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Abstract This study aimed to investigate the mechanism of mineral spontaneous combustion in an open pit. On the study of coal and mineral mixture in open pit mines, as well as through the specific surface area and Search Engine Marketing (SEM) experiments, the specific surface area and aperture characteristics of distribution of open pit coal sample and pit mineral mixture samples were analyzed. Thermal analysis experiments were used to divide the oxidation process was divided into three stages, and the thermal behavior characteristics of experimental samples were characterized. On the basis of the stage division, we explored the transfer law of the key active functional groups of the experimental samples. The apparent activation energy calculation of the key active groups, performed by combining the Achar differential method with the Coats–Redfern integral method, microstructural and oxidation kinetic properties were revealed. The resulted showed that the mixed sample had high ash, the fixed carbon content was reduced, the specific surface area was far lower than the raw coal, the large aperture distribution was slightly higher than the medium hole, the micropore was exceptionally low, the gas adsorption capacity was weaker than the raw coal, the pit coal sample had the exceedingly more active functional groups, easy to react with oxygen, more likely to occur naturally, and its harm was relatively large. The mixed sample contained the highest C–O–C functional group absorbance. The functional groups were mainly influenced by the self-OH content, alkyl side chain, and fatty hydrocarbon in the sample. The main functional groups of the four-like mixture had the highest apparent activation energy, and the two reactions were higher in the low-temperature oxidation phase.

With urbanization and increasing consumption, there is a growing need to prioritize sustainable development across various industries. Particularly, sustainable development is hindered by air pollution, which poses a threat to both living organisms and the environment. The emission of combustion gases containing particulate matter (PM 2.5) during human and social activities is a major cause of air pollution. To mitigate health risks, it is crucial to have accurate and reliable methods for forecasting PM 2.5 levels. In this study, we propose a novel approach that combines support vector machine (SVM) and long short-term memory (LSTM) with complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) to forecast PM 2.5 concentrations. The methodology involves extracting Intrinsic mode function (IMF) components through CEEMDAN and subsequently applying different regression models (SVM and LSTM) to forecast each component. The Naive Evolution algorithm is employed to determine the optimal parameters for combining CEEMDAN, SVM, and LSTM. Daily PM 2.5 concentrations in Kaohsiung, Taiwan from 2019 to 2021 were collected to train models and evaluate their performance. The performance of the proposed model is evaluated using metrics such as mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE), and coefficient of determination (R2) for each district. Overall, our proposed model demonstrates superior performance in terms of MAE (1.858), MSE (7.2449), RMSE (2.6682), and (0.9169) values compared to other methods for 1-day ahead PM 2.5 forecasting. Furthermore, our proposed model also achieves the best performance in forecasting PM 2.5 for 3- and 7-day ahead predictions.

Abstract Temperature variation and gas generation at different depths and positions in the coal combustion process were studied to determine the propagation and evolution of high temperature regions in the process of coal spontaneous combustion. This study selected coal samples from Mengcun, Shaanxi Province, People’s Republic of China, and developed a semi-enclosed experimental system (furnace) for simulating coal combustion. The thermal mass loss of coal samples under various heating rates (5, 10, and 15 °C/min) was analyzed through thermogravimetric analysis, and the dynamic characteristics of the coal samples were analyzed; the reliability of the semi-enclosed experimental system was verified through the equal proportional method of fuzzy response. The results reveal that the high-temperature zone is distributed nonlinearly from the middle to the front end of the furnace, and the temperatures of points in this zone decreased gradually as the layer depth increased. The apparent activation energy of the coal samples during combustion first increased and then decreased as the conversion degree increased. Furthermore, the proportion of mass loss and the mass loss rate in the coal samples observed in the thermogravimetric experiment is consistent with that observed in the first and second stages of the experiment conducted using the semi-enclosed system. The research findings can provide a theoretical basis for the prevention and control of high-temperature zones in coal combustion.

With the continual acceleration of urbanization, the amount of urban infrastructure and the quality of public services are increasing in many cities. A pressing concern in this context is the growing problem of incompatible fire protection construction, indicating a need for urban fire stations with well-planned layouts. However, research on optimizing the layout and placement of fire stations by considering the various factors affecting station layouts is lacking. The current study addressed this gap by establishing an optimal fire station layout by using a geographic information system (GIS) and elucidated the trends of GIS application in firefighting and rescue operations. The study’s findings reveal the benefits of avoiding blind spots, enhancing the selection of fire station sites, and optimizing service coverage. Furthermore, this study optimized the layout of CZ city’s downtown fire stations, which could enhance CZ city’s firefighting capabilities.

