Temperature Of Spontaneous Combustion Research Articles

Research on spontaneous combustion characteristics and high temperature point prediction method of rectangular coal pile

ABSTRACT In order to prevent and control the spontaneous combustion phenomenon of rectangular coal pile under the coal storage and transportation scenario, the spontaneous combustion process of rectangular coal pile was simulated based on the experimental platform, the spontaneous combustion temperature change characteristics of rectangular coal pile were obtained, a coal spontaneous combustion prediction model was constructed based on the proposed prediction method. The results show that the height of the coal pile of 30 cm is a demarcation line, the temperature change of the upper measurement point is more consistent, but the lower measurement point has a greater variability, and reaches the ignition point in a shorter time, the peak temperature is higher, which further exacerbates the spontaneous combustion of the coal pile; At 3 h the coal pile formed an initial high-temperature region, and at about 14 h the pile was in a state of complete spontaneous combustion; The prediction method using temperature data from surface measurement points as model inputs is more feasible; All three models have good robustness and generalization, and the specific order of merit is GA-BP > GA-RF > GA-SVR. The study has an important guide for the safe, green and economical development of coal transportation and storage.

Research on prediction model of coal spontaneous combustion temperature based on SSA-CNN

To predict the coal spontaneous combustion temperature accurately and efficiently, this study proposes a model based on the Sparrow Search Algorithm (SSA) and Convolutional Neural Network (CNN). Firstly, the study analyzes the main gas reactions during the coal oxidation to pyrolysis process. Six gas indicators, namely O2, CO, C2H4, CO/ΔO2, C2H4/C2H6, and C2H6, are closely related to coal temperature. Subsequently, a prediction indicator system is established. Then, the excellent data mining capabilities of CNN are leveraged through deep learning, along with their unique advantages in local perception and weight sharing, and a CNN prediction model framework is constructed. Moreover, the comparison between the algorithm performances is executed and SSA is selected for optimization. Utilizing its exceptional global search capability and adaptability, SSA optimizes the seven hyper-parameters of the model, significantly enhancing prediction accuracy. In the final step, SSA-CNN is compared with five reference models on test samples. The SSA-CNN model showcases a maximum relative error of 0.155, outperforming other models. Moreover, the RMSE of this model yields 8.4500, which is also lower than other models. The results suggest that the combination of the selected gas indicators with the SSA-CNN model can accurately predict the spontaneous combustion temperature of coal.

Prediction Model of Borehole Spontaneous Combustion Based on Machine Learning and Its Application

In order to quickly and accurately predict borehole spontaneous combustion danger and avoid borehole spontaneous combustion fires, a borehole spontaneous combustion prediction model combining the Hunger Games search optimization algorithm (HGS) and Random Forest (RF) algorithm was introduced. The number of trees and the minimum number of leaf nodes in RF were optimized by HGS. Based on the data obtained from the temperature rise experiment of spontaneous combustion characteristics in a Shandong mine laboratory, O2, CO, C2H4, CO/∆O2 and C2H4/C2H6 were selected as the input indexes for the prediction of borehole spontaneous combustion, and the spontaneous combustion temperature was selected as the output indexes to train the built model. The prediction performance and accuracy of the model were evaluated using four indexes: the mean absolute error (MAE), mean absolute percentage error (MAPE), root mean square error (RMSE) and coefficient of determination (R2). At the same time, the prediction results of the HGS-RF model were compared with those of the RF model, Sparrow search algorithm (SSA) optimization RF model, particle swarm optimization RF model (PSO) optimization RF model and quantum particle swarm optimization RF model (QPSO) optimization. The results showed that the MAE of the RF, SSA-RF, PSO-RF, QPSO-RF and HGS-RF model samples were 17.541, 15.7752, 12.5903, 6.8594 and 6.6921, respectively. MAPE was 13.81%, 10.9766%, 9.6802%, 4.5731% and 5.1536%, respectively. RMSE values were 21.5646, 15.2017, 17.0091, 11.9879 and 12.1691, respectively. The R2 values were 0.9043, 0.9315, 0.9266, 0.9668, and 0.9717, respectively. At the same time, the reliability of the HGS-RF model was supplemented by taking the test data of the Zhengjia1204 coal mining face as an example. Finally, the model was applied to the prediction of borehole spontaneous combustion in the Jinniu Coal Mine, Shanxi Province. The prediction results show that the HGS-RF model can predict the spontaneous combustion temperature of different boreholes quickly and accurately. The results show that the HGS-RF model is more universal and stable than other models, indicating that the HGS-RF model is more suitable for the prediction of borehole spontaneous combustion.

Study on Thermodynamic Characteristics and Heat Transfer Method of Uncontrolled Fire in Coal Mine Gangue Mountain Spontaneous Combustion Based on System Dynamics.

