Search Results (3,466)

Freshwater scarcity and rising wastewater generation have intensified global reliance on desalination and reuse, with reverse osmosis (RO) providing 65–70% of installed desalination capacity and achieving energy reductions from 15 kWhm⁻³ in the 1970s to 1.8–2.5 kWhm⁻³ today. However, fouling caused by organics, inorganics, microorganisms, and colloids remains the major operational challenge, accounting for ≈25% of RO costs and over USD 15 billion annually. This review synthesises fouling mechanisms and mitigation strategies in renewable energy (RE)-driven RO wastewater-treatment systems, where intermittency exacerbates fouling through start–stop cycles and low-shear conditions. Analysis of recent literature highlights that mixed fouling reduces flux by 10–30%, increases transmembrane pressure, and deteriorates permeate quality. Advances in pretreatment (coagulation, MF/UF), antifouling membranes (hydrophilic coatings, zwitterionic surfaces), and cleaning protocols (osmotic backwashing, nanobubbles) have improved performance, yet complete prevention remains elusive. Persistent gaps include predictive fouling models, standardised performance metrics, and scalable green chemistries for silica and combined fouling control. Future directions emphasise integrated solutions combining advanced materials, AI-driven monitoring, and renewable-aware operational strategies, alongside circular economy approaches for brine valorisation. These innovations are critical for achieving sustainable, low-carbon RO systems for global water security. Full article

To achieve low-carbon and economical operation of industrial parks, this paper proposes a two-level sequential optimization framework for the coordinated operation of multiple industrial parks incorporating an extended carbon emission flow (CEF) model. First, an extended CEF model is established in the upper-level model by incorporating the virtual carbon emission characteristics of energy storage systems into the conventional CEF model, thereby enabling the calculation of dynamic node carbon intensity (NCI). The obtained NCI is subsequently transmitted to the lower-level model, which is formulated as a coordinated operation model for multiple industrial parks. Based on the received NCI, peer-to-peer (P2P) electricity and carbon quota trading are incorporated to achieve economical and low-carbon operation through resource sharing. A Nash bargaining model is further introduced to ensure the fair allocation of cooperative benefits. Furthermore, the original Nash bargaining problem is decomposed into two tractable subproblems. To preserve the privacy of participating parks, an adaptive alternating direction method of multipliers (ADMM) is employed to solve the decomposed subproblems in a distributed manner while improving the convergence performance of the algorithm. Finally, case studies based on the IEEE 33-bus system demonstrate the effectiveness of the proposed framework. Full article

Under China’s dual-carbon strategic goals, large-scale public centralized heating plays a critical role in energy conservation. However, traditional manual open-loop control suffers from high response latency. Furthermore, existing unidirectional predictive methods lack dynamic feedback correction mechanisms. To address these issues, this study proposes an intelligent dual-system collaborative control architecture specifically designed for public heating systems. This architecture utilizes a cloud–edge device framework. It establishes a two-way linkage between heating equipment and the cloud decision platform. Consequently, it constructs an integrated regulation framework encompassing forward decision generation, reverse state verification, and dynamic feedback correction. Specifically, the forward module utilizes a Mamba-structured state-space model to generate data-driven boiler operation strategies. Meanwhile, the reverse module employs a Temporal Convolutional Network with Monte Carlo Dropout (TCN-MC Dropout). This probabilistic network enables state inversion evaluation with reliable uncertainty prediction intervals. These two modules are deeply coupled through an adaptive feedback correction mechanism. Together, they significantly improve system stability and operational robustness under complex thermal disturbances. Specifically, the proposed architecture achieves a room temperature compliance rate exceeding 96% and restricts temperature fluctuations to within $\pm 0.75 °\mathrm{C}$. Simultaneously, it reduces boiler energy consumption by over 16.4%. This solution has been successfully deployed in the heating network at Shaanxi Normal University as a representative real-world case study. Ultimately, it provides a practical technical reference for the intelligent upgrading and low-carbon transformation of public centralized heating systems. Full article

