Search Results (540)

Visual quality control increasingly relies on machine learning (ML), but reliable production adoption requires a governed data-model-service lifecycle rather than an isolated detector. This article presents an implementation-grounded Open Access Platform (OAP) blueprint and audit protocol for visual quality control as a service. The contribution is not a new detector backbone. It is a reproducible governance pattern that connects ontology-based data validation, Neo4j knowledge-graph traceability, Eclipse Arrowhead service discovery, human-in-the-loop annotation, dataset and model registries, operational quality gates, and controlled model adaptation. The platform context is supported by implemented OAP tools for battery carbon-footprint analysis and production planning, while the visual inspection modules are reported at the notebook level. The benchmark protocol uses leakage-safe splits and OK samples with empty label files, separates the MVTec AD augmentation corpus from the unlearning stress-test corpus, and requires FP-on-OK gating with a retrained post-removal reference. The resulting architecture supports semantic traceability, controlled promotion and rollback, human accountability and Industry 5.0-aligned industrial AI governance without overstating the completed evidence. Compared with existing MLOps architectures, OAPs add inspection-specific semantic enforcement, knowledge-graph lineage, and governed adaptation. Full article

The transition to low-carbon energy systems is expanding the use of subsurface resources for heat extraction, energy storage, carbon management, and infrastructure reuse. This review examines hydraulic fracturing as a context-dependent engineering intervention across enhanced geothermal systems, geothermal reuse of depleted reservoirs and wells, unconventional gas, underground hydrogen storage, CO₂-based subsurface engineering, and natural hydrogen. We synthesize how stimulation can improve permeability, connectivity, heat exchange, injectivity, and deliverability, while evaluating constraints related to water use, induced seismicity, leakage, well and caprock integrity, life-cycle emissions, and public acceptance. The central trade-off is that higher stimulation efficiency does not necessarily produce greater sustainability. Short-term gains in flow or energy delivery can increase long-term risks to containment, thermal performance, seismic safety, and environmental accountability. Evidence is strongest for enhanced geothermal systems and commercial unconventional gas, whereas field support remains limited for porous-media hydrogen storage, CO₂-based fracturing, and natural hydrogen. Responsible deployment therefore requires site-specific boundaries, real-time monitoring, multi-physics and data-driven modeling, life-cycle assessment, and adaptive governance. Hydraulic fracturing can enable selected sustainable subsurface applications, but its value depends on balancing engineering performance against long-term environmental and integrity constraints. Full article

Decarbonization of district heating (DH) systems requires practical solutions that can reduce greenhouse-gas (GHG) emissions while utilizing existing infrastructure. Therefore, the study experimentally evaluates hydrogen–methane-based gas co-combustion in a real urban DH installation in Riga, Latvia, using a 6.3 MW hot-water boiler operating under commercial conditions without equipment modification. Experiments were conducted under steady-state operating conditions by blending hydrogen with the baseline methane-based gas at volumetric fractions of 0%, 10%, and 20%. Thermal performance and emissions (CO₂, NO_x, and CO) were monitored during 30 min measurement periods, and three independent experiments were performed for each hydrogen blending level. The experimental data were analyzed using one-way analysis of variance (ANOVA) to evaluate the statistical significance of the observed changes. Stable ignition, flame anchoring, and load-following performance were maintained under all investigated conditions, and no flashback or blow-off events occurred. Boiler efficiency remained essentially constant at approximately 92% (92.1–91.9%), while thermal output was maintained at 6.3 MW. When CO₂ emissions were normalized to useful thermal energy output (kg CO₂/MWh), the specific CO₂ emission intensity decreased from 202 kg/MWh for pure methane-based gas operation to 161 kg/MWh at 20 vol.% hydrogen addition, corresponding to an approximately 20% reduction in the carbon intensity of delivered heat under the investigated operating conditions. Carbon monoxide (CO) emissions remained low (~6–7 mg/kWh) and particulate matter concentrations remained below 1 mg/m³. Nitrogen oxide emissions increased moderately from approximately 40 mg/kWh to 52 mg/kWh due to enhanced combustion temperatures but remained within applicable regulatory limits. No degradation of safety systems, fuel metering equipment, or infrastructure and no leakage events were observed during the experiments. The results demonstrate that hydrogen blending up to 20 vol.% can achieve substantial reductions in the carbon intensity of heat generation while preserving boiler performance and operational safety, confirming hydrogen co-combustion as a practical transitional decarbonization pathway for existing DH systems. They also uncover the potential of hydrogen blending to support the sustainable decarbonization of DH systems by reducing GHG emissions while preserving existing infrastructure and operational reliability. Full article

