Search Results (1,663)

The blast furnace–basic oxygen furnace long process is the dominant steel production route in China. Increasing the scrap ratio is an effective way to reduce cost and carbon emissions, and scrap preheating is a key technology to achieve a high scrap ratio. To improve the low thermal efficiency and poor deep-bed melting performance of converter gas-based scrap preheating, an innovative process using oxygen-enriched combustion in a hot metal ladle is proposed. Numerical simulation is essential for capturing the complex multiphysics phenomena, as real-time monitoring of melting inside the packed scrap bed is extremely difficult. In this study, a novel multiphysics approach based on a User-Defined Function (UDF) is developed to dynamically track the progressive melting of the scrap skeleton, overcoming the key limitation of conventional enthalpy–porosity models that cannot capture the feedback between phase change and porous medium property evolution. A three-dimensional transient model was established, integrating turbulent combustion, gas–solid convective heat transfer in porous media, and solid–liquid phase change. The effects of impact pit depth, scrap porosity, and converter gas flow rate on temperature distribution, melting behavior, and thermal efficiency were systematically investigated. Results showed that porosity had the strongest influence; thermal efficiency increased from 33.92% to 65.59% as porosity rose from 0.6 to 0.8, due to a transition from conduction-dominated to coupled convection–conduction heat transfer. Converter gas flow rate exhibited a non-monotonic effect, peaking at 3688.14 m³·h⁻¹, highlighting a trade-off between energy input and gas residence time, while impact pit depth showed a limited effect with diminishing returns. A 600 s full-process simulation revealed stage-dependent melting, and the initial phase was crucial for process optimization. The optimal condition, with a pit depth of 64 cm, porosity of 0.8, and converter gas flow rate of 3688.14 m³·h⁻¹, achieved a 1.23% melting fraction and 65.59% thermal efficiency within 120 s. These findings clarify the combined roles of geometric confinement, permeability, and energy-residence time interactions, providing guidance for industrial scrap preheating design. Full article

Buildings account for 40% of global energy consumption and one-third of greenhouse gas emissions. Renewable energy systems (RESs), such as solar photovoltaic (PV) and geothermal heat pumps, are critical technological solutions for decarbonization. Despite the growing literature, existing reviews lack a comprehensive synthesis integrating machine learning (ML), Internet of Things (IoT), and Building Information Modeling (BIM). Following the PRISMA protocol, this paper presents a systematic review of 41 studies published between 2012 and 2025. The review evaluates four primary domains: RES performance, building energy prediction, HVAC optimization, and occupancy-aware management. Quantitative findings reveal that solar PV-integrated buildings achieve electricity cost reductions of 35–64%, while ML-enhanced energy prediction models attain accuracies up to R² = 0.989. Critical research gaps are identified, including the scarcity of real-time sensor integration and geographically inclusive multi-climate datasets. Ultimately, this review contributes a structured synthesis of effective technologies, a comparative analysis of methodological approaches (ML, simulation, hybrid), and actionable future directions. It provides practical guidance for researchers and policymakers toward achieving net-zero energy buildings. This study serves as a definitive reference for the development of sustainable, low-energy built environments. Full article

This paper addresses the bidirectional multi-chamber lock scheduling problem by formulating a multi-objective mixed-integer linear programming (MILP) model that simultaneously minimizes average ship waiting time and maximizes chamber utilization. A tailored adaptive large neighborhood search (ALNS) algorithm is developed specifically based on the principle of the destruction and reconstruction of solutions. The algorithm efficacy is validated using the real-word data from Huai’an Lock of the Subei canal. The scheduling rules and parameters are defined from practical operation records. Simulation results demonstrate that the ALNS-based optimization significantly improves lock performance with average chamber utilization increasing by 12.98% and waiting time decreasing by 44.40%. Sensitivity analyses on objective weights further confirm the robustness of the proposed method. Benchmark comparisons with a greedy heuristic, genetic algorithm (GA), and particle swarm optimization (PSO) highlight the effectiveness and computational efficiency of ALNS. This study further explores a threshold-based directional control strategy, showing that relaxing strict alternating-direction rules under asymmetric traffic demand can improve efficiency. The findings provide practical insights for lock scheduling, offering decision support for lock authorities in designing adaptive scheduling and directional control policies. Full article

