Search Results (48)

Hydrogen is increasingly recognized as a clean energy vector and storage medium, yet its viability and strategic role in the Western Balkans remain underexplored. This study provides the first comprehensive techno-economic, environmental, and strategic evaluation of hydrogen production pathways in Albania. Results show clear trade-offs across options. The levelized cost of hydrogen (LCOH) is estimated at 8.76 €/kg H₂ for grid-connected, 7.75 €/kg H₂ for solar, and 7.66 €/kg H₂ for wind electrolysis—values above EU averages and reliant on lower electricity costs and efficiency gains. In contrast, fossil-based hydrogen via steam methane reforming (SMR) is cheaper at 3.45 €/kg H₂, rising to 4.74 €/kg H₂ with carbon capture and storage (CCS). Environmentally, Life Cycle Assessment (LCA) results show much lower Global Warming Potential (<1 kg CO₂-eq/kg H₂) for renewables compared with ~10.39 kg CO₂-eq/kg H₂ for SMR, reduced to 3.19 kg CO₂-eq/kg H₂ with CCS. However, grid electrolysis dominated by hydropower entails high water-scarcity impacts, highlighting resource trade-offs. Strategically, Albania’s growing solar and wind projects (electricity prices of 24.89–44.88 €/MWh), coupled with existing gas infrastructure and EU integration, provide strong potential. While regulatory gaps and limited expertise remain challenges, competition from solar-plus-storage, regional rivals, and dependence on external financing pose additional risks. In the near term, a transitional phase using SMR + CCS could leverage Albania’s gas assets to scale hydrogen production while renewables mature. Overall, Albania’s hydrogen future hinges on targeted investments, supportive policies, and capacity building aligned with EU Green Deal objectives, with solar-powered electrolysis offering the potential to deliver environmentally sustainable green hydrogen at costs below 5.7 €/kg H₂. Full article

Natural resource assets inherently integrate tripartite synthesis of legal, economic, and ecological attributes. They serve dual critical functions as foundational elements supporting the evolution of new-quality productive forces and pivotal mechanisms safeguarding ecosystemic integrity. It has become a global consensus and direction of action to advance comprehensive supervision of natural resource assets and practice the concept of “Community of Life for Human and Nature”. Under the background of the super-ministry system restructuring in China, comprehensive supervision of natural resource assets remains challenged by system fragmentation in supervision objectives and multifaceted interest conflicts among stakeholders. In light of this, this research focuses on the theoretical justification and system optimization of the comprehensive supervision of natural resource assets in China. Using comparative analysis and normative analysis methods, we validate the system’s function on the comprehensive supervision of natural resource assets, summarize foreign experiences, and ultimately aim to explore the optimization pathway of the legal system for the comprehensive supervision of natural resource assets. The results show the following: (1) The choice of the legal system for the comprehensive supervision of natural resource assets emerges as the functional product aligning societal objectives, the rational paradigm for achieving efficient resource allocation, and the adaptive response to the external effects of common property. (2) The system supply of comprehensive supervision of natural resource assets in foreign countries is characterized by normative convergence in conceptual elements and typological categorization in objectives and objects. Therefore, this research recommends that, in order to optimize the system of the comprehensive supervision of natural resource assets in China, (1) in terms of protection of source, natural resource assets should be categorized, with operational natural resource assets focusing on management and public welfare natural resource assets focusing on conservation. (2) In terms of valuation, the economic valuation of natural resource assets should be integrated with ecosystem service assessments to enhance fair market equity. (3) In terms of method, the big data center should be established to enable the synergistic integration of technological innovation and system reforms. (4) In terms of subject, requiring the participation of various government departments, non-governmental organizations, the general public, and other parties could realize the connection of different legal bases for the comprehensive supervision of natural resource assets and the balance of multiple rights and interests, which should help to achieve balanced resource efficiency and biodiversity conservation and safeguard national ecological security. Full article

The movement towards low-emission and sustainable building practices has driven increased use of natural, carbon-based materials such as wood. While these materials offer significant environmental advantages, their inherent flammability introduces new challenges for timber building safety. Despite advancements in fire protection standards and building regulations, the risk of fire incidents—whether from technical failure, human error, or intentional acts—remains. The rapid detection of fire onset is crucial for safeguarding human life, animal welfare, and valuable assets. This study investigates the potential of monitoring fire precursor gases emitted inside building structures during pre-ignition and early combustion stages. The research also examines the sensitivity and effectiveness of commercial smoke detectors compared with custom sensor arrays in detecting these emissions. A representative structural sample was constructed and subjected to a controlled fire scenario in a laboratory setting, providing insights into the integration of gas sensing technologies for enhanced fire resilience in sustainable building systems. Full article

