Dust generated during ore blending poses a significant threat to workshop air quality, making reliable negative-pressure ventilation essential for industrial dust control. Conventional uncovered plate channels (UPCs) have open upper surfaces and worn belt-edge gaps, causing air leakage, pressure losses, and insufficient far-end suction. This study proposes a perforated plate channel (PPC) as a retrofit design for ore-tank dust removal systems and evaluates whether leakage reduction improves negative-pressure preservation, dust-laden airflow transport, and fault tolerance under degraded sealing conditions. Simulation results show that the PPC maintains a low-pressure region approximately twice as long as that of the UPC; under comparable conditions, the PPC achieves a pressure drop of 100–150 Pa lower and increases the air velocity at far working positions by 3–5 m/s. The PPC improves the effective ventilation rate by 35.07%–134.20%, with greater gains observed at increasing distances from the fan. In the fault experiments, the PPC raises the effective ventilation rate by 49.12% under belt warping and by 72.88% under belt deflection. These findings demonstrate that PPC retrofitting can significantly enhance the ventilation reliability of industrial dust-control systems under both normal operation and leakage-fault conditions.

Hydrothermal bio-oil production from biomass is a promising solution to alleviate the energy crisis and contribute to carbon neutrality effectively. A deeper elucidation of the nitrogen-oxygen conversion mechanisms within the oil phase was essential for enhancing the energy density and combustion efficiency of bio-oil, as well as for mitigating nitrogen oxide emissions. In this work, ReaxFF molecular dynamics simulations were employed to investigate the complex transformation pathways of nitrogen and oxygen during the production of bio-oil by hydrothermal liquefaction of biomass model compounds. The study systematically explored how different reaction conditions, particularly the choice of reaction solvent, influence the transformation of these elements and the overall product regulation mechanisms. The results showed that the oil phase yield reached a peak of 64 wt% at a 2:1 ratio and 574 K in the acetone system. Elevated temperatures induced an overall increase in nitrogen and oxygen contents within the oil phase. Detailed kinetic and thermodynamic analyses highlighted that the elevated activation energy (Ea = 53.09 kJ/mol) and pronounced selectivity (S infinity ij = 1.26) associated with water effectively hindered the incorporation of oxygen-containing species into the oil phase. Acetone (Ea = 48.11 kJ/mol, S infinity ij =1.18) demonstrated superior efficacy in nitrogen removal by scavenging-NH2 and markedly inhibiting the deamination pathway, which diminished amine formation. In addition, acetone reacted with reactive C=O intermediates from sugar degradation to form acetals and ketals, thereby obstructing the initial stages of the Maillard reaction and suppressing N-heterocycles. These findings provide valuable theoretical guidance for optimizing solvent selection and process parameters to improve bio-oil yield and quality.

The increasing dependency on critical infrastructure and the vulnerability to cyber-attacks, particularly Distributed Denial of Service attacks, pose significant challenges and threats in this cold warfare era. This paper explores an epidemic model based distributed denial of service attacks system to analyze the impact of seclusion strategies on protecting critical infrastructure against cyber-attacks by leveraging machine learning knowledge with non-linear exogenous networks supported with Levenberg-Marquardt backpropagation. The proposed information security model presents the critical infrastructure nodes into susceptible, infected, quarantined and recovered differential compartments for the targeted population to portray the attack's dynamics and quarantine measures effectively. To analyze the rates for infection, efficiency in the quarantine and the recovery state, the synthetic data is acquired to carry out processes on various scenarios with Adams numerical solver and the said information is fed to intelligent supervised nonlinear autoregressive exogenous neural networks to decipher the attack patterns. The efficacy of the proposed stochastic computing paradigm is established on mean squared error-based convergence trends, error in time series illustrations, error-histogram, and error distribution in histograms, statistics on correlation and autocorrelation metrics based on an exhaustive simulation study for an information security model. The validation of the performance of the design nonlinear networks is further endorsed from counterpart's backpropagation schemes of Bayesian regularization and scaled conjugate gradient, based on the results of statistics in terms of mean, standard deviation, worst, and best of the convergence arcs, error distribution on heat map, inference on median with box plots, plot-matrix analysis, violin plots dynamics and computational time analysis, on exhaustive autonomous executions for solving cyber-attack model in information security.