Blast furnace (BF) ironmaking is a complex multiphase process involving gas–solid flow, heat transfer, chemical reactions, burden movement, and phase transformation under high-temperature conditions. Since many internal states of the blast furnace cannot be directly observed during operation, numerical simulation and mathematical modeling have become important tools for understanding furnace behavior and optimizing operational parameters. This paper reviews recent advances in blast furnace numerical simulation and internal state reconstruction methods. Existing approaches, including packed-bed flow models, cohesive zone reconstruction methods, burden distribution models, and temperature field prediction methods, are summarized and discussed. In addition, the evolution of blast furnace mathematical models from early one-dimensional steady-state formulations to modern three-dimensional multifluid and hybrid simulation approaches is reviewed. Recent developments in computational fluid dynamics (CFD), the discrete element method (DEM), digital twin, and data-driven modeling are also discussed. Compared with traditional simplified models, modern multidimensional and hybrid approaches show improved capability in describing asymmetric furnace inner states, multiphase transport behavior, and operational parameter effects under industrial conditions. However, challenges still remain in achieving computational efficiency, parameter calibration, multiphase coupling, and real-time industrial application. Future studies are expected to focus on the integration of mechanism-based simulation and intelligent data-driven methods to improve prediction accuracy, operational adaptability, and intelligent control capability in blast furnace ironmaking. Full article

Tight oil and gas reservoirs have become an important alternative to conventional hydrocarbon resources worldwide. They are characterized by dense formations, strong heterogeneity, and the low natural productivity of individual wells, making well pattern deployment and injection–production parameter optimization highly challenging. In real development, tight oil and gas fields usually involve hundreds or even thousands of wells. If each well is analyzed and optimized individually, a large amount of computation is required. Meanwhile, uncertainty in geological models further increases the complexity of development scheme design. Traditional manual adjustment methods based on engineering experience are inefficient and make it difficult to obtain an optimal well pattern suitable for the efficient development of tight oil and gas reservoirs under complex constraints, thus showing obvious limitations. To address these problems, this study first analyzes the strengths, weaknesses, and applicability of existing well placement optimization methods. Based on this analysis, we propose an optimization design method that integrates numerical simulation software for tight oil and gas reservoirs with modern intelligent optimization algorithms, enabling rapid and effective integrated optimization of horizontal well placement and fracturing in tight reservoirs. After being applied to Block X of a tight oil field, this optimization method achieved favorable field results, with an average cumulative oil and gas equivalent production of 31,400 metric tons per well, providing a new approach for the effective development of similar tight oil and gas reservoirs. Full article

Oil and gas pipelines are critical infrastructures that require continuous and reliable monitoring to detect leaks, pressure anomalies, corrosion, and unauthorized activities. Wireless sensor networks (WSNs) have emerged as an effective solution for large-scale pipeline monitoring due to their low deployment cost and real-time sensing capabilities. However, the resource-constrained nature of sensor nodes and the open wireless communication environment expose pipeline monitoring systems to various routing attacks, for example, blackhole, sinkhole, selective forwarding, and false data injection attacks, while simultaneously demanding strict energy efficiency to prolong network lifetime. In this paper, we propose SEER-PM (Secure and Energy-Efficient Routing for Pipeline Monitoring): a novel protocol that integrates an Artificial neural network (ANN)-based trust mechanism with energy-aware routing metrics. SEER-PM dynamically evaluates node trustworthiness based on packet forwarding behavior, residual energy, and signal consistency. By training the ANN on historical behavioral data, the system accurately detects malicious nodes with high precision. Simulation results demonstrate that SEER-PM outperforms existing secure routing protocols (Sec-AODV and T-LEACH) in terms of packet delivery ratio (PDR) by 14%, detection rate by 9.5%, and network lifetime by 12% under heavy attack scenarios. The proposed protocol enhances the reliability, security, and sustainability of pipeline monitoring WSNs operating in harsh and remote environments. Full article