Against the background of the “dual carbon” goals and the integration of a high proportion of renewable energy, hydrogen energy storage, with its advantages of long duration and large scale storage as well as clean energy conversion, has become an important approach to improving the flexibility and security of energy systems. To address the accident risks associated with leakage from high pressure hydrogen storage in stationary hydrogen energy storage facilities, this study takes an integrated hydrogen energy storage station involving hydrogen production, storage, compression, and utilization as the research object. A numerical model for hydrogen leakage and dispersion from high-pressure storage cylinders in an open environment is established to investigate the effects of leakage aperture, natural ventilation, mechanical ventilation, and emergency shutdown on hydrogen cloud evolution and deflagration risk. The results show that an increase in leakage diameter significantly increases the flammable hydrogen volume and Q9 peak value. Large-scale leakage is prone to local accumulation under the influence of blast walls and obstacles, resulting in a 780 m³ combustible volume and 14.7 m³ Q9; medium-scale leakage has a longer duration, whereas small-scale leakage presents the lowest risk. Under natural wind conditions, crosswind provides better dilution, reducing Q9 by 53%. Mechanical ventilation can effectively reduce the value of Q9 by 36%, with ventilation layout exerting a more significant influence than wind speed. The combined use of mechanical ventilation and emergency shutdown can further reduce the 42% flammable volume and shorten the duration of high concentration hydrogen clouds. The findings can provide guidance for the safety layout, ventilation design, and emergency protection of hydrogen energy storage stations. Unlike conventional CFD-based leakage consequence analyses, this study couples hydrogen dispersion simulation with Q9-based deflagration risk assessment and a hierarchical safety strategy involving natural, mechanical ventilation, and emergency shutdown. Full article

The disengagement of contact wires in rigid overhead contact systems (ROCS) is a critical hazard to urban rail transit, potentially causing pantograph–catenary collisions, wire breakage, and large-scale catenary failures. To investigate its mechanical mechanism and detection approach, a three-dimensional solid and flexible multibody coupled model of a rigid pantograph–catenary system was established using finite element analysis and multibody dynamics simulation. The effects of train speed, contact force fluctuation, contact wire stress concentration, and disengagement degree on pantograph–catenary dynamic behavior were analyzed. The results show that the standard deviation of contact force at overlapping spans is significantly higher than that in other sections, indicating that overlapping spans are high-risk regions for disengagement. The peak stress of the contact wire is mainly concentrated at registration points and increases with train speed, which may promote abnormal wear and local deformation of the conductor rail jaw. Field-observed environmental degradation factors, such as tunnel water leakage, corrosion, and conductive grease deterioration, may further weaken the clamping performance of the conductor rail. Hazard simulations show that disengagement at overlapping spans deteriorates current collection quality and aggravates carbon strip wear, especially at the beginning of the subsequent overlapping span. Based on onboard inspection equipment, an engineering detection route integrating area-scan imaging, three-dimensional profile scanning, and line-scan imaging is proposed to support the inspection of severe and minor disengagement defects. Full article

