of-life treatment. The environmental impacts are assessed using the ReCiPe 2016 Midpoint (H) method implemented in OpenLCA 2.0.4, and normalization follows the standards provided by the official ReCiPe 2016 framework. The East China Power Grid serves as the baseline electricity mix for the operational stage. Regarding GHG emissions, FCEVs demonstrate a 12.36% reduction in carbon dioxide (CO₂) emissions compared to BEVs. This reduction is particularly significant during the operational phase, where FCEVs can lower CO₂ emissions by 53.51% per vehicle relative to BEVs, largely due to hydrogen energy’s higher efficiency and durability. In terms of economic costs, BEVs hold a slight advantage over FCEVs, costing approximately 0.8 RMB/km/car less. However, during the manufacturing phase, FCEVs present greater environmental challenges. It is recommended that companies fully consider which environmental issues they wish to make a greater contribution to when selecting vehicle types. This study provides insight and implications for large companies with financial viability concerns about environmental impact regarding selecting the two types of vehicles for last-mile transportation. The conclusions offer guidance for companies assessing which vehicle technology better aligns with their long-term operational and sustainability priorities. It can also help relevant practitioners and researchers to develop solutions to last-mile transportation from the perspective of different enterprise sizes. Full article

The growing demand for energy-efficient and sustainable manufacturing requires maintenance strategies that extend beyond reliability optimization toward active energy management. This study proposes a Smart Hybrid Maintenance System (SHMS) that integrates Reliability-Centered Maintenance (RCM) and Condition-Based Maintenance (CBM) principles with energy performance assessment. The framework combines classical reliability indicators (MTBF, MTTR, and Availability) with energy-oriented Key Performance Indicators (EEI, EENS, and OEE) to quantify the relationship between machine degradation, operational availability, and energy efficiency. The methodology was validated using two datasets: NASA N-CMAPSS for simulation-based benchmarking and the Smart RDM industrial environment for real process data. Results demonstrate that predictive maintenance supported by the Hybrid Risk Index ($HRI$) reduces unplanned downtime by up to 12%, corresponding to a 7–9% decrease in specific energy consumption and a measurable improvement in the Energy Efficiency Index. By embedding energy metrics into predictive maintenance decision-making, the SHMS enables dual optimization of reliability and energy performance. The proposed approach not only enhances equipment availability and cost efficiency but also supports industrial decarbonization targets, positioning predictive maintenance as a key enabler of energy-aware and sustainable manufacturing aligned with Industry 5.0 objectives. Full article