The larger research problem is to get combustion engines more effective and flexible and reduce or even eliminate greenhouse gas emissions. Here we concentrate more on a smaller-scale and focused research problem about the significance of air feeding in engine operation. Therefore, the need for modeling a charge air system is obvious. The interaction and co-operation between the charge air systems and combustion engines is a central issue in this article. A literature review was carried out on related topics, and it reveals a research gap in this area. A simulation model of a charge air system based on first principles is developed. It is based on physical and systemic modeling, and it is constructed including control loops reducing and controlling the pressures in the charge air chain. The simulation models of this auxiliary system and engine are successfully combined, and functioning together is demonstrated. The composed models represent real research laboratory equipment in the University of Vaasa Energy Laboratory under construction. The research laboratory equipment and the whole research environment are described. Simulation scenarios are presented both with the charge air system alone and with the combined model, including also the engine part. The significance of the developed models is discussed, and the path towards a digital twin experiment environment is outlined. As a conclusion, we can claim that the combined simulation model is successfully constructed and shown to operate in a stable and physically plausible manner. The digital twin concept can be tested completely only when the research laboratory is constructed and ready and the test runs begin to produce measurement data for the digital part. Then also the simulation models can be tuned to a better accuracy level, and the operation as a digital twin will be verified. Full article

During fast-fill, large type IV polymer composite hydrogen storage tanks experience significant temperature gradients associated with both the compression of the gas and a Joule–Thomson effect that can compromise vessel integrity, significantly affecting overall safety. In order to remedy this concern, the current work proposes a novel active mixing approach in which the tank rotates, which leads to enhanced internal convective heat transfer and consequently minimizes temperature gradients. Transient CF simulations were performed using the Redlich–Kwong real-gas equation of state, capturing the high-pressure thermodynamic behavior of hydrogen precisely. The study, based on the 1000 s fast-refueling of a tank of 20.56 m³ internal volume, was carried out to assess the tangential speeds of rotation at 10, 30, and 50 rad/s, respectively. Results also show that thermal performance has a strongly nonlinear dependence on rotational speed. At 10 rad/s, a reasonably even temperature profile develops with a much lower energy cost. The most significant suppression of peak temperatures, and therefore the most efficient cooling, is seen at 30 rad/s. Nevertheless, when the rotation speed further elevates to 50 rad/s, abundant viscous dissipation heating results in an unwanted secondary temperature increase while partially counteracting the benefits brought about by improved mixing. On the whole, the results indicate that an ideal operating window more closely correlated with 30 rads/s is seen to provide the most beneficial compromise between temperature uniformity, maximum temperature limitation, and energy consumption for rapid refueling of large composite hydrogen storage systems. Full article

Gas blending with hydrogen represents a core research direction for present and future energy transport systems. The throttling of natural gas and hydrogen mixtures through pressure-regulating valves inevitably induces thermodynamic temperature variations. Theoretical analyses and simulated thermal profiles demonstrate that hydrogen blending effectively counteracts the extreme expansion temperature drop post-throttling. This thermodynamic shift alleviates the localized microclimatic thermal conditions favorable to ice-plugging, validating the feasibility of hydrogen injection as a systematic thermal mitigation strategy for high-pressure pipeline networks. This study utilizes computational fluid dynamics software to model the flow field variations in pure hydrogen and gas–hydrogen mixtures under the influence of pressure-regulating valves. Employing a real gas equation of state across varying operational temperatures and pressure conditions, this research calculates and analyzes the flow field variations driven by the Joule–Thomson effect for pure hydrogen and mixtures with varying hydrogen blending ratios. The objective is to inform temperature regulation strategies for long-distance hydrogen–natural gas pipeline networks and to establish an empirical temperature fitting relationship for pure hydrogen. The numerical evaluation indicates a maximum relative error of 6.02% and a maximum absolute error of 0.06877 K. Furthermore, guided by the localized temperature variation patterns, the temperature rise results from 75 pure hydrogen simulation cases were extracted. A Multilayer Perceptron artificial intelligence algorithm was utilized to perform inverse calculation iterations on the thermal data and regulation results. Through the stochastic selection of initial parameters and repeated training iterations referencing the fitting formula, an optimized regulation sequence was obtained. This process drives the fluid temperature to approach the practical regulation target. Following the network training phase, the maximum absolute error between the calculated temperature regulation result and the target regulation temperature is recorded at 0.0556 K, providing a methodological reference for subsequent high-pressure hydrogen applications. Full article