Two-phase closed thermosyphons (TPCTs) are effective devices for transferring heat and could be used to reduce the internal heat of coal piles, inhibiting coal spontaneous combustion. Nanofluids have recently been investigated as working media for improving the thermal efficiency of TPCTs by exploiting the high thermal conductivity of nanoparticles. In this study, the heat transfer performance of a TPCT containing CuO–H2O nanofluids of various nanoparticle concentrations was investigated experimentally and through simulations for a coal pile at different operating temperatures (373.15, 413.15, 473.15, and 573.15 K). At 373.15 K, the optimal CuO–H2O concentration was 10 mass%, which was associated with a 34.5% improvement in heat transfer. At 413.15, 473.15, and 573.15 K, the optimal CuO–H2O concentration was 15 mass%, with the corresponding heat transfer improvements being 8.1%, 41.9%, and 40.8%, respectively. At higher operating temperatures, the thermal resistance of the TPCT decreased and the heat transfer performance increased. In particular, at 473.15 and 573.15 K, increasing the nanoparticle concentration improved heat exchange in the working medium, increasing the heat transfer efficiency. These results could be used to further optimize TPCTs and by practitioners to reduce the risk of coal fires.

Abstract Nitrocellulose (NC) is widely used in both military and civilian fields. Because of its high chemical sensitivity and low decomposition temperature, NC is prone to spontaneous combustion. Due to the dangerous properties of NC, it is often dissolved in other organic solvents, then stored and transported in the form of a solution. Therefore, this paper took NC solutions (NC-S) with different concentrations as research objects. Under different atmospheric conditions, a series of thermal analysis experiments and different reaction kinetic methods investigated the influence of solution concentration and oxygen concentration on NC-S’s thermal stability. The variation rules of NC-S’s thermodynamic parameters with solution and oxygen concentrations were explored. On this basis, the spontaneous combustion characteristics of NC-S under actual industrial conditions were summarized to put forward the theoretical guidance for the spontaneous combustion treatment together with the safety in production, transportation, and storage.

Numerous incidents have illustrated that lithium-ion batteries are flammable and thermally unstable when used improperly or at high temperature. Decomposition reactions further increase the possibility of fire and explosion. Polymer electrolytes have been partially applied to commercial lithium-ion polymer batteries (LIPBs) and determined to improve their thermal stability at high temperature. To determine the degree of thermal stability conferred by a polymer electrolyte and enable its future application to various battery types under different combination modes, this study conducted adiabatic thermal tests to simulate and analyse the thermal runaway curves of LIPBs under various states of charge (SOC) by conducting thermodynamic evaluations. Combining the simulation kinetic model, the pseudo-first-order model was fitted to the self-heating reaction of the polymer electrolyte. In a typical explosion, the LIPB had a TNT equivalent mass of 0.028 g in a nitrogen atmosphere and 0.023 g under ambient conditions. Moreover, under a limited overpressure (2068.4 Pa; 0.3 psig), the safety distance of the LIPBs was 1.66 m in a nitrogen atmosphere and 5.79 m in an air atmosphere. In addition, the value of pressure increased from 2.90 to 22,476.9 Pa within 0.2 m in the case of an SOC change from 30% to 100 % in a nitrogen atmosphere. In the same distance, the blast pressure of an LIPB in the nitrogen atmosphere was 43% higher than that in ambient air. Combined with the thermal analysis data and kinetic model, the thermal stability results for the various SOCs of the polymer electrolyte were confirmed in calculating the reaction kinetics and key safety parameters in nitrogen and air environments.

The protection system has a vital role in subsystems of the power network including the generation, transmission and distribution system. This system combines several types of protection relays including directional over current relay, differential relay, distance relay, earth faults relay, inverse definite minimum time (IDMT) relay for the protection of line, generators bus bars, transformers, line to ground faults and feeder lines of the power system. In this research paper, the overall performance of the protection system is improved by reducing the overall operational time of directional over current relays (DOCRs) through the optimal coordination between the primary and backup over current protective relays while keeping the time multiplier setting (TMS), pickup tap setting (PTS) and coordination time of interval (CTI) within the allowable limits. A new optimization strategy, namely the FPSOGSA, is developed by integrating the concept of fractional calculus inside the mathematical model of canonical particle swarm optimization and combining with traditional gravitational search algorithm to enhance the optimizer characteristics and tested on coordination problem of DOCRs in standard power systems including the IEEE 3, 8 and 15 bus networks. The results, yielded by proposed algorithm and their comparison with other well-known recently established counterparts, reveal that the synergy of FC, PSO and GSA enhance the performance of the optimizer by combining the individual strengths of these tools and finding the best global solution. The consistency, stability and reliability of FPSOGSA is endorsed by comprehensive statistical analysis based on the empirical cumulative distribution function, histograms, boxplot illustrations, quantile–quantile plots, minimum fitness evolution in each independent simulation.