The complex, heterogeneous nature of many subsurface environments makes accurate Rate of Penetration (ROP) prediction both critical and challenging for achieving drilling efficiency, cost control, and operational safety. Although artificial intelligence has demonstrated strong potential in extracting nonlinear patterns from drilling and well-log data, its application to heterogeneous formations remains limited by: (i) overreliance on operational parameters that lack formation-physics context, (ii) rigid train–test splits that ignore geological variability, and (iii) heuristic hyperparameter selection practices that are not reproducible. This study presents a geomechanically augmented deep learning framework applied to two vertical wells in a Middle East carbonate-clastic field (Well A: 9375 records, 1000–3370 m; Well B: 4443 records, 1945–3131 m). Five contributions are introduced: (1) a physics-informed input space integrating lithology-specific geomechanical properties (UCS, CCS, Young’s modulus, shear modulus, friction angle), validated against core measurements (R² = 0.79–0.95); (2) a heterogeneity-adaptive train–test partitioning strategy demonstrating that formation complexity, rather than a fixed universal ratio, governs the optimal split; (3) a residual Fully Connected Neural Network (FCNN) with Swish activation and systematic hyperparameter sensitivity analysis; (4) a rigorous preprocessing pipeline comprising 99th-percentile Winsorization, interaction-term feature engineering (WOB × CCS, RPM × UCS), Lasso selection, Z-score normalization, and Gaussian noise augmentation, with all transforms fitted exclusively on training data to prevent leakage; and (5) a hybrid LSTM-FCNN that processes depth-ordered sequences via Savitzky–Golay denoising and a ten-step sliding window. The standalone FCNN achieved R² = 0.8641 (Well A) and R² = 0.9062 (Well B). The LSTM-FCNN improved intra-well accuracy to R² = 0.9877 and R² = 0.9551 and resolved a severe cross-well transfer asymmetry (B → A: R² = 0.0388 for FCNN versus R² = 0.8217 for LSTM-FCNN; A → B: R² = 0.8963), confirming that depth-sequential modeling captures transferable formation patterns across contrasting lithological profiles. Full article

With the advancement of the new-type power system construction, the accurate and efficient calculation of node-level carbon emission factors (CEFs) has become a key basis for indirect carbon emission accounting in power systems. Existing centralized methods face two major challenges in large-scale power grids: a heavy computational burden caused by the expanding scale of carbon emission flow equations, and potential privacy leakage caused by the centralized aggregation of regional operational data. To address these issues, this paper proposes a partitioned iterative CEF calculation framework based on the centralized data distribution pattern (CDDP) and the biconjugate gradient stabilized (BiCGSTAB) algorithm. The power grid is naturally divided into multiple subregions according to the power supply jurisdiction of each node. Each subregion independently solves its local CEF model, while a centralized broker coordinates boundary information exchange among regions. During this process, each region only discloses the required boundary-node CEFs, which is consistent with the mechanism of unified dispatch and hierarchical management. Tests on the 2000-node, 10,000-node, and 25,000-node systems show that the maximum relative errors are 0.00019%, 0.0030%, and 0.0900%, respectively. These results verify the effectiveness and scalability of the proposed framework and provide a feasible engineering solution for efficient, privacy-preserving node-level CEF calculation in large-scale power grids. Full article

In geological carbon storage, cyclic casing loading can induce micro-annuli in the B-annulus cement sheath, risking CO₂ leakage. Compared with conventional cement, the Sn58Bi low-melting-point alloy boasts excellent flowability and favorable elastoplastic behavior, emerging as a promising sealing alternative. This study focuses on enhancing wellbore integrity by using Sn58Bi alloy to seal the B-annulus cement sheath. An experimental system was established to simulate micro-annulus evolution, with gas migration tests conducted under cyclic internal pressure to systematically evaluate the effects of temperature and cyclic loading on the alloy’s sealing performance. Additionally, a three-layer casing–annulus–formation coupling model was constructed to investigate the radial displacement of the Sn58Bi alloy sheath and cement sheath at 30 °C and 20 MPa casing pressure, clarifying their distinct mechanical responses. Results show that the alloy’s sealing performance improves with temperature (30–90 °C), while elevated cyclic internal pressure accelerates gas breakthrough and reduces sustainable cycles. Under identical conditions (30 °C, 20 MPa), Sn58Bi alloy exhibits significantly superior CO₂ sealing capacity to conventional cement. This study confirms the alloy’s potential for enhancing wellbore integrity and provides theoretical support for its application in B-annulus plugging during subsurface carbon storage. Full article

