Search Results (1,388)

The rapid growth and seasonal availability of agricultural materials, such as straws, stalks, husks, shells, and processing wastes, present both a disposal challenge and an opportunity for renewable fuel production. Solar-assisted thermochemical conversion, such as solar-driven pyrolysis, gasification, and hydrothermal routes, provides a pathway to produce bio-oils, syngas, and upgraded chars with substantially reduced fossil energy inputs compared to conventional thermal systems. Recent experimental research and plant-level techno-economic studies suggest that integrating concentrated solar thermal (CSP) collectors, falling particle receivers, or solar microwave hybrid heating with thermochemical reactors can reduce fossil auxiliary energy demand and enhance life-cycle greenhouse gas (GHG) performance. The primary challenges are operational intermittency and the capital costs of solar collectors. Alongside, machine learning (ML) and AI tools (surrogate models, Bayesian optimization, physics-informed neural networks) are accelerating feedstock screening, process control, and multi-objective optimization, significantly reducing experimental burden and improving the predictability of yields and emissions. This review presents recent experimental, modeling, and techno-economic literature to propose a unified classification of feedstocks, solar-integration modes, and AI roles. It reveals urgent research needs for standardized AI-ready datasets, long-term field demonstrations with thermal storage (e.g., integrating PCM), hybrid physics-ML models for interpretability, and region-specific TEA/LCA frameworks, which are most strongly recommended. Data’s reporting metrics and a reproducible dataset template are provided to accelerate translation from laboratory research to farm-level deployment. Full article

Hazardous gas leaks are a major trigger of chemical incidents. If not handled in time, they can easily lead to secondary disasters such as fires and explosions. In recent years, with the construction of hazardous chemical monitoring and early-warning systems in China, large volumes of field operating data from flammable and toxic gas sensors have been accumulated, providing a data foundation for leak-pattern studies grounded in real-world scenarios. In this study, 56 leak samples verified by site feedback were selected. Time-aware interpolation and Z-score normalization were used for preprocessing, and time-series features—including standard deviation of first differences, autocorrelation coefficients, and frequency-domain energy—were extracted. Leak patterns were then identified using two unsupervised approaches: K-Means clustering and a 1D-CNN autoencoder. Results show that K-Means effectively distinguishes macro-patterns such as sustained leaks, instantaneous leaks, fluctuating leaks, and interrupted leaks, while the autoencoder demonstrates stronger capability in extracting temporal features, revealing leak evolution and transition characteristics. The two methods are complementary and together provide a viable route to developing an end-to-end model for leak scenario identification and risk discrimination. This work not only verifies the feasibility of conducting leak-pattern recognition using real GDS data but also offers technical guidance for the intelligent upgrading of hazardous chemical monitoring and early-warning systems. Full article

Improving the energy efficiency of Australia’s ageing housing stock is critical to achieving national decarbonisation and climate resilience goals. Although houses built prior to the introduction of national energy efficiency regulations in the 1990s are commonly assumed to be thermally inefficient, empirical evidence for their performance under Australian climatic conditions remains limited, particularly for prevalent pre-war construction typologies. This study addresses this gap by examining the thermal comfort and energy demand of a representative double-brick house built in the 1930s in Adelaide, Australia. A combined methodology was adopted, integrating long-term environmental monitoring, occupant responses, and building performance simulations conducted in two stages. The first stage evaluated the existing building’s thermal and energy performance to establish a calibrated baseline, while the second stage applied parametric optimisation analysis to assess potential retrofit strategies. Baseline results indicate that the case-study dwelling exhibits strong passive cooling performance in summer, challenging the prevailing assumption that older Australian houses are inherently thermally inefficient. Building on this calibrated baseline, parametric optimisation of 467 retrofit configurations was undertaken and benchmarked against the Australian Nationwide House Energy Rating Scheme (NatHERS). The results show that a combined strategy of increased insulation, reduced infiltration, upgraded glazing, and optimised external shading can reduce total heating and cooling loads by up to 78% compared to the original condition, achieving energy ratings of up to 8.5 NatHERS Stars. The findings demonstrate a transferable workflow that links empirical performance assessment with simulation-based optimisation for evaluating retrofit options in older housing typologies. For pre-war double-brick houses in warm-temperate climates, the results indicate that prioritising airtightness and glazing upgrades offers an effective and feasible retrofit pathway, supporting informed decision-making for designers, owners, and policymakers. Full article