The reliable inspection of aging oil and gas pipelines is essential for preventing accidents and ensuring operational safety, yet the accuracy of RFID-based detection systems is often limited by noise-sensitive peak detection algorithms, motivating the need for more robust signal processing approaches. In this study, an improved Discrete Wavelet Transform (DWT)-based method is proposed, employing db6/db8 wavelets for signal denoising and reconstruction, followed by peak localization using derivative zero-crossing to enhance detection precision. Experimental validation was conducted through both simulations and physical tests, where the proposed method achieved zero false and missed detections in simulation environments and reduced relative error by 30–50% compared to conventional algorithms in practical scenarios. These results demonstrate that the proposed approach significantly improves detection reliability and accuracy. Overall, the method provides an effective and cost-efficient solution for pipeline surface defect inspection, offering strong potential for application in real-world industrial monitoring systems. Full article

Environmental monitoring in hazardous indoor zones conventionally relies on fixed-sensor networks or manual inspections, both of which suffer from spatial blind spots and increased human exposure risks. This paper addresses the problem of transforming sparse, mobile sensor measurements into spatially resolved risk assessments in GPS-denied environments. We propose a Hazard Index (HI) framework that normalizes environmental parameters against established safety thresholds into a unified, graduated risk metric with $O\left(N\right)$ computational complexity, where N is the number of monitored parameters. The framework is designed for multi-parameter hazard assessment; the present work validates the computational pipeline, spatial mapping methodology, and classification logic through single-parameter CO₂ detection ($N=1$) deployed on a LiDAR-guided robotic platform integrating an MQ-135 gas sensor interfaced via a NodeMCU ESP8266 microcontroller. Experimental validation across a 144 sq ft indoor area achieved a trajectory-following RMSE of 0.54 ft relative to planned waypoints using Hector SLAM without odometry, detected CO₂ concentrations ranging from 0.02% to 0.25%, and identified a hazardous region encompassing eight measurement points ($\mathrm{HI}\ge 1.0$) using a three-tier classification scheme (Safe, Elevated, Hazardous) within 225 s of active mapping. The framework provides a lightweight computational footprint suitable for real-time evaluation on an NVIDIA Jetson Nano. The proposed approach establishes a cost-effective, reproducible methodology for autonomous indoor environmental monitoring, with the modular architecture designed for future expansion to multi-parameter sensing. Full article

Observation of welding features is important for GMAW training and instruction because the welding arc, molten pool, filler wire, and groove can be difficult to distinguish during welding. In this study, a compact, low-cost, and practical visualization system was developed to support gas metal arc welding (GMAW) skill training from both the welder’s and instructor’s perspectives. The system consists of a welder-side unit and an instructor-side unit and uses a commercial camera, optical filters, a wide-angle lens, and a compact computer. Welding images were acquired under actual GMAW conditions, and the effects of optical filter selection, exposure time, tone mapping, and trimming methods were investigated. A 600 nm long-pass filter and an exposure time of 20,000 μs provided a suitable balance between arc-light suppression, brightness stability, and image clarity. Gamma correction improved the visibility of key regions, including the molten pool, arc, torch, groove, and wire. In addition, low-pass-filtered centroid tracking enabled stable trimming of the weld region from wide-angle images. The developed system achieved real-time display and recording of standardized welding images, demonstrating its potential to support GMAW training through improved image visibility, real-time monitoring, and standardized image recording, while also providing visual data for post-weld review and future skill-assessment applications. Full article

