Search Results (9,824)

Achieving operational excellence in metalworking industries demands tools that accurately model complex production dynamics and guide improvement strategies. This study applies a discrete-event simulation (DES) framework to optimize productivity and reduce steel waste in coupling manufacturing for oil pipeline applications. A six-phase methodology was implemented, covering system characterization, conceptual modeling, statistical data fitting, Python-based simulation, model verification and validation, and experimental scenario analysis. Four improvement scenarios, preventive maintenance, operator training, material quality control, and integrated optimization, were evaluated through ANOVA. Results show that the integrated scenario increased throughput by 14.5%, improved OEE by 8.6%, reduced scrap generation by 35.4%, and shortened lead time by 11.5% compared with the base model. The validated DES model achieved less than 5% deviation from actual plant data, confirming its precision and reliability. The study establishes DES as a robust decision-support tool for industrial optimization and sustainable waste reduction. Future research should integrate real-time data and digital twin architectures to enable adaptive improvement in smart manufacturing. Full article

Accurate prediction and optimized control of shield tunneling attitude are critical for ensuring tunneling quality and construction safety. In karst and other highly heterogeneous strata, complex geological conditions and construction parameters exhibit significant nonlinear coupling, greatly increasing the difficulty of attitude regulation. To address this challenge, this study proposes a machine learning-based approach for the automatic control of shield tunneling attitude. First, a Tree-structured Parzen Estimator-optimized Light Gradient Boosting Machine predictive model is employed to construct a nonlinear mapping model between construction parameters and shield tunneling attitude. Subsequently, the SHapley Additive exPlanations (SHAP) interpretability model is introduced to identify the core tunneling factors influencing attitude stability. On this basis, the developed predictive model is integrated into the multi-objective evolutionary algorithm based on decomposition (MOEA/D) framework as a fitness function to achieve multi-objective optimization of key construction parameters. Using field data from shield tunneling construction in the karst strata of Shenzhen Metro Line 16, the proposed model achieved prediction accuracies of R² = 0.959 for pitch and R² = 0.936 for roll, outperforming XGBoost, Random Forest, Long Short-Term Memory, and Transformer baselines. SHAP analysis identified the partitioned propulsion thrust, partitioned chamber pressure, cutterhead rotational speed, and advance rate as key parameters influencing attitude. Further, MOEA/D optimization generated a Pareto set of construction parameters, from which the optimal solution was selected using the ideal point method, resulting in reductions of 26.45% and 39.47% in pitch and roll deviations, respectively. These findings demonstrate the feasibility and effectiveness of the proposed method in achieving high-precision prediction and intelligent optimization control of shield tunneling attitude under complex geological conditions, providing a reliable technical pathway for metro and tunnel construction projects. Full article

To address insufficient real-time detection accuracy of standard YOLO models under adverse weather, we propose PFOD-Net, a multi-scale detector based on polarimetric features and an improved YOLOv8n. Compared with traditional intensity imaging, polarimetric imaging extracts optical information from target scenes more effectively; when coupled with an appropriate feature selection mechanism, it significantly enhances detection and recognition performance in complex environments. Experimental results on the Polar LITIS dataset demonstrate that PFOD-Net significantly outperforms YOLOv8 in both accuracy and speed: mAP@0.5 (mean Average Precision) increased from 33.5% to 81.7%—an increase of 48.2 percentage points. Notably, the detection performance of PFOD-Net is substantially reinforced in complex conditions like fog and haze. By innovatively integrating polarimetric information, lightweight architectures, and the fusion of max-pooling and average-pooling, this method provides an effective solution for object detection and recognition in adverse weather. Full article

An asymmetric-coupling decentralized control framework is developed using a Lyapunov-like lemma to achieve synchronization and trajectory tracking among multiple robots. Multiple robots are treated as a single integrated system when employing a Lyapunov-based strategy to design the asymmetric coupled control system. The challenge of verifying the negative semi-definiteness of the Lyapunov function’s time derivative, due to the inclusion of asymmetric coupling terms from the controllers, is addressed through grouping and factorization techniques. The benefits of asymmetric coupling control are demonstrated in comparison to two-way coupling control. Two, three, and four robots are studied, respectively. In graph theory, several control-coupling topologies exist for networked robots. A family of coupling topologies for the four-robot system is compared and ranked in terms of the joint convergence speed and servo gains. Numerical simulations and comparisons are conducted to verify the theoretical results. Full article