Directed Energy Deposition (DED), as a core branch of additive manufacturing, encompasses two typical processes: laser directed energy deposition (LDED) and wire and arc additive manufacturing (WAAM), which are widely used in manufacturing aerospace engine blades and core components of high-end equipment. In recent years, with the increasing adoption of deep learning (DL) technologies, the research focus in DED has gradually shifted from traditional “process parameter optimization” to “AI-driven process optimization” and “online real-time monitoring”. Given the complex and distinct influence mechanisms of key parameters (such as laser power/arc current, scanning/travel speed) on melt pool behavior and forming quality in the two processes, the introduction of artificial intelligence to address both common and specific issues has become particularly necessary. This review systematically summarizes the application of DL techniques in both types of DED processes. It begins by outlining DL frameworks, such as artificial neural networks (ANNs), recurrent neural networks (RNNs), convolutional neural networks (CNNs), and reinforcement learning (RL), and their compatibility with DED data. Subsequently, it compares the application scenarios, monitoring accuracy, and applicability of AI in DED process monitoring across multiple dimensions, including process parameters, optical, thermal fields, acoustic signals, and multi-sensor fusion. The review further explores the potential and value of DL in closed-loop parameter adjustment and reinforcement learning control. Finally, it addresses current bottlenecks such as data quality and model interpretability, and outlines future research directions, aiming to provide theoretical and engineering references for the intelligent upgrade and quality improvement of both DED processes. Full article

Active distribution networks (ADNs) are rapidly evolving with the integration of distributed energy resources, flexible loads, and energy storage systems. Traditional planning methods, based on passive upgrades and worst-case scenarios, are no longer adequate for high DER penetration and dynamic system behavior. This review highlights the key evolution needs that will drive the evolution towards a more dynamic and optimized active distribution planning. Furthermore, this work reviews the core elements in ADN planning, covering time horizons, objectives, decision variables, uncertainty approaches, and optimal power flow formulations. This work also reviews recent generative AI models applied to active distribution networks, presenting a structured classification and definitions of each generative AI category. Full article

Indoor air quality (IAQ) in high-rise residential buildings is an increasing concern, especially in hot and humid climates where prolonged indoor exposure elevates health risks. This study evaluates the performance of Fresh Air Handling Units (FAHUs) using two complementary approaches: (1) real-time sensor data to quantify IAQ conditions and (2) occupant survey responses to capture perceived comfort and pollution indicators. The results show that floor level did not predict satisfaction, even though AQI data revealed clear differences between flats, suggesting perceptions are driven more by sensory cues than by actual pollutant levels. Longer weekday exposure emerged as a stronger predictor of dissatisfaction. These gaps between perceived and measured IAQ highlight the need for improved ventilation scheduling and greater occupant awareness. FAHUs were found to be inefficient, consuming 21–26% of total building energy while lacking pollutant-specific monitoring capabilities. To address these issues, the study recommends the integration of IoT-enabled sensors for real-time pollutant detection, enhanced facade sealing to minimize external infiltration, and the upgrade of filtration systems with HEPA filters and UV purification. Additionally, AI-driven predictive maintenance and automated ventilation optimization through Building Management Systems (BMS) are suggested. These findings offer valuable insights for improving IAQ management in high-rise buildings, with future research focusing on AI-based predictive modeling for dynamic air quality control. Full article

Rapid industrialization has generated large volumes of acidic wastewater that, without adequate treatment, pose serious environmental and public health risks. Traditional remediation processes, such as chemical neutralization, ion-exchange, and membrane filtration, are effective but costly, energy-intensive, and generate toxic secondary waste. In contrast, acidophilic microalgae offer a sustainable, cost-effective, and eco-friendly alternative. Algae rely on their cellular structure and metabolism to adsorb, absorb, bioaccumulate, and transform toxic metals while simultaneously neutralizing wastewater with minimal secondary waste production. Although acidophilic algae tolerate highly toxic and low pH conditions, their growth rate and biomass productivity, key drivers of algae-based bioremediation, are often compromised under such conditions. Thus, identifying robust species and evolving strains to thrive in these wastewaters without compromising productivity will facilitate adoption of algae-based bioremediation on a large scale. Integrating algal wastewater remediation with biofuel and biofertilizer production can contribute to the circular economy. In this review, we synthesize mechanisms employed by acidophilic algal strains when exposed to acidic and metal-enriched environments to remediate wastewater. We highlight recent studies applying these strains to acidic wastewater remediation and biogas upgrading and discuss current biotechnological tools aimed at enhancing strain performance for future use in commercial systems. Full article