The growing environmental concerns and dwindling fossil fuel reserves necessitate the development of sustainable alternative fuels. This study utilises spent tire-tube rubber residues (STRR) as a feedstock to produce pyrolytic oil and systematically evaluates its diesel blending ratios of 10%, 20%, 30%, and 40% (B10-B40) as a fuel substitute in compression ignition (CI) engines. Calorific values in the range of 34.5–41.7 MJ/kg and kinematic viscosities between 2.7 and 2.9 cSt were observed for the blends. The tests conducted on the engine indicated that B10 had the optimal performance, with a 6.4% increase in brake thermal efficiency and reductions of 9.2% and 7.3% in CO2 and NOx emissions, respectively. Thermal degradation studies indicated single-stage decomposition with Ea of 156.7–163.1 kJ/mol and ln(A) of 20.1–33.2 min−1, respectively. Thermodynamic evaluation revealed that the reactions are non-spontaneous, with average changes in Gibbs free energy (ΔG) and enthalpy (ΔH) of 149.7 and 153.9 kJ/mol, respectively. A gate-to-gate life cycle assessment (LCA) demonstrated significant environmental advantages, including an average reduction of −587.4 kg oil eq per tonne of STRR processed in fossil resource consumption (M1: −584.8; M2: −589.9 kg oil eq), with reductions in global warming (M1: −240.3, M2: −256.7 kg CO2 eq) and ecotoxicity impacts (M1: −783.2 to M2: −795.3 kg 1,4-DCB). Scenario M2 represented partial reuse of the pyrolysis gas, resulting in better mitigation of human health impacts (M2: −8.5 Pt vs. −7.6 Pt in M1). The results demonstrate the potential of STRR-derived fuels via pyrolysis as a promising waste-valorisation pathway with reduced process-level environmental impacts, supporting its potential contribution to resource recovery and circular economy strategies.

Thermal runaway (TR) is traditionally attributed to electrolyte flammability, which has fostered the misconception that mitigating electrolyte combustion is sufficient to prevent TR. Using a three-strategy accelerating-rate calorimetry approach, we decoupled the energetic contributions of electrolyte decomposition from those of solid-phase redox reactions. Strikingly, extracting ∼1.7 L of volatiles at 140 °C does not substantially reduce TR intensity, the rate of temperature rise, or the maximum battery temperature, indicating that TR severity is primarily governed by solid-phase redox reactions rather than being exclusively driven by electrolyte combustion. To elucidate this, our approach captures the dynamic changes in the electrolyte by decoupling vaporization from chemical gas production. The Antoine-Arrhenius dual-stage pressure model identifies approximately 130 °C as an operational transition temperature at which the dominant pressure-generation mechanism gradually shifts from solvent vaporization to chemically driven gas generation. The apparent activation energy of electrolyte decomposition (Ea ≈ 117.4 kJ/mol) confirms that these electrolyte-derived processes merely act as initiators. Beyond 170 °C, the system transitions to catastrophic TR driven by irreversible solid-phase electrode reactions. These findings suggest that enhancing the thermal stability of electrode lattice structures is of paramount importance for fundamentally mitigating TR hazards.

In this work, we explored the combustion characteristics and reaction kinetics of the three TR gas mixtures under varying equivalence ratio (\(\:{\upvarphi\:}\)) and initial temperature ( T 0 ) based on thermal runaway (TR) experiments and GRI-Mech 3.0 mechanism. Results indict that laminar burning velocity (LBV) and the peak flame temperature ( T max ) changed non-monotonically with \(\:{\upvarphi\:}\), reaching a single optimum, and both increased as the T 0 rose. During the TR gas combustion, the flame temperature continuously increased after the combustible mixture passed through the flame surface. The peak concentrations of H, O, and OH radicals increased sharply near the Tmax. Rate of production and sensitivity analyses indicated that equivalence ratio (\(\:{\upvarphi\:}\)) and T 0 jointly govern radical chemistry and flame propagation, with R84, R38, and R3 identified as the key promoting reactions. Near-stoichiometric conditions and higher T 0 enhanced radical activity and flame propagation, although the fundamental competition between promoting and inhibiting reactions remained unchanged.