Coal mine fires represent one of the major threats constraining sustainable and safe production in the coal industry. To investigate the mechanisms of accident causation and coupling evolution, this study proposed a fire risk analysis method integrating the N-K model (a model for quantifying interactions among system components) with complex network theory. Seventy-five coal mine fire accident cases were selected as samples to identify the coupling types and coupling mechanisms among human, management, technology, environment, and equipment risk factors. The N-K model was employed to determine accident coupling types and calculate risk coupling values. Based on association rule mining among risk factors, a coal mine fire risk network model was constructed. By integrating accessibility characteristics derived from complex network analysis with the N-K model, the normalized out-degree of network nodes was adjusted using N-K coupling values to better reflect node influence, thereby identifying key risk factors. The results showed that management factors were the dominant dimension driving risk coupling, and an increase in the number of coupled factors significantly affected the level of coal mine fire risk. The top four key risk factors were inadequate safety supervision by regulatory authorities, insufficient safety training and education, illegal production organization, and incomplete safety technical measures. Finally, targeted prevention and control strategies were proposed. The findings provide critical support for advancing sustainable and safe coal mine production by informing targeted safety interventions and optimizing resource allocation in safety management. Full article

Conventional ammonia production via the Haber–Bosch process is energy-intensive and carbon-heavy. Emerging biomass-based approaches offer a sustainable alternative but often lack rigorous system-level analysis based on actual reaction kinetics. This study presents a novel integrated process coupling biomass pyrolysis/gasification with Chemical Looping Ammonia Generation (CLAG) and waste heat recovery. Unlike previous models relying on simplified assumptions, this simulation incorporates experimental kinetic data for both N-absorption and N-desorption stages to ensure high fidelity. The system’s energy and mass flows were rigorously evaluated using Aspen Plus. Results indicate that the gasification stage is optimal at an O₂/biomass molar ratio of 0.2 and 750 °C. In the CLAG unit, a higher N-absorption temperature (1600 °C) and α-Al₂O₃/C ratio (3:3) significantly enhance ammonia yield. Under these optimal conditions, the system achieves a remarkably low energy consumption of 10.12 GJ/t-NH₃ and specific CO₂ emissions of 3.2 t/t-NH₃—a reduction of over 60% compared to traditional coal-based routes. The integration of waste heat recovery is identified as a critical factor in minimizing net energy input. This work validates the feasibility of the biomass-based CLAG process as a low-carbon, energy-efficient pathway for sustainable ammonia synthesis. Full article

Terrestrial net primary productivity (NPP) integrates vegetation responses to climate, terrain, and human activities, yet their combined effects in mountainous–basin regions remain unclear. Focusing on the Chengdu–Chongqing Economic Circle (CCEC) in southwest China, we build a framework that couples spatial diagnosis, interaction-aware attribution, and scenario-based projection. Using 500 m MODIS NPP (2000–2020) with climatic, topographic, land-use, and socio-economic data, we quantify NPP trends, use optimal-parameter GeoDetector and partial correlations to separate driver contributions and interactions, and train a random forest (RF)–SHAP model driven by CMIP6–SSP climate projections to 2050. The CCEC shows strong greening: 85.17% of the area exhibits increasing NPP and 68.56% shows extremely significant increases, with productivity peaking at mid-elevations (~1950 m) and intermediate slopes. Elevation, NDVI, and temperature dominate, while precipitation, slope, and soil moisture are secondary, and enhancement-type interactions, especially between elevation and precipitation, prevail. Land-use statistics and NPP transfer matrices highlight cropland-to-forest/grassland conversion as the main greening source. CMIP6-based simulations indicate stable or modestly higher NPP through 2050, with western mountain forests remaining key carbon sinks and basin lowlands constrained by warming and land-use pressure. Full article