Understanding the pathways through which digital innovation contributes to carbon emission reduction is crucial for designing effective climate policies. Existing studies generally find a negative association between digitalization and carbon emissions, but they often treat this relationship as a “black box” and pay insufficient attention to the distinct local and spatial mechanisms through which digital innovation operates. This paper investigates the impact of digital innovation on city-level carbon emissions in 283 Chinese cities from 2010 to 2020 and decomposes the total effect into a local synergistic effect and a spatial spillover effect using a Spatial Durbin Model. We further conduct an empirical test of the underlying mechanisms, including energy efficiency gains and industrial structure upgrading for the local synergy pathway, and green technology diffusion for the spatial spillover pathway. The results indicate that (1) digital innovation significantly reduces city-level carbon emissions, confirming an overall negative effect; (2) spatial decomposition reveals two simultaneous pathways, with a significant local synergistic effect within cities and a spatial spillover effect to neighboring cities; (3) the mechanism analysis shows that the local synergy is significantly associated with improvements in energy efficiency and industrial upgrading, whereas the spatial spillover is significantly associated with the diffusion of green patents; and (4) the effects are especially pronounced in technology-intensive industries and cities in more advanced regions. These findings imply that carbon reduction driven by digital innovation occurs through both intra-city optimization and inter-city technology diffusion. Therefore, policies should not only motivate cities to strengthen their own digital capacities, but also promote interregional collaboration to enhance positive spillovers and achieve cost-effective and well-coordinated carbon neutrality. Full article

The progressive depletion of high-grade ore bodies has shifted attention toward the exploitation of lower-grade deposits as viable sources of value. In recent years, there has been growing emphasis on mining and processing methods that incorporate sustainability by addressing both environmental and socio-economic considerations. To maximize resource recovery, integrated strategies that combine exploration, grade control drilling, mine planning, and processing are essential. Within this framework, particle sorting has emerged as an effective coarse separation method that can upgrade low-grade feed prior to the more energy-demanding milling and subsequent processing stages. Incorporating screening before particle sorting not only assists in identifying the distribution of metals but also determines the most suitable particle size ranges for sorting performance. This study reports on the applicability of sensor-based sorting technologies to low-grade gold and nickel ores from Australia, with a focus on grade deportment by particle size. The results demonstrate that substantial upgrading of low-grade ores is possible, achieving 70%–80% metal recovery within approximately 30%–40% of the original mass through the use of induction and XRT sensors. Overall, the findings indicate that both induction and XRT sorting methods are broadly effective across ore types, offering enhanced upgrading capability and improved processing efficiency. Full article

Old communities are the foundational units for low-carbon transformation in the background of high-quality urban development and dual carbon goals. However, existing research prioritizes building energy-efficient technologies and macro-level spatial indicators, with limited attention to how community-scale built environments specifically influence residents’ behaviors. This study takes five old communities in Chengdu as its subject and quantitatively measure residents’ perceptions of their built environment. Using multiple regression and subgroup regression analyses, it systematically identifies key built environment factors in old communities that influence low-carbon travel behavior. The results show that: (1) Diversity, accessibility, street connectivity, and aesthetics consistently demonstrated significant negative effects across demographic groups; (2) As people age, the carbon emissions from their travel to tend to decrease. The impact intensity of street connectivity on low-carbon travel varies significantly among different age groups; (3) Compared with woman, men overall have higher travel carbon emissions. All findings indicate that complete spatial functions, clear road networks, and accessible facilities promote low-carbon travel. This offers key insights for upgrading built environments in old communities. Full article

Reducing energy demand and carbon emissions in residential buildings requires more than technological upgrades; it demands a nuanced understanding of occupant behaviour. Residential energy use is shaped by both physical design and human actions, yet behavioural factors remain underexplored, contributing to the energy performance gap. This study addresses this issue by developing and validating a behavioural framework grounded in the Theory of Planned Behaviour (TPB) to examine how attitudes, social norms, perceived control, and environmental awareness influence energy-related decisions. Data were collected through an online survey of 310 households in metropolitan Sydney and analysed using Stata v17 software employing principal component analysis and regression modelling. Results reveal that environmental awareness is the most significant predictor of pro-environmental intention, which strongly correlates with actual behavioural outcomes. While attitudes and perceived control were generally positive, subjective norms and awareness remained moderate, limiting behavioural change. The proposed framework demonstrates strong validity and reliability, offering a practical tool for policymakers, designers, and educators to integrate behavioural insights into sustainable building strategies. By prioritising awareness campaigns and normative interventions, stakeholders can complement technical retrofits with behavioural measures, accelerating progress towards low-carbon housing and benefiting both households and the broader community. Full article