With the continuous development of China’s economy and technology, the number of urban transportation shield tunnels has been increasing. As tunnel depth and diameter grow, the geological conditions become increasingly complex, making leakage at segment joints of shield tunnels a more prominent issue, significantly affecting the sustainable development of urban transportation. To address the issue of water leakage, microbially induced calcium carbonate precipitation (MICP) technology offers a green and environmentally friendly solution. However, relying solely on MICP technology is insufficient to enhance the waterproofing performance of large segment joints of shield tunnel. To address this, this study proposes combining diatomite as both a carrier and filler material with MICP technology, using a diatomite–MICP composite grout to improve the waterproofing performance of tunnel segment joints. First, through laboratory macro-scale tests and micro-morphology analysis, the influence of diatomite dosage on the sealing performance of diatomite–MICP composite grout was systematically studied, and the optimal diatomite dosage was determined. Based on this, a self-developed segment joint waterproofing testing platform was adopted to conduct hydraulic tests on double-seal gasket joints, evaluating the enhancement effect of the composite grout on the overall waterproofing performance of tunnel segment joints. The results indicated that the dosage of diatomite significantly affects the sealing performance of the composite grout, with an optimal dosage of 20% by weight of the bacterial solution. At this dosage, the composite grout achieved the highest density, resulting in maximum unconfined compressive strength and shear strength, as well as the lowest permeability coefficient. The joint water pressure test confirmed that after grouting with a diatomite–MICP composite grout at the optimal dosage of 20%, the breakdown water pressures of the inner and outer seal gaskets at the segment joints reached 2011 kPa and 2019 kPa, representing increases of 15.91% and 16.64% compared to the control group without grouting. This study demonstrates the effectiveness and application potential of the green biomineralization technologies in waterproofing of shield tunnel joints. Full article

This study provides a structured operational-to-financial roadmap for soil organic carbon (SOC) sequestration in mineral soils as a specific carbon-farming pathway. It integrates SOC management; Monitoring, Reporting, and Verification (MRV) execution; financial recognition; and farmer adoption barriers. A comparison of carbon farming pathways is first presented to investigate their strengths and limitations, highlighting the specific importance of SOC management in mineral soils. For high-integrity carbon accounting, SOC gains should be assessed not only for quantity, but also for additionality, permanence, uncertainty, leakage, lifecycle emissions, and transparent verification. Credible MRV frameworks operationalize this logic: monitoring quantifies SOC changes, reporting ensures transparency, and verification provides independent assurance for carbon credit issuance and financial recognition. However, MRV execution faces several challenges, including high spatial variability of SOC, slow accumulation rates, methodological uncertainty, and high costs that limit scalability and reduce trust among stakeholders. Financial incentives are available from both public and private sources, supporting long-term soil carbon stabilization, verified carbon removals, and corporate insetting projects. Yet, adoption remains constrained by uncertain payments, poor transparency, contract and permanence concerns, as well as learning and operational costs for farmers. Addressing these bottlenecks is essential for transforming mineral-soil SOC sequestration into a scalable, high-integrity climate and economic opportunity. Full article