In this study, luffa vine (LV) was chemically modified to enhance its suitability as a sustainable reinforcement material for concrete. The treatments involved sequential immersion in alkaline, acidic-oxidizing, and silane solutions, which improved fiber crystallinity and interfacial bonding with the cement matrix, thereby enhancing the ductility of concrete composites. Thermogravimetric analysis demonstrated an increase in initial pyrolysis temperature from 271.3–291.2°C to 304.8–327.1°C, along with an evident rise in activation energy from 147.98 to 201.40 kJ/mol, confirming improved thermal stability. Master plots analysis identified the F n model as the best fit for raw LV at two major stages, while modified LV followed a combination of F n and D n models. The Sestak-Berggren model accurately reconstructed the reaction mechanism functions. Thermodynamic analysis revealed an endothermic and non-spontaneous pyrolysis process for modified LV with a 66.44 kJ/mol increase in average enthalpy change (Δ H ). The enhanced thermal properties help prevent concrete spalling under high-temperature exposure (e.g. fire scenarios), positioning modified LV a promising eco-friendly reinforcement for fire-resistant concrete.

A B S T R A C TThermal runaway (TR) is traditionally attributed to electrolyte flammability, fostering the misconception that solid-state electrolytes are inherently safe. Using a three-strategy accelerating rate calorimetry approach, we decouple the energetic contributions of volatile combustion from solid-phase redox reactions. Strikingly, extracting ~1.7 L of volatiles at 140 °C eliminates all organic solvents but does not significantly reduce TR intensity, the temperature rise rate (dT/dt), or the maximum temperature (Tmax). This proves TR is fundamentally governed by solid-phase redox reactions, not electrolyte combustion. To quantify this, we develop an Antoine-Arrhenius dual-stage pressure model distinguishing physical vaporization from chemical gas kinetics. Kinetic analysis reveals that the apparent activation energy (Ea ≈ 117.4 kJ/mol) of early electrolyte decomposition acts merely as an initiator. Crucially, the model identifies 130 °C as an early-warning threshold transitioning to chemical gas evolution. Monitoring deviations in pressure rise rate (dP/dt) creates a diagnostic window before irreversible TR at 170 °C. Consequently, since the primary energy source lies in solid electrodes, switching to solid-state electrolytes alone cannot prevent thermal failure. Future safety strategies must prioritize stabilizing electrode lattice oxygen and blocking solid-phase interface pathways.

Parallel-structured power electronic devices have become fundamental components in renewable energy grids due to their ability to provide enhanced capacity. As the number of parallel modules increases, the sharing of electrical parameters among modules faces severe challenges. The electrical characteristics of a new package mainly depend on the contact states. However, the sealed package design makes it difficult to directly measure the contact state and electrical parameters. Therefore, this study evaluated the contact state of parallel modules under actual operating conditions through electro-thermal-mechanical simulation. Subsequently, based on a synchronous testing platform, the corresponding contact states were reproduced, and the influence mechanism of contact conditions on peak current and switching energy was analysed. Furthermore, a novel predictive framework integrating quantile regression (QR) with bidirectional gated recurrent unit (BiGRU) network architectures was developed, which to overcome limitations in experimental datasets and reveal the dynamic changes of switching energy under more contact states. Finally, from the perspective of packaging structure optimization, the consistency of the contact state among parallel modules was improved to enhance the distribution uniformity of the switching energy for parallel modules.

Buried natural gas pipelines, as the primary mode of gas transportation, have exhibited pronounced risks due to complex soil environments that hinder accurate prediction of gas leakage diffusion patterns, posing severe threats to life and property. This study employed COMSOL Multiphysics to create a three-dimensional numerical model, systematically investigating the combined effects of soil porosity (0.2-0.6), moisture content (0.01-0.6), permeability (0.5-50 Darcy, 1 Darcy = 10(-12) m(2)). Furthermore, pipeline burial depth (0.3-3 m) affects gas leakage dynamics. Key findings revealed that under low moisture conditions (1% water content), elevated soil porosity accelerates vertical gas migration by 35%-48%, enabling expeditious surface accumulation with methane concentrations exceeding 15% LEL (lower explosive limit). Conversely, at typical moisture levels (20% water content), porosity variations showed a negligible impact on gas distribution. Soil moisture emerges as a dominant inhibitory factor: Increasing moisture from 0.05 to 0.6 lessened high-concentration zones (>= 5% methane) by 40%-62% through improved capillary resistance. Permeability escalation amplifies hazardous boundaries exponentially, with 50D permeability scenarios showing a 2.5-fold expansion compared with 0.5D cases. Shallow burial (0.3-1 m) prioritises vertical diffusion, elevating surface concentrations to 8%-12% LEL within 100 min, while deeper burial (>2 m) redirects 70%-85% of gas laterally, creating expansive subsurface plumes (>4 m radius) with delayed surface arrival (>300 min). By integrating multi-physics simulations, this study clarified the mechanistic interactions between soil parameters and gas leakage behaviour, offering scientific insights for optimising leak detection, risk assessment, and emergency management in buried pipelines. These findings rendered vital engineering guidance for ameliorating pipeline loss prevention and mitigating environmental hazards.