This study investigates the impact of alternative external wall systems on energy efficiency, cooling loads, and operational CO₂ emissions in a residential building in Hilla, Babylon, Iraq. An integrated assessment combining calibrated dynamic thermal simulation (DesignBuilder/EnergyPlus) and Life Cycle Assessment (LCA) was employed to evaluate thermal performance, operational energy demand, embodied carbon, and lifecycle emissions under consistent modeling conditions. The baseline model was calibrated against monthly electricity billing data, yielding an NMBE of +3.65% and a CV(RMSE) of 5.01%, indicating acceptable calibration performance for the investigated building and calibration period. Four wall systems were examined: conventional brick (BC), unstabilized rammed earth (RE-U), straw-stabilized rammed earth (RE-S), and fly-ash brick (FA). All four were evaluated using dynamic thermal simulation and operational-energy analysis, while the quantitative lifecycle environmental assessment was limited to BC, RE-S, and FA due to the lack of a sufficiently representative dataset for the unstabilized compacted-soil configuration (RE-U). The results highlight indicator-specific distinctions: RE-S achieved the greatest reductions in annual electricity consumption (20.6%), peak-summer cumulative cooling load (35.3%), and cooling-related operational CO₂ emissions (25.0%); RE-U provided the most favorable free-running operative-temperature response on 31 July under non-mechanical cooling conditions; and among the three configurations included in the quantitative LCA, FA exhibited the lowest embodied-carbon benchmark. The LCA findings for BC, RE-S, and FA further demonstrate that environmental impacts vary across lifecycle stages and indicators, underscoring the importance of interpreting embodied and operational carbon within clearly defined methodological boundaries. The study concludes that achieving low-carbon housing requires an integrated design approach combining low-carbon materials, improved envelope performance, and enhanced operational efficiency, with lifecycle assessment incorporated into early design decisions to support sustainable outcomes. Full article

The growing penetration of renewable energy sources introduces significant uncertainties into integrated energy systems (IESs). Conventional single-timescale management strategies, typically designed for static power balance, fail to address the symmetry of source-load uncertainties arising from both supply and demand sides. To address this challenge, this paper proposes a multi-timescale optimal scheduling framework that integrates demand response (DR) and multi-energy flow coupling. The framework adopts a hierarchical progressive strategy across day-ahead, intra-day, and real-time stages. The day-ahead stage optimizes the economic baseline with an hourly resolution. The intra-day stage conducts rolling correction at 15 min intervals to activate slow-response equipment flexibility, boosting combined heat and power (CHP) generation by 40.70% and increasing waste-heat cooling consumption by 41.12%. The real-time stage employs energy storage at 5 min resolution to suppress fluctuations, maintaining electricity, heat, and cooling load deviations, respectively, at remarkably low levels of 0.17%, 0.10%, and 0.06%. Comparative results show that with power-to-gas (P2G) integration, the system purchases off-peak electricity for synthetic natural gas production, cutting gas procurement costs by 12.70% and reducing net carbon emissions from 5.14 t to 4.91 t. DR mechanisms enable a gas–electricity substitution strategy that lowers electricity purchase costs by 9.97%, reduces evening peak electric vehicle (EV) charging load by 8.32%, and decreases charging expenses by 15%. Full article

Silica aerogels are among the most extraordinary porous materials produced through sol–gel chemistry, distinguished by ultralow density, exceptionally high porosity, large specific surface area, and extremely low thermal conductivity. Despite these characteristics, widespread application of conventional silica aerogels has been constrained by inherent brittleness, poor mechanical strength, and moisture sensitivity. Significant research has therefore focused on silica aerogel composites, in which reinforcing or functional phases—fibers, polymers, carbon nanomaterials, metal oxides, and biopolymers—are integrated into the silica network to enhance mechanical robustness, flexibility, hydrothermal stability, electrical conductivity, catalytic activity, and multifunctionality while largely preserving the parent aerogel’s desirable properties. We review the synthesis, characterization, properties, and applications of silica aerogel composites. Sol–gel processing and drying technologies are discussed, followed by composite-formation strategies and the advanced techniques used to evaluate structural, mechanical, thermal, surface, and functional properties. The effects of reinforcing phases on mechanical performance, thermal conductivity, and hydrothermal stability are analyzed, and current and emerging applications in thermal insulation, environmental remediation, catalysis, acoustic damping, aerospace systems, biomedical engineering, and energy storage are highlighted. Finally, key challenges and future directions involving multifunctional materials, green synthesis, and data-driven materials design are discussed. Full article