Rising atmospheric CO₂ levels pose a critical challenge to achieving global sustainability targets. Geological carbon sequestration (GCS) offers a long-term solution for reducing greenhouse gas emissions, but its large-scale deployment faces limitations in cost, uncertainty, and operational risk. Recent advances in machine learning (ML) present transformative opportunities to enhance every stage of the carbon capture and storage (CCS) lifecycle, from pre-injection evaluation to post-injection monitoring. This review systematically examines ML integration in CCS applications, emphasizing roles in geological characterization, injection optimization, plume prediction, and leakage detection. It provides a structured overview of ML methodologies including Random Forest, Support Vector Regression, and XGBoost, along with emerging deep learning models used for anomaly detection and uncertainty quantification. Experimental insights, monitoring techniques, and real-time data applications are summarized to illustrate ML’s capability in accelerating simulations, reducing costs, and increasing safety assurance. Furthermore, real-world case studies such as Sleipner (Norway), Illinois Basin–Decatur (USA), Boundary Dam (Canada), Gorgon (Australia), and Quest (Canada) demonstrate how ML has enhanced performance, predictive accuracy, and storage reliability in field-scale CCS projects. The review concludes by identifying existing challenges, data scarcity, interpretability, and regulatory integration, and proposes a unified ML framework for scalable, autonomous, and secure CO₂ storage. Overall, this study provides a comprehensive roadmap for leveraging artificial intelligence to achieve reliable, cost-effective, and sustainable carbon management solutions aligned with global net-zero objectives. Full article

Agricultural organic solid wastes (AOSW) are often accompanied by agricultural production, with large quantities, leading to resource waste and environmental impacts. Composting and anaerobic digestion (AD) are mainstream and recommended methods of AOSW disposal in China. This research conducts a review of the greenhouse gas (GHG) emissions of AOSW composting and AD methods, with consideration of advancements, challenges, limitations, and future directions. Composting’s biogenic GHG contributes 30–740 kg CO₂-eq·t⁻¹ DM, with feedstock and systems as main factors. AD’s biogenic GHG comes from 0–10% methane leakage (196.5–982.4 kg CO₂-eq·t⁻¹ DM) and digestate composting (276.6–1002.3 kg CO₂-eq·t⁻¹ DM). Non-biological GHG for both is 11.3–561.8 kg CO₂-eq·t⁻¹ DM. Process regulation cuts GHG by 13.9–55.8%, exogenous addition by 6.0–97.3%. This paper highlights and discusses AOSW biological treatment’s GHG emission and reduction strategy, supporting low-carbon resource recycling. Full article

Mongolian rangelands face interacting ecological and livelihood pressures, including livestock pressure, vegetation change, soil-carbon dynamics, household income variability, and inefficiencies in livestock by-product recovery. This paper examines whether observed administrative and household data, field-observed pilot-area audit evidence, satellite-derived/backcast vegetation indicators, model-reconstructed ecological trajectories, econometric associations, machine-learning diagnostics, Monte Carlo uncertainty outputs, and scenario-based carbon-finance calculations are consistent with a study-specific ecological–economic feedback framework in Mongolian pastoral rangelands. The analysis combines observed livestock and household data, satellite-derived vegetation indicators, field-anchored soil organic carbon (SOC) information, climate controls, and pilot-area by-product audit evidence in a dual-scale framework comprising nine pasture-user groups in Öndörshireet Soum, Töv Aimag, and a national soum-level panel for 2002–2024. SOC, above-ground biomass (AGB), and below-ground biomass (BGB) trajectories are treated as model-reconstructed series rather than independently observed annual field measurements. Fixed-effects panel models are used to estimate conditional associations, while machine-learning models assess predictive consistency within reconstructed data structures. Under the fitted full specification, the best-performing national-panel model reports an out-of-sample R² of 0.942 for model-reconstructed SOC; this value is interpreted as high internal predictive consistency within the reconstructed SOC panel, not as independent validation of observed annual SOC change. Because the SU/SOC ratio mechanically contains SOC, the full-specification predictive results are subject to leakage risk, and leakage-free validation is needed for a more conservative assessment of predictive performance. Panel estimates suggest that vegetation condition is positively associated with ln(household income), while the by-product waste ratio is negatively associated with ln(income), conditional on fixed effects and model specification. Scenario-based carbon-finance outputs, framed with reference to Verra’s VM0042 Improved Agricultural Land Management methodology, vary materially with compliance, carbon price, weighted average cost of capital, and revenue-sharing assumptions; these outputs are illustrative sensitivity calculations and do not demonstrate VM0042 compliance, project eligibility, project-registration readiness, verified emission reductions, or credit-issuance readiness. The findings are associational, reconstruction-dependent, and scenario-based. They support an analytical framework rather than establish a closed causal loop. Full article