The coupled disaster of coal spontaneous combustion (CSC) and gas explosions in the goaf of high-gas mines is a critical focus for disaster prevention. This paper reviewed the current research on these mechanisms and associated risk assessments, aiming to support the development of prevention technologies in China. The review covered three areas: Gas explosion mechanisms, coal spontaneous combustion characteristics and risk assessment, and the coupling laws and risk evaluation of these disasters in goafs. Five key issues for future research are identified: The need for more detailed studies on the explosion mechanisms of multi-component gas mixtures; further exploration of coal spontaneous combustion evolution and risk determination in goafs; systematic improvement of theories on coupled coal combustion and gas explosion disasters; clarification of flame shock wave propagation in gas explosions; and the urgent development of a risk evaluation system for these coupled disasters. Continuous research in this field is of vital importance for enhancing the safety standards of coalmines, promoting the sustainable development of the coal industry, and achieving the goals of carbon peak and carbon neutrality.

The objective of this study is to illustrate the mathematical behavior of hybrid nanofluidic model involving SWCNTs-CuO/ethylene glycol as a base fluid using cascade forward neural network with Bayesian regularization technique (CFNNs-BRT). The mathematical formulation of SWCNTs-CuO/ethylene glycol with stretching and shrinking surfaces is portrayed by partial differential equations (PDEs), which are transformed into a system of nonlinear ordinary differential equations (ODEs) through suitable substitutions while the analysis is conducted for the physical parameter of interest such as unsteadiness, the constant stretching/shrinking, magnetic field, the mass flux constraint, temperature ratio, and the thermal radiation parameters. Synthetic dataset is created by Adams' computing paradigm and the supervised methodology CFNNs-BRT is implemented on the attained data, and the outcomes of CFNNs-BRT reliably align with numerical solutions for each case of SWCNTs-CuO/ethylene glycol that demonstrating with negligible error. The legacy of CFNNs-BRT procedures is efficiently illustrated with mean squared error based learning curves, learn of adaptive controlling parameters, error frequency distribution bins, and regression studies for exhaustive experimentations for hybrid nanofluidic model involving SWCNTs-CuO/ethylene glycol. According to the study, under stretching or shrinking surface circumstances, the hybrid SWCNT-CuO/ethylene glycol nanofluid may generate two potential solutions, but only within certain parameter constraints. It provides improvement in the primary branch and greater thermal conductivity than single-particle nanofluids. While the secondary branch is unstable, stability checks reveal that the primary branch is both stable and physically reachable

To clarify how discharge conditions influence ageing-induced safety deterioration in high-LIBs energy-density cells, this study evaluated commercial NCA-type 18650 lithium-ion batteries cycled at 0.5 C, 1 C, and 2 C. Galvanostatic charge-discharge tests were performed using a Neware battery testing system to monitor capacity retention, state of health, and direct current internal resistance (DCIR). Ageing-related safety changes were further scrutinised by accelerating rate calorimeter through the onset temperature of self-heating (Tonset), maximum temperature rise rate (dT/dt)max, and apparent maximum pressure rise rate (dP/dt)max, while field emission-scanning electron microscopy (FE-SEM) was used to identify post-cycling changes in electrode microstructure.,The results show that degradation severity is governed less by discharge rate itself than by cumulative electrochemical exposure time. Cells cycled at lower discharge rates experienced longer operating durations per cycle, resulting in greater capacity loss, higher DCIR, earlier self-heating onset, and stronger exothermic and pressure responses. FE-SEM observations further confirmed more evident structural deterioration after prolonged cycling exposure. These findings support a time-dominant ageing mechanism and provide a useful basis for improving lifetime prediction and safety management of high-energy-density LIBs.