Coal fire disaster caused by the spontaneous combustion of coal has always been one of the serious problems that threaten the safety of coal mining. In our study, we first solved the dynamic parameters and mechanical functions of coal gangue using the Achar differential method and Coats-Redfern integral method. Then, based on the flow and heat transfer mechanism of hot rod vapor-liquid two-phase flow, combined with coal spontaneous ignition conditions, influencing factors, coal pile spontaneous combustion temperature field structure distribution, etc., the heat transfer process of the hot rod in the coal pile (coal gangue mountain) was analyzed. Results show the average activation energies of the second stage of different types of coal mine gangue. Upon comparing the characteristic parameters of coal gangue in different regions and ages, it is found that 8# coal gangue has better combustion and burn-out characteristics, and its comprehensive combustion characteristics are second only to those of the coal gangue in Datong and higher than those in Panzhihua, Pingdingshan, and Hancheng. The higher the content of volatile matter and fixed carbon in coal gangue, the lower the ash content, and the better the comprehensive combustion performance of the coal gangue. Under the condition of sufficient oxygen supply combustion, the larger the fuel ratio, the better the burnout performance of the coal gangue. The test of the influence of the hot rod on the temperature field distribution inside the coal pile shows that the maximum cooling rate of a single hot rod to the coal pile during the test period is 33.4°C, and the maximum cooling rate reaches 39.6%. The calculated heat dissipation of the 80 h hot rod is 1.0865, 2.1680, and 3.3649 MJ, respectively.

Coal Spontaneous Combustion Temperature Prediction Based on Fuzzy Combined Kernel Relevance Vector Machine

This paper proposes a fuzzy combined kernel relevance vector machine method for the coal spontaneous combustion temperature prediction to avoid the shortcomings of traditional machine learning algorithms, such as the large prediction error, the weak generalization ability of the single kernel function, and the inability to deal with abnormal values. First, build a platform to simulate the coal spontaneous combustion scene and obtain the data of different index gas concentrations and the coal spontaneous combustion temperature through experiments. Second, the fuzzy algorithm is used to give the training samples different membership degrees with attention, so as to reduce the influence of outliers on the model. Third, the combined kernel function weighted by the polynomial kernel function and Gaussian kernel function is used to construct the input sample matrix to map the data from the low-dimensional space to a high-dimensional space, so as to obtain a better training model. Finally, the fuzzy combined kernel relevance vector machine model is constructed and compared with methods based on the radial basis function neural network, least squares support vector machine, Gaussian kernel relevance vector machine, and combined kernel relevance vector machine to verify the effectiveness of prediction for coal spontaneous combustion temperature. The results show that the fuzzy combined kernel relevance vector machine not only has the characteristics of strong generalization ability and weakening the influence of abnormal data, but also with a more sparse model, which is suitable for the prediction of coal spontaneous combustion temperature.

Prediction of coal spontaneous combustion temperature based on improved grey wolf optimizer algorithm and support vector regression

The effective prediction of coal spontaneous combustion temperature is of great importance to the monitoring and prevention of coal mine fires. Aiming at the problem of insufficient prediction accuracy of traditional coal spontaneous combustion temperature prediction model, and considering the characteristics of prediction data samples and the timeliness of applicable models, an improved grey wolf optimized support vector regression coal spontaneous combustion temperature prediction model based on nonlinear parameter control, dynamic inertia weights and grey wolf social hierarchy is proposed, and the effectiveness of the improved grey wolf optimizer algorithm is verified by numerical experiments. The O2 concentration, CO concentration, C2H4 concentration, CO/ΔO2, and C2H4/C2H6 selected from the coal spontaneous combustion procedure warming experiment were used as the input indexes of the prediction model, and the coal body temperature was used as the output index, and the prediction model was compared and analyzed with the particle swarm optimization support vector regression and grey wolf optimized support vector regression models through the experimental data. The results show that the improved grey wolf algorithm has stronger global search ability, faster convergence speed and better stability, and the proposed prediction model has strong advantages in accuracy and stability, which can provide better decision reference for coal spontaneous combustion fire prediction and warning in coal mines.

Unveiling the mechanisms of in-situ combustion of post-steam driven heavy oil by electric heating method induced auto-ignition

Ignition design of air injection wells is the crucial factor to a successful in-situ combustion. In this paper, experimental studies were carried out on post steam flooding wells at JIN-91 of Liaohe oilfield for improving ignition success rate. The experiments were conducted by a high temperature-pressure ignition device specifically designed for in-situ combustion, and the effects of various parameters were investigated by factorial experiments. The results showed that the ignition temperature was reduced and the ignition time was shortened due to three factors: heating temperature, reservoir pressure, and clay minerals. When heating temperature exceeded 360 °C, experimental pressure was higher than 4.0 MPa, and 10% of montmorillonite clay was added, the crude oil samples could be ignited rapidly. According to the factorial results, the most effective method to shorten ignition time is by increasing heating temperature, the safest method to reduce crude oil spontaneous combustion temperature and to shorten ignition time is by applying clay minerals, and reservoir pressure is the key factor for safety operation during ignition operation. These results will provide experimental theoretical guidance for high efficiency ignition, process optimization, and safe implementation of crude oil reservoir, they also will deliver tech support for the high efficiency operation of in-situ combustion.