Accurate estimates of maintenance energy requirements are essential for precision feeding, yet breed-specific values remain scarce for Azawak cattle. This study quantified net energy for maintenance (NE_m), metabolizable energy for maintenance (ME_m), and the efficiency of metabolizable energy use for maintenance (k_m) in Azawak bulls. Nine bulls were evaluated in a replicated 3 × 3 Latin-square design and assigned to three graded metabolizable energy supply levels, designated low, intermediate, and high. The feeding levels were established by varying daily feed allowance while maintaining the same basal forage-based ration. Indirect calorimetry was performed using the GreenFeed system to quantify respiratory gas exchange, urinary nitrogen was determined, and heat production was calculated according to Brouwer’s equation. Increasing energy supply significantly (p < 0.05) enhanced oxygen consumption, carbon dioxide and methane production, urinary nitrogen excretion, and heat production. The heat production (HP) and metabolisable energy intake (MEI) relationship was analysed using a linear mixed-effects model, with bull included as a random effect to account for repeated measurements. The resulting population-level relationship was ln(HP) = −1.1246 + 0.8985 × MEI. The estimated intercept was −1.1246 ± 0.0536 (95% CI: −1.2297 to −1.0195), whereas the MEI slope was 0.8985 ± 0.0850 (95% CI: 0.7320 to 1.0651; p < 0.001). Back-transformation of the intercept yielded an NE_m of 0.325 MJ/kg BW^0.75/day (95% CI: 0.292–0.361), whereas ME_m, estimated at the point where predicted heat production equalled metabolizable energy intake, was 0.517 MJ/kg BW^0.75/day (95% CI: 0.467–0.576). The corresponding k_m was 0.629 (95% CI: 0.566–0.688), indicating that 62.9% of metabolizable energy supplied at maintenance was converted into net energy. These results provide the first breed-specific maintenance energy coefficients for Azawak bulls and offer a robust basis for improving ration formulation, energy-use efficiency, and feeding precision in this important indigenous cattle breed. Full article

Renewable energy scenarios are widely used for power system stochastic optimization and risk evaluation, yet massive redundant scenarios boost computational complexity, waste resources and destabilize results, necessitating scenario reduction. This paper proposes an improved two-stage clustering method to overcome the manual parameter tuning defects of conventional clustering-based reduction. Specifically, the Snow Ablation Optimizer is embedded into DBSCAN to auto-adjust core hyperparameters, realizing efficient initial scenario reduction and suppressing the interference of abnormal data. Afterwards, K-means optimized via the Calinski–Harabasz index is adopted for secondary reduction to adaptively identify optimal cluster numbers and enhance the representativeness of reserved scenarios. Basic comparative simulations validate its technical superiority: handling 5000 raw scenarios only takes around 2 min, with the Wasserstein distance decreased by 7.73% and 17.79% versus backward reduction and forward selection, while the silhouette coefficient rises by 12.73% and Davies–Bouldin index drops by 7.95% compared with classic K-means. Further stochastic unit commitment tests quantify tangible economic and low-carbon gains in day-ahead scheduling, and empirical coefficient-based scaling analysis extends these benefits to long-term grid operation and policy deployment for TSOs/DSOs. The integrated results confirm the method’s technical, economic and sustainable merits, delivering actionable quantitative support for high-renewable power system low-carbon planning and energy policy formulation. Full article