The expedious increase in municipal solid waste and energy demand has highlighted the urgent need for safe and efficient waste-to-energy technologies, yet spontaneous combustion during solid recovered fuel (SRF) production and storage remains a pronounced safety concern. This study investigated SRF samples collected across different seasons in central Taiwan using thermogravimetric analysis (TGA), auto-ignition temperature testing, and elemental analysis, integrated with advanced thermodynamic modelling. Autumn and winter samples exhibited the lowest ash content (12.46%) and apparent activation energy (Ea, 120.3 kJ/mol), along with the highest moisture content (29.68%), indicating the greatest thermal hazard. The elevated moisture content promotes microbial activity and heat accumulation during drying, delaying heat dissipation and thereby increasing the likelihood of self-heating. In contrast, summer samples, while not having the lowest apparent activation energy, show elevated combustible matter, carbon, and oxygen contents, and a higher calorific value, suggesting that careful management is still necessary to prevent self-heating. These results quantify the seasonal variation in SRF thermal stability, offering new insights into the climatic influence on combustion risk. The findings provide both scientific evidence and practical guidance for the safe storage, transportation, and utilisation of SRF in energy conversion processes, highlighting the study's unique contribution to understanding SRF safety under varying environmental conditions.

Based on the self-built lithium-ion battery thermal runaway fault simulation platform, this paper systematically studied the physical and chemical characteristics of 18650 lithium-ion battery thermal runaway residues with different state of charge (SOC) under thermal abuse conditions, and constructed a ' trace characteristics-state of charge ' correlation model. The experimental results show that with the increase of SOC, the apparent morphology of the battery residue is aggravated, the microstructure is significantly loosened, and the internal porosity and fractal dimension increase exponentially. Through multi-scale characterization techniques such as scanning electron microscopy (SEM), energy dispersive spectroscopy (EDS), X-ray diffraction (XRD) and industrial CT, 12 key characteristic parameters were extracted to reveal the evolution of cathode material structure collapse, aluminum foil melting and gas release during thermal runaway. Principal component analysis (PCA) showed that the cumulative variance contribution rate of the first two principal components was 91.153%, of which PC1 (73.84%) dominated the thermodynamic and structural evolution, and PC2 (17.31%) correlated the chemical mechanism. Based on the comprehensive score model, the low (0-25%), medium (25-75%) and high (75-100%) SOC states are effectively distinguished, which provides a quantitative basis for lithium-ion battery fire traceability.

Battery thermal management system is critically vital for ensuring operational safety and preventing thermal runaway incidents. A primary challenge in system operation management is achieving precise temperature control while abating energy consumption. A battery thermal management system with a strategy rooted in nonlinear model predictive control was put forward. The grey wolf optimization algorithm was innovatively introduced as an optimization solver for temperature-energy-performance collaborative control. First, a control-oriented dynamic model was built via the lumped-parameter method. Prediction accuracy was maintained while computational complexity was curtailed to satisfy real-time control requirements. Second, a nonlinear model predictive controller was designed using battery temperature and coolant temperature as state variables, with compressor speed and pump speed as control variables. A collaborative optimization framework for temperature control, energy minimization, and operational reliability was established through constraint boundaries. Comparative analysis between nonlinear model predictive control and traditional proportional-integral-derivative control was conducted using a amesim-simulink co-simulation platform. Temperature control accuracy, response speed, and energy efficiency were evaluated. Results demonstrated that nonlinear model predictive control exhibited pronounced advantages in temperature control response, energy consumption control, and system operational assurance. Thermal runaway risk is effectively lessened through intelligent coordinated control of compressor and pump operations. System safety and reliability were thereby enhanced. This research provided a novel solution for performance optimization design of battery thermal management systems with notable theoretical and practical values.

The online monitoring of wet scrubber dust removal efficiency typically relies on one-dimensional (1D) pressure signals, which often suffer from low robustness and insufficient feature extraction under complex operating conditions. To address these limitations, this paper proposed a prediction method based on signal-to-image transformation and a multimodal fusion convolutional neural network (CNN). First, 1D pressure signals were converted into 2D images using transformation techniques, such as Symmetrized Dot Pattern (SDP) and Pseudo Image Encoding (PIE), to reveal hidden spatiotemporal features. Subsequently, a sub-area dynamic expert fusion strategy was introduced to integrate complementary features by leveraging the regional probability confidence of different image modalities. Experimental results demonstrated that the proposed fusion model achieved a prediction accuracy of 97.5%, improving by 2.6 percentage points over the optimal single-modal model and >40% over traditional machine learning models (SVM/DT). Furthermore, the model exhibited strong robustness (maintaining 79.7% accuracy even with 25% data loss). This study provides a cost-effective, high-precision intelligent monitoring solution for industrial wet dust scrubbers, facilitating real-time optimization and energy conservation.