This aim of this research was to explore the appraisal of the use of smart alert bracelets by older adults diagnosed with dementia. Convenience sampling was adopted to recruit older adults with dementia in Yunlin County, Taiwan. A manual questionnaire survey was conducted, and SPSS 26.0 statistical software was used for analysis. The results of this study showed noticeable positive correlation results in the post-test for the modes “wearing device”, “degree of dementia”, and “field configuration”. Based on the experimental results, the following suggestions are provided: (1) in terms of statistical calculation, the statistical results were affected by changes in some participants; (2) as for the design of equipment, to be more suitable for adult use, the size and color of bracelets need to be optimized; (3) as for the problem of battery charging of the device, because the charging location of the device is not easy to find, it is better to extend device standby time; (4) regarding the selection of equipment, older adults with early-stage dementia could be concerned about the function of the wearable device, so it is recommended to provide a device designed with clear functions, such as a watch, so that older adults are more willing to wear it. Patients diagnosed with moderate and severe dementia should be advised to use concealed non-sensory devices, such as charms and cards, to better facilitate assistance from caregivers in wearing them; and (5) as for the device, in case of a loss event, in addition to mobile phone notifications, other light and sound device notifications can be added, allowing caregivers to pay more attention to information in real time. In summary, the feedback from caregivers and older adults suggests that if the device is to be used without charging, the overall design should be light and small, which is more suitable for service designs.

This study involved the construction and explosion of a large-scale (80-meter-long) underdrain and detailed investigations of the damaging impacts of a gas explosion to provide an experimental foundation for similarity modeling and infrastructural designs. The experiment vividly recreated the scene and explosion damage of the “11.22″ explosion accident in Qingdao, China, thus allowing for evaluations of the movements and destruction of the cover plates. The damage mechanism was determined by analyzing the overpressure curves inside and outside the underground canal. It was determined that the cover plates were first lifted by the precursor wave, which induced a maximum overpressure of 0.06 MPa and resulted in explosion venting. The pressure entered the deflagration stage at the end of the explosion. The combustion wave overpressure reached 3.115 MPa close to the initiation point, and had a significant influence on the projectile energy of the cover plates there. Overall, 64% of the cover plates were only affected by the precursor wave, while 36% of the cover plates were subjected to both the precursor wave and the combustion wave; these cover plates were severely damaged. The results of this study provide fundamental insights relevant to the prevention and control of underdrain gas explosions.

In the current study, a novel intelligent numerical computing paradigm based upon the foundation of the artificial neural networks legacy involving the Bayesian regularization (ANN-BR) approach has been implemented for the investigation of the non-uniform heat preoccupation process with the bio-convective flow dynamics of nanomaterial involving gyro-tactic microorganisms. The designed bio-convective stratified nanofluid flow (BCSNF) model initially represented by a system of PDEs is transformed into nonlinear ODEs by exploring appropriate transformations. The reference dataset for the BCSNF model was generated by the Adams numerical method for six scenarios by variation of the magnetic number, Brownian motion parameter, Prandtl number, bio-convection Lewis number, thermophoretic parameter, and bio-convection Peclet number. The approximate solutions were determined with 5–7 decimal places of accuracy and interpreted for the BCSNF model by the testing, training, and validation processes of the designed ANN-BR scheme. To check the efficiency of the introduced ANN-BR method, absolute error analysis, histogram studies, regression indices, and mean squared error (MSE) based figures of merit were used exhaustively to solve the variants of the BCSNF model involving gyro-tactic microorganisms with viscous dissipation, stratification, and heat immersion to study the influence of prominent parameters on the velocity, temperature, concentration, and motile density profiles.