The transition toward sustainability in the construction industry requires integrated tools that align with circular economy principles. This study introduces the Interactive Stakeholder Journey Map in Construction (ISJMC), an innovative visual and systemic tool that supports waste management and circularity throughout the life cycle of construction assets. Although the sector is economically significant, it remains one of the main contributors to environmental degradation due to high resource consumption and low waste recovery rates. Developed according to EN 15643-3:2012, a European standard that provides a framework for assessing the social sustainability of construction works, focusing on aspects such as accessibility, health, and comfort and grounded in the Design Thinking methodology, ISJMC enables mapping stakeholder interactions, touchpoints, and responsibilities across all life cycle stages, including initiative, design, procurement, construction, use, and end of life. A systematic literature review and collaborative workshops guided the tool’s development and validation. The application in a real case involving a medium-sized Brazilian construction company helped identify significant pain points and opportunities for implementing circular practices. The results demonstrate that ISJMC (i) facilitates a systemic and visual understanding of material and information flows, (ii) promotes transparent mapping of resource value to support better decision-making, and (iii) encourages the identification of circularity opportunities while fostering collaboration among stakeholders. The tool revealed critical challenges related to waste generation and management. It supported co-creating sustainable strategies, including improved material selection, lean construction practices, and stronger supplier engagement. By translating complex standards into accessible visual formats, ISJMC contributes to the academic field, supports practical applications, and offers a foundation for expanding circular approaches in construction projects. Full article

Natural Gas Liquid (NGL) pumps are critical assets in oil and gas operations, where unplanned failures can result in substantial production losses. Traditional maintenance approaches, often based on static schedules and expert judgement, are inadequate for optimising both availability and cost. This study proposes a novel Artificial Intelligence (AI)-based methodology and digital tool for optimising NGL pump maintenance using limited historical data and real-time sensor inputs. The approach combines dynamic reliability modelling, component condition assessment, and diagnostic logic within a unified framework. Component-specific maintenance intervals were computed using mean time between failures (MTBFs) estimation and remaining useful life (RUL) prediction based on vibration and leakage data, while fuzzy logic- and rule-based algorithms were employed for condition evaluation and failure diagnoses. The tool was implemented using Microsoft Excel Version 2406 and validated through a case study on pump G221 in a Saudi Aramco facility. The results show that the optimised maintenance routine reduced the total cost by approximately 80% compared to conventional individual scheduling, primarily by consolidating maintenance activities and reducing downtime. Additionally, a structured validation questionnaire completed by 15 industry professionals confirmed the methodology’s technical accuracy, practical usability, and relevance to industrial needs. Over 90% of the experts strongly agreed on the tool’s value in supporting AI-driven maintenance decision-making. The findings demonstrate that the proposed solution offers a practical, cost-effective, and scalable framework for the predictive maintenance of rotating equipment, especially in environments with limited sensory and operational data. It contributes both methodological innovation and validated industrial applicability to the field of maintenance optimisation. Full article

Health indicators (HIs) are quantitative indices that assess the condition of engineering systems by linking sensor data with monitoring, diagnostic, and prognostic methods to estimate the remaining useful life (RUL). Digital twins (DTs), which serve as digital representations of physical assets, enhance system monitoring, diagnostics, and prognostics by operationalizing analytic capabilities derived from sensor data. This paper explores the integration of HIs and DTs, illustrating their roles in condition-based maintenance and structural health monitoring. The methodologies discussed span data-driven and physics-based approaches, emphasizing their applications in rotary machinery, including bearings and gears. These approaches not only detect anomalies but also predict system failures through advanced modeling and machine learning (ML) techniques. The paper provides examples of HIs derived from vibration analysis and soft sensors and maps future research directions for improving health monitoring systems through hybrid modeling and uncertainty quantification. It concludes by addressing the challenges of data labeling and uncertainties and the role of HIs in advancing performance engineering, making DTs a pivotal tool in predictive maintenance strategies. Full article

Power transformers (PTs) play a vital role in the electrical power system. Assessing their health to predict their remaining useful life is essential to optimise maintenance. Scheduling the right maintenance for the right equipment at the right time is the ultimate goal of any power system utility. Optimal maintenance has a number of benefits: human and social, by limiting sudden service interruptions, and economic, due to the direct and indirect costs of unscheduled downtime. PT now produces large amounts of easily accessible data due to the increasing use of IoT, sensors, and connectivity between physical assets. As a result, power transformer prognostics and health management (PT-PHM) methods are increasingly moving towards artificial intelligence (AI) techniques, with several hundreds of scientific papers published on the topic of PT-PHM using AI techniques. On the other hand, the world of AI is undergoing a new evolution towards a third generation of AI models: large-scale foundation models. What is the current state of research in PT-PHM? What are the trends and challenges in AI and where do we need to go for power transformer prognostics and health management? This paper provides a comprehensive review of the state of the art in PT-PHM by analysing more than 200 papers, mostly published in scientific journals. Some elements to guide PT-PHM research are given at the end of the document. Full article