Spontaneous combustion of coal piles threatens the production and transportation safety of coal mining, which is attracting more and more attention. To understand the underlying physics, conducting pore-scale research on the spontaneous combustion of coal piles is critical. To enable pore-scale research, a pore-scale model of the spontaneous combustion of a coal pile is described, and governing equations are introduced. To understand the competition between airflow, heat–mass transfer, and oxidation reaction, the lattice Boltzmann method (LBM) is utilized, which offers distinct advantages in handling complex pore geometries, multi-physics coupling, and reactive transport at the pore scale. The present model integrates, for the first time in a pore-scale LB framework, airflow driven by thermal buoyancy, convective heat and mass transfer, and Arrhenius-type oxidation kinetics within a realistic coal pile geometry. After the numerical method is validated, the effects of inflowing air velocity, inflowing air temperature, oxygen concentration, and coal particle size are discussed. With an increase in inflowing air velocity, convective heat transfer is enhanced, and the coal pile maximum temperature decreases monotonically. According to the Arrhenius equation, with an increase in the inflowing air temperature and oxygen concentration, the oxidation reaction is accelerated, and the coal pile maximum temperature increases. When the size of the coal particle increases, the oxidation reactive area decreases, and the coal pile maximum temperature decreases, while the steady temperature is not affected. Full article

Motivated by the need to reduce failure risks, enhance real-time situational awareness, and support data-driven decision-making, this article comprehensively reviews the latest progress in pipeline safety and intelligent technology, focusing on analyzing the effectiveness and challenges faced by integrity management technology in practical situations. A structured literature survey was conducted to outline the key role and significant achievements of smart technology in improving the efficiency and reliability of pipeline safety management. Using this methodology, the review synthesizes progress in pipeline integrity management and monitoring technology, including the application of distributed strain measurement technology, wireless sensor networks, and Internet of Things technology, as well as the practical effects of deep learning and machine learning in defect detection and incident recognition. Additionally, special attention is given to analyzing the latest achievements in applications of large model technology, distributed optical fiber sensing technology, and acoustic analysis technology in the field of leakage monitoring. Based on the reviewed research, the article identifies key technical challenges, including targeted monitoring technology solutions and management strategies for the challenges in the field of pipeline safety. The findings conclude that intelligent technologies substantially enhance the development trend of AI applications. Hence, next-generation pipeline safety will rely on tightly coupled AI–IoT ecosystems. It anticipates the future of pipeline safety management by providing theoretical reference and technical support for pipeline safety guarantees and intelligent operation and maintenance. Full article

Terminal flight phases, particularly landing, are among the most critical, due to low altitude, low speed and the possible presence of crosswinds. Tools capable of accurately modeling and simulating these phases are essential for identifying potential issues and assessing airplane safety integrity. This work focuses on the development of a nonlinear flight simulator devised to handle terminal maneuvers, including ground effect and wind. Such a simulator incorporates the six-degree-of-freedom rigid body equations of motion coupled with a landing gear model and with a basic control that emulates the action of the pilot, while the aircraft aerodynamic characteristics are estimated through a dedicated semi-empirical procedure. The proposed simulator is employed to assess the effect of crosswind and approach speed on different performance indicators, considering a general aviation airplane (Ryan Navion). These indicators include ground roll distance, wing-tip clearance and lateral forces exerted on the landing gear. The results demonstrate that landings are achievable even beyond the demonstrated crosswind limits without encountering wing-tip strikes or rollover and that higher approach speeds could be advisable in strong crosswind conditions. Full article

As the composition of energy markets becomes increasingly diverse and distributed in character, it is difficult for traditional vertically integrated energy system (IES) structures and centralized optimization methods to stimulate coupled interactions and interactive synergies among multiple subjects. Consequently, a collaborative low-carbon scheduling strategy utilizing a leader–follower game framework is introduced for the distributed IES. Making the integrated energy system operator (IESO) a leader, distributed integrated energy supply system (DIESS) and smart user terminal (SUT) as followers, the optimal interaction operation strategy of each subject in the game process can be solved. Firstly, the overall energy interaction process of the system and the game objectives of each participant are introduced to construct a distributed collaborative optimization model with one leader and multiple followers. Secondly, the integrated demand response (IDR) and the ladder-type carbon trading scheme are considered, the two-stage operation process of the electrical gas technology (P₂G) equipment is analyzed in detail, and the genetic algorithm nested CPLEX solver is used to solve the model. Finally, the results show that this paper can provide guarantee and theoretical support for the optimal operation of the integrated energy market in terms of trading model and algorithm. Full article