In protracted humanitarian crises, solid waste management (SWM) becomes a major challenge due to limited resources, inadequate infrastructure, and competing response priorities. Waste generated in humanitarian settings typically consist of heterogeneous streams, where plastics, biodegradable fractions, and packaging materials represent the dominant components. Proper management of this waste is essential to reduce health risks and environmental impacts on local communities. Within this framework, sustainable bio-based alternatives and compostable solutions represent promising alternatives. The EU-funded Bio4HUMAN project promotes the integration of innovative bio-based solutions aligned with humanitarian and sustainability goals. An exploratory assessment focused on analyzing waste production, material composition, and handling practices in two case study locations in Sub-Saharan Africa (Democratic Republic of Congo (DRC) and South Sudan (SS)). The results indicate that humanitarian waste cannot be clearly distinguished from household or commercial waste, as streams are typically mixed. Waste composition is dominated by organic matter (43–65%), followed by plastics (15–33%), while other fractions such as paper, glass, metals, and textiles are less significant. Further insights into challenges and opportunities were obtained through a combination of quantitative surveys (n = 29), qualitative interviews with key informants (KIIs) (44) and group discussions sessions (FDG) (9), direct observations, and literature review. Subsequently, a scoping approach was applied to map and classify suitable sustainable solutions into two main categories: bio-based products (BBPs) and organic waste valorization technologies. These were assessed through life cycle assessment (LCA) in accordance with ISO 14040 and 14044, applying SimaPro v.10.2.0.3 software and the Ecoinvent 3.10 database, and compared against fossil-based alternatives. This study compares two case scenarios: a HDPE oil bottle versus PLA alternative (functional unit 6 L), and PE water container versus PLA alternative (functional unit 10 L). For the oil bottle, PLA shows a lower carbon footprint (1.33 kg CO₂-eq) than HDPE (2.37 kg CO₂-eq). In contrast, for the water container, PLA performs worse (2.22 kg CO₂-eq) compared to PE (1.59 kg CO₂-eq), due to higher material demand. The results suggest that benefits are context-dependent and most evident for lightweight products with high leakage risks, particularly when composting infrastructure is accessible. This study advances previous work on humanitarian SWM by integrating field-based waste flow characterization with context-specific screening and life cycle assessment of bio-based alternatives, providing quantitative evidence on the conditions under which these solutions can effectively reduce environmental burdens in protracted crisis settings. Full article

To address the application bottlenecks of organic phase change materials characterized by low thermal conductivity and susceptibility to liquid leakage, this study utilized natural poplar wood as a raw material to construct a three-dimensional carbon/silver heterogeneous porous skeleton via delignification, gradient carbonization, and in situ electroless silver plating. Polyethylene glycol (PEG) was then vacuum-encapsulated within this structure to prepare form-stable composite phase change materials (CPCMs). The regulatory effects of carbonization temperature and metal interface modification on the microscopic morphology and thermophysical properties of the materials were systematically investigated. The results indicate that the skeleton carbonized at 800 °C achieves an optimal balance between pore distribution and skeleton rigidity, ensuring the uniform conformal growth of silver nanoparticles and endowing the material with excellent anti-leakage performance. The thermal conductivity of the optimal sample reaches as high as 0.683 W/(m·K), with the melting latent heat maintained at 133.9 J/g, while also demonstrating an agile and stable photothermal conversion response. Non-equilibrium molecular dynamics (NEMD) simulations further confirm that the silver nanoparticle modification layer smooths the phonon vibration frequency mismatch between the carbon substrate and organic segments, significantly reducing the interfacial thermal resistance. This research provides an important reference for the structural design and microscopic heat transfer mechanism analysis of high-performance phase change energy storage materials. Full article