This paper investigates the design of a digital product passport (DPP) model based on the asset administration shell (AAS) framework to support the circular economy while ensuring cross-industry applicability. In a circular economy, resources are continuously reused, fostering more sustainable manufacturing. The European Commission’s initiatives target this issue, with the DPP playing a critical role in sharing product sustainability information, such as product composition and repairability, throughout its lifecycle. However, a widely applicable DPP approach has yet to be established. This study consolidates existing standards, and scientific literature to develop a data model that aligns with circular economy principles. Using the AAS framework initially developed by the Plattform Industrie 4.0, we mapped the data requirements to submodel templates and addressed gaps in the data needed for real-life implementation. The results demonstrate that the proposed DPP data model is specific enough for practical use cases, such as the upcoming EU battery passport, while remaining flexible enough for application across various industries. The AAS framework’s adaptability and comprehensive data exchange capabilities make it a suitable foundation for developing DPPs that support the transition to a circular economy. Full article

Asset accumulation in later life is a pressing issue in Japan due to the growing gap between life expectancy (87.14 years for women, 81.09 years for men in 2023) and the retirement age (65 or less). This gap heightens financial insecurity, emphasizing the need to meet asset goals by 65. Hyperbolic discounting, driven by present-biased preferences, often hinders this process, but empirical evidence for those aged 65 and older remains limited. Moreover, prior research has overlooked the varying impacts of hyperbolic discounting across different wealth levels. This study addresses these gaps by analyzing data from 6709 active Japanese investors aged over 65 (2023 wave) using probit regression. Wealth thresholds are categorized into four levels: JPY 20 million, JPY 30 million, JPY 50 million, and JPY 100 million. The results show that hyperbolic discounting significantly impairs asset accumulation at the JPY 100 million level but not at lower thresholds. This effect likely reflects the complex nature of hyperbolic discounting, which primarily affects long-term savings and investments. The findings underscore the importance of addressing hyperbolic discounting in later-life financial planning. Recommendations include implementing automatic savings plans, enhancing financial literacy, and incorporating behavioral insights into planning tools to support better asset accumulation outcomes. Full article

With the growing emphasis on sustainability, awareness of the environmental impacts and negative potential inherent in current business systems has increased. The circular economy (CE) represents an innovative approach that transforms the traditional linear economy into a restorative system, focussing on extending the life cycle of materials through continuous circulation. The Circular B project aims to develop an international framework that considers multiple facets of the CE, including material and asset management and the use of components in the built environment throughout the entire life cycle of the value chain. The primary objective of the CE is to eliminate waste and pollution (e.g., carbon reduction) and strengthen the resilience of the value chain. However, the current implementation of circular strategies has not yet been found to be effective, with several challenges that cause adverse impacts. This study focuses on investigating and analyzing these challenges, particularly in the cultural and societal domains, using both qualitative and quantitative approaches. The scope of the questionnaire was to identify (1) awareness and understanding, (2) cultural attitude, (3) barriers to adoption, (4) incentives and motivations, (5) participation and engagement, and (6) education and training. A questionnaire was distributed to 270 respondents, with anonymous responses collected. The survey included eight questions specifically designed to address cultural and societal challenges. The survey was conducted with participants from various sectors, including academia, local authorities, industry professionals, consultants, and others collected from all over the world, ensuring diverse perspectives. The main weaknesses found based on this survey are related to (1) budget constraints due to high costs of reintegrating in the loop of materials or components due to the complexity of circular processes, (2) applicability on the market remains still limited, (3) the importance of planning and design in the initial phases, (4) the importance of establishing a comprehensive network to enhance collaboration among stakeholders, and (5) inadequate policies. The insights gained from this study will help stakeholders, such as constructors, maintainers, engineers, designers, and consultants, across various organizations in the value chain to develop practical solutions to mitigate these challenges and improve the overall business system. Full article