Interest in replacing petroleum-based lubricants with bio-based alternatives is driven by growing demand for lubricants, in contrast to a decreasing supply of products derived from fossil resources, coupled with environmental concerns. Biolubricants offer several advantages over conventional petroleum-based lubricants, such as biodegradability and renewability. Researchers have been seeking solutions for these challenges over the years, employing various approaches, including the use of different raw materials, chemical modifications, and different types of additives. This review evaluates a total of 504 articles published between 2010 and 2025 in the Scopus database, with the help of RStudio, using the bibliometrix package. The objective is to provide an integrated bibliometric and systematic analysis, presenting the research landscape on the tribological properties of biolubricants, which may contribute to the development of novel investigation initiatives in the field. The main thematic trends, researchers, journals, and most active countries and institutions have been evaluated. Additionally, the most cited studies, recent advances and existing gaps are presented and discussed. Full article

The accurate prediction of extreme dynamic amplification factor (DAF) values is significantly important to ensure a long-term safety assessment of bridges under stochastic vehicular loading. However, predicting extreme DAFs is challenging due to traffic randomness, road roughness variability, and nonlinear vehicle–bridge interaction (VBI) effects. This study presents an integrated framework for extreme DAF prediction for simply supported bridges by combining stochastic traffic–bridge interaction simulations with Bayesian updating and a Peaks-Over-Threshold–Generalized Pareto Distribution (POT–GPD) model. A coupled VBI model is developed, incorporating cellular automaton-based traffic flow, multi-axle nonlinear vehicle dynamics, finite-element bridge modeling, and stochastic road roughness profiles. A new DAF definition based on dynamic displacement difference is proposed to better represent dynamic effects. DAF samples obtained from VBI simulations under different road roughness levels are analyzed using the POT method, with GPD parameters estimated through maximum likelihood and Bayesian inference. Extreme DAFs corresponding to different return periods are then determined. The results indicate that extreme DAF values increase with worsening road roughness and longer return periods and that the Bayesian POT–GPD approach effectively captures tail behavior while providing reliable uncertainty quantification for extreme DAF prediction. Full article

Steelmaking involves long, tightly coupled process chains and specialized domain knowledge, making it difficult in practice for a single general-purpose LLM to consistently align engineers’ queries with the correct process stage. This paper presents STAR, an industry-oriented multi-stage process-domain router for steel metallurgy, and provides an integration blueprint that maps routing labels to domain-specific prompting and retrieval scopes in a router-plus-agents architecture. We construct a quality-controlled metallurgical corpus from textbooks, manuals, and papers via OCR and multi-dimensional text-quality scoring. Based on this corpus, we build an LLM-assisted pipeline to synthesize query–domain pairs for eight fine-grained process domains under domain definitions/keywords and format constraints, and index all queries in a shared embedding space with FAISS. We design a three-stage router: (1) a lightweight filter using chit-chat rules and a nearest-neighbor distance threshold to separate steel-related queries from general ones, (2) a kNN label-voting router whose confidence is derived from the Top-k neighbor label concentration, and (3) an LLM-based refinement step for low-confidence cases with safe fallback. Experiments on 3136 steel-domain queries and approximately 2000 general queries show that STAR achieves 0.921 Top-1 accuracy and 0.899 macro-F1 on 8-way fine-grained steel-domain routing, and achieves a steel-query recall of 0.999 for steel-versus-general filtering (queries routed to general_llm in deployment). In this work, we primarily evaluate routing quality and efficiency; end-to-end answer quality evaluation of downstream agents is left for future work. Full article

To systematically investigate the issues of tunnel overheating and excessive humidity, this study integrates theoretical analysis, experimental research, and numerical simulations. It examines the coupled heat and moisture transfer behavior in the surrounding rock of metro tunnels and its impact on the tunnel’s thermal and humid environment. Based on the theory of heat and moisture transport in porous media, a coupled mathematical model is developed using relative humidity and temperature gradients as the driving potentials. Taking into account the climatic zoning of China, Beijing, Shanghai, Guangzhou, and Kunming are selected as representative cities for cold, hot summer/cold winter, hot summer/warm winter, and temperate climate regions, respectively. The interannual variation characteristics of the thermal and humidity conditions inside metro tunnels in these cities are analyzed and compared. The results indicate that across different climatic zones, higher outdoor peak air temperatures lead to higher peak air temperatures inside the tunnels. The thickness of the thermal regulation zone is primarily influenced by the initial rock temperature and the annual average atmospheric temperature. The thickness of the moisture regulation zone is affected by both the annual temperature fluctuation and the annual average relative humidity, increasing with greater annual atmospheric temperature variation. Full article