The present study reveals the potential of coal fly ash (CFA) as the environmental pollutant adsorbent. The conventional approach of alkali fusion followed by hydrothermal treatment of Taiwan based coal fly ash resulted in new and novel zeolite material ZSM-22. The composition, morphology, chemical structure, and the surface properties of as-prepared zeolite were investigated using BET, SEM, FT-IR, XRD, and XRF. The specific surface area (SSA) of the zeolite is enhanced from 1.28 m2/g to 30.21 m2/g with a drop in Si/Al ratio of 3.1 to 2.4 suggesting the improvement of crystallinity. Further, the application of zeolite on the adsorption of the cationic dye Rhodamine 6G (R 6G) from aqueous solution in a batch process was investigated. Besides, the effect of parameters such as the effect of time, temperature, pH, dosage, agitation speed, kinetics, and isotherms on the removal of dyes was studied. The results indicate that adsorption of dye is more significant at pH = 6 indicating the zeolite surface is positively charged. The proficient sorption efficacy of R 6G onto ZSM-22 followed pseudo-second-order kinetic model and fits well with the Langmuir adsorption isotherm with maximum specific removal of 195.3 mg/g. Moreover, the possible adsorption mechanism for the selective separation of dye using CFA is mainly due to the electrostatic interactions and the hydrogen bonding. Finally, the key takeaways of the present research are that zeolite ZSM-22 is a prospective candidate as an adsorbent for the separation of dye molecules.

In recent decades, fractional order calculus has become an important mathematical tool for effectively solving complex problems through better modeling with the introduction of fractional differential/integral operators; fractional order swarming heuristics are also introduced and applied for better performance in different optimization tasks. This study investigates the nonlinear system identification problem of the input nonlinear control autoregressive (IN-CAR) model through the novel implementation of fractional order particle swarm optimization (FO-PSO) heuristics; further, the key term separation technique (KTST) is introduced in the FO-PSO to solve the over-parameterization issue involved in the parameter estimation of the IN-CAR model. The proposed KTST-based FO-PSO, i.e., KTST-FOPSO accurately estimates the parameters of an unknown IN-CAR system with robust performance in cases of different noise scenarios. The performance of the KTST-FOPSO is investigated exhaustively for different fractional orders as well as in comparison with the standard counterpart. The results of statistical indices through Monte Carlo simulations endorse the reliability and stability of the KTST-FOPSO for IN-CAR identification.

The efficient parameter estimation of harmonics is required to effectively design filters to mitigate their adverse effects on the power quality of electrical systems. In this study, a fractional order swarming optimization technique is proposed for the parameter estimation of harmonics normally present in industrial loads. The proposed fractional order particle swarm optimization (FOPSO) effectively estimates the amplitude and phase parameters corresponding to the first, third, fifth, seventh and eleventh harmonics. The performance of the FOPSO was evaluated for ten fractional orders with noiseless and noisy scenarios. The robustness efficiency of the proposed FOPSO was analyzed by considering different levels of additive white Gaussian noise in the harmonic signal. Monte Carlo simulations confirmed the reliability of the FOPSO for a lower fractional order (λ = 0.1) with a faster convergence rate and no divergent run compared to other fractional orders as well as to standard PSO (λ = 1).

The purpose of this study was to explore the damage effects and injury mechanism of free-field explosion shock waves on rabbit lungs. Six free-field explosion experiments, each with 500 g trinitrotoluene (TNT), were conducted as the shock wave overpressure acting on the rabbits was measured. The peak overpressure of the shock wave was 533, 390, 249, 102, and 69 kPa at the respective test points. Damage to the rabbit lungs caused by shock wave overpressure was investigated through observation, anatomical analysis, and hematoxylin-eosin (HE) staining processing. The shock wave overpressure of 69–102 kPa caused mild-to-moderate injury; the shock wave overpressure of 102–249 kPa caused moderate injury; the shock wave overpressure of 249–390 kPa resulted in moderate-to-severe injury; and the shock wave overpressure of 390–533 kPa caused severe injury to the rabbit. Mild, moderate, and severe injuries destroyed some, most, or all alveolar structures, correspondingly, as well as producing partial cell apoptosis. The overpressure damage mechanism primarily involves the collapse and rupture of pulmonary alveolus in the lung tissue. As a novel attempt, the investigation provided here may serve to improve the current shock wave injury mechanism.

The knacks of evolutionary and swarm computing paradigms have been exploited to solve complex engineering and applied science problems, including parameter estimation for nonlinear systems. The population-based computational heuristics applied for parameter identification of nonlinear systems estimate the redundant parameters due to an overparameterization problem. The aim of this study was to exploit the key term separation (KTS) principle-based identification model with adaptive evolutionary computing to overcome the overparameterization issue. The parameter estimation of Hammerstein control autoregressive (HC-AR) systems was conducted through integration of the KTS idea with the global optimization efficacy of genetic algorithms (GAs). The proposed approach effectively estimated the actual parameters of the HC-AR system for noiseless as well as noisy scenarios. The simulation results verified the accuracy, convergence, and robustness of the proposed scheme. While consistent accuracy and reliability of the designed approach was validated through statistical assessments on multiple independent trials.