In fields such as manufacturing and aerospace, remaining useful life (RUL) prediction estimates the failure time of high-value assets like industrial equipment and aircraft engines by analyzing time series data collected from various sensors, enabling more effective predictive maintenance. However, significant temporal diversity and operational complexity during equipment operation make it difficult for traditional single-scale, single-dimensional feature extraction methods to effectively capture complex temporal dependencies and multi-dimensional feature interactions. To address this issue, we propose a Dual-Path Interaction Network, integrating the Multiscale Temporal-Feature Convolution Fusion Module (MTF-CFM) and the Dynamic Weight Adaptation Module (DWAM). This approach adaptively extracts information across different temporal and feature scales, enabling effective interaction of multi-dimensional information. Using the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) dataset for comprehensive performance evaluation, our method achieved RMSE values of 0.0969, 0.1316, 0.086, and 0.1148; MAPE values of 9.72%, 14.51%, 8.04%, and 11.27%; and Score results of 59.93, 209.39, 67.56, and 215.35 across four different data categories. Furthermore, the MTF-CFM module demonstrated an average improvement of 7.12%, 10.62%, and 7.21% in RMSE, MAPE, and Score across multiple baseline models. These results validate the effectiveness and potential of the proposed model in improving the accuracy and robustness of RUL prediction. Full article

The chemical industry, a cornerstone of the global economy essential for modern life, has raised significant concerns due to its unique nature. Chemical technologies often require high energy inputs, involving ecotoxic reagents thus assessing risks from an economic standpoint becomes complex. While the economic aspects of chemical technologies have been discussed and economic tools have been used to inform investment decisions in this field, many fundamental issues remain unexplored, such as the clear definition of chemical technology economics and the reasons for its importance. The primary contribution of this article is to synthesize insights into these fundamental issues and propose pathways for future research in chemical technology economics. This review is divided into two sections: the first provides an overview of the significance of economic factors in chemical technologies, and the second explores the fundamentals of economics and their application to chemical technology considerations. Our research underscores that economic theories significantly influence the profile of chemical technologies, viewing the chemical sector as a dual asset. First, the sector has a unique opportunity to lead the way in promoting sustainable economic development, and second, it can adopt economic behaviors that align with environmental and societal needs. Full article

The proportional hazards model (PHM) is a vital statistical procedure for condition-based maintenance that integrates age and covariates monitoring to estimate asset health and predict failure risks. However, when dealing with multi-covariate scenarios, the PHM faces interpretability challenges when it lacks coherent criteria for defining each covariate’s influence degree on the hazard rate. Hence, we proposed a comprehensive machine learning (ML) formulation with Interior Point Optimizer and gradient boosting to maximize and converge the logarithmic likelihood for estimating covariate weights, and a K-means and Gaussian mixture model (GMM) for condition state bands. Using real industrial data, this paper evaluates both clustering techniques to determine their suitability regarding reliability, remaining useful life, and asset intervention decision rules. By developing models differing in the selected covariates, the results show that although K-means and GMM produce comparable policies, GMM stands out for its robustness in cluster definition and intuitive interpretation in generating the state bands. Ultimately, as the evaluated models suggest similar policies, the novel PHM-ML demonstrates the robustness of its covariate weight estimation process, thereby strengthening the guidance for predictive maintenance decisions. Full article

The production of multivariate time-series data facilitates the continuous monitoring of production assets. The modelling approach of multivariate time series can reveal the ways in which parameters evolve as well as the influences amongst themselves. These data can be used in tandem with artificial intelligence methods to create insight on the condition of production equipment, hence potentially increasing the sustainability of existing manufacturing and production systems, by optimizing resource utilization, waste, and production downtime. In this context, a predictive maintenance method is proposed based on the combination of LSTM-Autoencoders and a Transformer encoder in order to enable the forecasting of asset failures through spatial and temporal time series. These neural networks are implemented into a software prototype. The dataset used for training and testing the models is derived from a metal processing industry case study. Ultimately, the goal is to train a remaining useful life (RUL) estimation model. Full article

Archaeological sites constitute one of the main tourist attractions in the heritage offerings of most populations. Their ability to convey the ways of life and construction techniques of past societies through physical remains positions them as a culturally significant alternative for visitors. However, their physical conservation, essential for efficiently ensuring information with precision, poses a serious challenge for the various professionals involved, as numerous social and anthropic risks threaten long-term preservation for the enjoyment of future generations. Of all traditional building materials, earth is undoubtedly one of the most fragile and sensitive to loss in the absence of the original protection systems, so that a precise assessment of its threats is essential to minimizing the destruction of these non-renewable assets. The objective of this study is to evaluate the most determining human risk factors within the territorial scope of the Iberian Peninsula, including aspects such as its musealization, suitable interpretation, visit planning, agricultural land use, vandalism and rural depopulation. This is achieved through a literature review and on-site data collection from 85 archaeological sites, as well as the development of an analysis tool to assess the degree of vulnerability, aiming to develop prevention measures. Full article