The United Kingdom’s (UK) retrofit revolution is at a crossroads and the efficacy of retrofit interventions is not solely a function of insulation thickness. To truly slash emissions and lift households out of fuel poverty, we must solve the persistent problem of thermal bridging (TB), i.e., the hidden flaws that cause heat to escape, dampness to form, and well-intentioned retrofits to fail. This review moves beyond basic principles to spotlight the emerging tools and transformative strategies to make a difference. We explore the role of advanced modelling techniques, including finite element analysis (FEA), in pinpointing thermal and moisture-related risks, and how emerging materials like vacuum-insulated panels (VIPs) offer high-performance solutions in tight spaces. Crucially, we demonstrate how an integrated fabric-first approach, guided by standards like PAS 2035, is essential to manage moisture, ensure durability, and deliver the comfortable, low-energy homes the UK desperately needs. Therefore, achieving net-zero targets is critically dependent on the systematic upgrade of the building envelope, with the mitigation of TB representing a fundamental prerequisite. The EnerPHit approach applies a rigorous fabric-first methodology to eliminate TB and significantly reduce the building’s overall heat demand. This reduction enables the use of a compact heating system that can be efficiently powered by renewable energy sources, such as solar photovoltaic (PV). Moreover, this review employs a systematic literature synthesis to critically evaluate the integration of TB mitigation within the PAS 2035 framework, identifying key technical interdependencies and research gaps in whole-house retrofit methodology. This article provides a comprehensive review of established FEA modelling methodologies, rather than presenting results from original simulations. Full article

The end-of-life phase of a building, which includes demolition and waste disposal, represents a crucial aspect of sustainable construction. In Europe, construction and demolition (C&D) waste accounts for approximately 40% of the total waste generated in the EU, making its management a global challenge. The EU Construction & Demolition Waste Management Protocol (2024) emphasizes the importance of evaluating, before proceeding with the demolition of a building, whether renovation could be a more efficient solution, considering economic, environmental, and technical aspects. From an economic perspective, demolition costs vary depending on several factors, including project size, structural complexity, techniques employed (conventional or non-conventional), materials to be removed, and local regulations. In addition to the direct costs of the intervention, it is essential to consider indirect impacts, such as the management of construction and demolition (C&D) waste, the removal of hazardous substances, and potential environmental damage to be mitigated. This study analyzes a case located in Italy, in the municipality of Caivano (Metropolitan City of Naples, in Campania region), concerning a building that required energy efficiency improvements and seismic upgrades. The decision to demolish and rebuild proved to be economically more advantageous than renovation, while also allowing a 35% increase in volume, enabling the creation of a greater number of housing units. Through the analysis of this real case study, the aim is to highlight how investments in demolition, if properly planned, designed, assessed, and managed, can effectively contribute to building redevelopment, supporting the transition towards a sustainable construction model in line with the principles of the circular economy. Full article

The energy efficiency of historic buildings is the focus of activities aimed at developing replicable methodologies for implementing innovative technological solutions. In line with this priority, the Sicilian Region has launched a project for the energy retrofitting of 91 heritage sites and buildings across the region. To support the decision-making process, this paper defines criteria and indicators for assessing the compatibility and effectiveness of energy efficiency upgrade solutions for buildings of cultural value. The goal of improving energy performance is framed within broader performance targets, including enhancing user experience, promoting cultural activities for users’ creative growth, and carrying out restoration works to strengthen the identity of the pre-existence. The criteria result from a thorough analysis of the current scientific debate on the energy efficiency of heritage buildings and have been validated through their application to the case study of Palazzo Belmonte-Riso, a listed building in the historical centre of Palermo (Italy). The suggested criteria provide guidance for evaluating implemented projects and developing new design solutions. The research proposes a holistic and multidisciplinary approach aligned with the New European Bauhaus, promoting creative and innovative solutions that embody sustainability, aesthetics, and inclusiveness in addressing key issues on the European Agenda. Full article

Manufacturing agglomeration (MA) is an important driving force for both sustained economic expansion and structural upgrading. Understanding whether and how MA contributes to energy conservation and pollutant mitigation is essential for promoting China’s green transition and offers valuable insight for emerging economies pursuing sustainable growth. The paper first theoretically examines the mechanisms linking MA, energy intensity (EI), and pollutant emission (PE). To overcome the regression bias caused by the heterogeneity of pollutant types among cities, the comprehensive index of PE is constructed. The empirical analysis yields two principal findings. First, MA significantly reduces PE, and this relationship remains robust after a series of tests. Second, EI plays a significant mediating role between MA and PE, that is, MA can achieve the reduction targets of PE by reducing EI. Therefore, in addition to its established role in fostering economic growth, MA should be utilized for its environmental advantages. Policymakers should give greater weight to the capacity of MA to enhance energy conservation and emission reduction, so as to stimulate the positive interaction among MA, EI, and PE, and thereby formulate more differentiated policies. Full article