In recent years, traditional cast-in-place concrete construction for pre-drilling engineering in unconventional natural gas fields has generated large amounts of waste concrete, consumed significant resources, and prolonged project schedules. To address these issues, this study proposes a prefabricated pavement slab system as a green and low-carbon alternative. Based on vehicle load surveys at shale-gas well sites in southwestern China, three loading conditions (design, overload, and ultimate axle loads) were defined. Theoretical calculations were then performed for reinforcement design, crack-width control, and local bearing capacity verification. A full-scale precast slab (3000 × 1495 × 150 mm) was fabricated and tested under static monotonic loading to measure deflection, crack development, steel strain, and concrete strain until failure. Separately, a three-dimensional finite element model of a four-panel pavement system (including a mortar-leveling layer and soil subgrade) was developed in ANSYS to simulate static and, preliminarily, moving loads. The experimental slab reached an ultimate load of about 365 kN (based on a single specimen, and thus not statistically representative), with ductile bending failure and crack/deflection patterns typical of reinforced concrete. The numerical model reproduced the cracking load and peak capacity with deviations below 17% from the test data, though post-cracking deflections were underestimated. Overall, the results demonstrate that the proposed prefabricated system is structurally feasible for heavy-duty drilling sites. It enables factory production, rapid on-site assembly, and reuse after dismantling, thereby reducing construction waste, shortening timelines, and supporting energy conservation and emission-reduction goals in the context of China’s green building policies. Full article

The decarbonization of maritime transport demands propulsion and onboard energy systems that simultaneously achieve ultra-low emissions, high power density, and robust operational reliability. Nuclear propulsion, particularly when coupled with small modular reactors (SMRs), offers a compelling pathway due to its near-zero operational emissions and exceptional energy density; however, the efficient utilization of high-grade nuclear thermal energy under shipboard constraints remains a critical challenge. To address this issue, this study presents a comprehensive thermodynamic and exergy-based assessment of a novel cascaded waste heat recovery (WHR) architecture designed for a 100 MW class nuclear-powered vessel, integrating a supercritical carbon dioxide (sCO₂) cycle with downstream steam Rankine (SRC) and Kalina cycles. Detailed process modeling is performed using Aspen HYSYS to quantify energy and exergy performance at the component and system levels. The proposed cascade exploits the complementary thermodynamic characteristics of each cycle, enabling staged recovery of high-, medium-, and low-grade heat from the nuclear secondary loop. Results indicate that while the sCO₂ cycle dominates gross power generation due to its high power density and favorable high-temperature performance, its net efficiency is constrained by substantial compression work and associated auxiliary losses. In contrast, the SRC and Kalina cycles exhibit significantly higher energy efficiencies, demonstrating superior suitability for medium- and low-temperature waste heat utilization. Through thermodynamic synergy, the integrated sCO₂–SRC–Kalina configuration achieves an overall energy efficiency of 20.73%, representing a substantial improvement over a standalone sCO₂-based WHR system. Exergy destruction analysis reveals that system irreversibilities are primarily concentrated in heat exchangers, particularly the primary heat exchanger interfacing the nuclear heat source with the sCO₂ loop, whereas turbomachinery contributions are comparatively minor. These findings highlight heat exchanger design optimization and improved temperature matching as the most effective pathways for further performance enhancement. Overall, this study demonstrates that multi-cycle cascade integration provides a viable and high-efficiency solution for nuclear marine energy recovery, with strong implications for advanced hydrogen energy systems and other high-performance power generation applications requiring compactness, scalability, and thermodynamic robustness. Full article

This study examines growing tensions between household energy security and climate and energy policy objectives in Poland. It aims to analyze household attitudes towards the decarbonization of heating, particularly the phase-out of solid fuels and gas, and to assess perceptions of alternative heat sources as a means of ensuring energy security. The analysis draws on a survey of representatives of 508 households in five municipalities in the Małopolska Voivodeship, characterized by diverse heating structures. The quantitative study was complemented by 18 in-depth interviews with local government representatives. The results indicate low public acceptance of heating decarbonization amid growing energy uncertainty. Respondents recognized the importance of diversifying heat sources and supported retaining a solid-fuel boiler, regardless of its class, as an emergency backup. Energy security was associated more strongly with autonomy, independence, and freedom of choice than with low-carbon technologies. Solid-fuel heating was perceived as strengthening household resilience to supply disruptions and crises. The findings highlight the need to consider household energy security, particularly energy costs and resilience, when designing energy policies and regulatory instruments such as ETS2 (EU Emissions Trading System 2), especially in municipalities with socio-economic and energy conditions similar to those of the municipalities included in the study. Full article