Persistent organic contaminants and complex wastewater matrices challenge conventional treatment because parent-compound removal does not necessarily imply mineralization, detoxification, or improved environmental safety. Advanced oxidation processes can address these limitations, but practical effectiveness is often constrained by oxidant activation, gas–liquid mass transfer, reagent distribution, light penetration, catalyst contact, energy demand, and matrix scavenging. This work critically examines hydrodynamic cavitation-assisted advanced oxidation processes for water and wastewater treatment, including systems based on hydrogen peroxide, ozone, Fenton and Fenton-like reactions, persulfate, peroxydisulfate, peroxymonosulfate, UV irradiation, photocatalysis, cold plasma, multi-hybrid configurations, and emerging reduction-oriented approaches. The discussion covers reactor configurations, target contaminants, real matrices, and sustainability-related performance metrics. The central argument is that hydrodynamic cavitation is not automatically sustainable as a stand-alone treatment. It becomes relevant as a sustainable intensification module only when measurable improvements are demonstrated in oxidant activation, mass transfer, treatment depth, biodegradability, toxicity reduction, process integration, or scale-up at acceptable energy and chemical cost. A reporting framework is proposed based on mineralization, COD/TOC reduction, by-products, toxicity, biodegradability, normalized energy consumption, chemical efficiency, real-matrix validation, reproducibility, and cost-relevant indicators. Future progress should move from isolated degradation tests to integrated, controllable, and scalable treatment frameworks. Full article

Deep gas emissions in volcanic and tectonic environments are commonly interpreted as the surface expression of localized deep emitters. This representation is adequate for first-order description, but it is not physically complete. Deep degassing is more appropriately represented as a coupled source–storage–pathway system in which volatile generation, compressible accumulation, phase change, hydraulic communication, and permeability evolution are dynamically linked. Starting from phase-wise mass conservation in deformable porous–fractured media, reduced equations for gas migration, pore-pressure diffusion, and thermo-poro-mechanical coupling are derived, showing how the distinction between gas-mass transport and pressure propagation provides a unified framework for volcanic and tectonic degassing. Deep pressure gradients are shown to arise from the competition between volatile supply and pathway leakance, while episodic discharge can occur when permeability evolves under effective stress, sealing, and failure. A minimal analytical source–storage–pathway model is further derived, yielding explicit criteria for valve onset, source charging and discharge times, and the distinction between pressure-led and mass-led responses. The framework is then applied to the published Campi Flegrei carbon dioxide (${\mathrm{CO}}_2$) diffuse total output record, providing a real-data illustration of slow storage loading and rapid transient discharge. The analysis considers magmatic exsolution, hydrothermal mediation, metamorphic devolatilization, advective–diffusive near-surface filtering, and the inverse problem through which surface fluxes and gas compositions are used to infer deep source properties. The formulation links magmatic degassing, hydrothermal pressurization, tectonic fluid ascent, and fault-valve behavior within a common continuum-physics perspective and identifies the constitutive assumptions that most strongly control interpretation. Full article

This study presents a comparative analysis of energy consumption and gaseous emissions from a compressed natural gas (CNG)-fueled vehicle under real driving emissions (RDE) conditions and values predicted using machine learning (ML) models developed from chassis dynamometer data. The analyzed components included energy consumption (EC) as well as carbon dioxide (CO₂), carbon monoxide (CO), total hydrocarbons (HC), methane (CH₄), and nitrogen oxides (NO_X). The models were trained using a limited set of easily accessible predictors, namely vehicle speed and acceleration. A hybrid modelling approach was proposed, combining laboratory data with validation under real-world conditions. Additionally, road load power (P_rl) was introduced as a novel predictor representing vehicle operating load. The results demonstrate that the models effectively capture emission trends, with the highest agreement obtained for CO, CO₂. The inclusion of P_rl improved prediction accuracy, which increased from approximately 64% to 71% for CO and from 57% to 61% for HC. For CO₂, the model achieved about 80–82% agreement with RDE measurements, with analogous levels obtained for EC. A key advantage of the proposed methodology is its reliance on a limited number of input variables, which enhances practical applicability while maintaining satisfactory accuracy. Furthermore, the use of precise laboratory data improves model robustness, and the approach enables the estimation of methane (CH₄), which is typically not measured by standard portable emissions measurement systems (PEMSs). The results confirm the effectiveness of the hybrid ML framework and highlight the importance of incorporating load-related parameters in real-world emissions and energy consumption modeling. Full article