The rapid growth in artificial intelligence (AI) demand has significantly increased the electricity consumption and carbon emissions of computing centers. How to schedule AI requests across computing centers to reduce carbon emissions and electricity costs while maintaining low latency is an essential research problem. Existing schedulers reduce emissions by shifting workloads or balancing resources but usually simplify power system modeling, ignore transmission-side costs and carbon emissions, or make local decisions without batch-level coordination. To better address these problems, we first develop an ILP-based scheduler to get optimized results, but it faces scalability limitations. Then, we propose RAPID, a region-aware and power-informed scheduling framework that integrates static and online heuristic schedulers for large-scale AI request scheduling. Experiments based on real-world GenAI traces and Chinese regional power profiles show that RAPID significantly reduces carbon emissions, electricity costs, and total energy consumption compared to methods from previous works while maintaining zero Service Level Agreement (SLA) violations. Full article

Cement production is associated with substantial carbon dioxide (CO₂) emissions due to the high material and energy intensity of Portland clinker manufacture. Partial clinker replacement with supplementary cementitious materials is one of the most promising strategies for reducing the carbon footprint of cement; however, the thermal activation of aluminosilicate raw materials does not always yield highly reactive products. In this study, a pillaring approach is proposed as a controlled method for modifying the structure of clays and unlocking their latent reactivity. Different clay types—namely, kaolinitic, montmorillonitic, and illite–chlorite clays—were sequentially treated with an aluminum sulfate solution and calcined at 650 °C. Their phase composition and microstructure were characterized by X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FTIR), and scanning electron microscopy (SEM), while specific surface area was determined by BET analysis and pozzolanic activity. The results showed that pillaring doubled the specific surface area of montmorillonitic (2:1) and illite–chlorite (2:1:1) clays. Replacing 30% of clinker with pillared clays and limestone increased the compressive strength to 86.5 MPa and the flexural strength to 34.6 MPa. The developed low-carbon composite cements also exhibited high durability: the density of the hardened cement mortar increased to 2.410 g/cm³, the strength loss after 200 freeze–thaw cycles decreased to ≤5.5%, and the sulfate resistance coefficient (Ks) increased to 0.98 (with minimal expansion of the samples <0.02%). The proposed approach makes it possible to reduce the carbon footprint of cement by 25–30% while enabling the use of locally available raw materials for the production of competitive low-carbon green cements. Reported reductions of this order are broadly consistent with the known effect of lowering clinker content through supplementary cementitious materials in blended cement systems. Full article

Sustainable urban freight logistics requires routing decisions that jointly account for operating cost, vehicle technology, low-emission-zone (LEZ) access, terrain-sensitive energy use, and battery feasibility. This study develops a policy–terrain-coupled mixed-fleet routing framework integrating LEZ exposure, system-level carbon settlement, terrain-sensitive energy consumption, electric-vehicle (EV) battery feasibility, and route-level EV/internal-combustion-engine vehicle reassignment within a unified daily total operational cost (DTOC) evaluator. An adaptive large-neighborhood search (ALNS) procedure reconstructs feasible routes, while vehicle type is re-evaluated through counterfactual comparison of the complete system objective. The main experiments use 60 enhanced Gehring–Homberger benchmark-derived scenarios and 20 independent seeds, supplemented by ablation, carbon-price, EV-fixed-cost, heuristic-weight, convergence, and customer-scale scalability analyses. The ALNS-based framework achieves the lowest mean DTOC among the tested procedures, albeit with higher runtime. Policy and terrain information alter modeled fleet composition, with topology-dependent cost effects. Lower EV fixed costs consistently increase EV share, whereas carbon-price effects vary across network structures. All runs in the additional 200–1000-customer tests were feasible, although runtime increased with problem size. London- and Madrid-informed cases are treated as archetypes rather than as real-world validation cases. These results provide a basis for scenario screening and comparative planning of policy–terrain interactions before city-specific calibration and deployment. Full article