Numerical investigation on combustion processes of an aircraft piston engine fueled with aviation kerosene and gasoline

Replacing gasoline with aviation kerosene for spark-ignition (SI) piston engines applied for aircrafts can get better security and easier logistics, however, this action cannot proceed smoothly by serious knocking combustion. In this study, the in-cylinder combustion for gasoline and aviation kerosene are investigated through computed-fluid-dynamics (CFD) to analyze combustion characteristics, and try to use pre-chamber technology to improve combustion issues.All CFD results demonstrate in-cylinder combustion differences effectively: All injected aviation kerosene cannot be completely evaporated, leading to non-uniformed unburned mixture and slow flame propagation. Some odd test phenomena are caused by unevaporated kerosene. The slower turbulence flame speed, worse equivalence ratio, higher local temperature and lower spontaneous combustion temperature lead to mixture auto-ignition and knocking combustion eventually. After adding a passive pre-chamber, the mixture distribution near spark plugs is more reasonable, higher turbulence kinetic energy (TKE) inside pre-chamber accelerates burning process, the ignition delay period is reduced by about 21.6%. The pre-chamber increases main-chamber's TKE, the whole combustion process is shortened by about 9%, the pre-flame reaction of unburned mixture near cylinder is shortened, contributing to suppress knocking combustion effectively. The passive pre-chamber technology is of great significance to the popularization and application for SI aircraft piston engine fueled with aviation kerosene.

Study on Multi-field Evolution and Influencing Factors of Coal Spontaneous Combustion in Goaf

ABSTRACT The coupled disaster of coal spontaneous combustion and gas explosion in the goaf threatens coal mine safety production. In order to further study the mechanism of coupled disasters and provide theoretical basis for disaster prevention and control, the multi-field evolutions and the key influencing factors of coal spontaneous combustion within methane gas condition of coal mine goaf had been investigated based on the special designed hot experimental platform and numerical simulations. Results show that the leakages of ventilation air into the goaf is positively related to the intensity of coal spontaneous combustion, but that is negatively related to the ranges of methane accumulations in the goaf. During the early stage of coal spontaneous combustion, the distributions of oxygen and methane in the goaf is less affected by the temperatures. When the temperature of coal spontaneous combustion exceeds 200°C, the oxygen-poor area, methane accumulation areas and a banded high-temperature distributions area in the non-spontaneous combustion areas would be clearly formed in the goaf. The changes of gas density in the goaf indicates that coal spontaneous combustion temperature in the goaf is another important factor influencing the change of gas density and gas accumulations, in addition to the ventilation air leakages.

Research on the prediction model of coal spontaneous combustion temperature based on random forest algorithm

Research on the prediction model of coal spontaneous combustion temperature based on random forest algorithm

The crystal structure and thermal decomposition kinetics of cis-hexanitrostilbene

Hexanitrostilbene (HNS) is an energetic material with wide application and excellent comprehensive performance. cis-HNS is successfully prepared using crude HNS with a purity of 95% as the raw material and N-methyl pyrrolidone (NMP) as the solvent. After separation and purification, acetone is used as a solvent to obtain light-yellow crystals at room temperature. The molecular structure of cis-HNS is determined through analysis of Fourier transform infrared, 13C NMR and 1H NMR spectroscopy and single-crystal X-ray diffraction data. The thermal decomposition properties of cis and trans-HNS are studied using differential scanning calorimetry (DSC). When the heating rate is low, cis-HNS will undergo a crystal transformation after melting, from liquid cis-HNS to liquid trans-HNS, and then it will solidify and release heat. According to the results of DSC data, the apparent kinetic parameters of thermal decomposition of cis- and trans-HNS were obtained by Kissinger method [Kissinger (1957). Anal. Chem. 29, 1702–1706] and Ozawa method [Ozawa (1965). Bull. Chem. Soc. Jpn. 38, 1881–1886], respectively. The spontaneous combustion temperature and self-accelerating decomposition temperature of cis and trans-HNS are calculated by the Zhang-Hu-Xie-Li method [Zhang et al. (1994). Thermochim. Acta, 244, 171–176].

Fuel characteristics of sewage sludge using thermal treatment

Pretreatment of sewage sludge under inert condition can produce enhanced solid fuel qualities. In this study, sewage sludge was torrefied in a horizontal tubular reactor at temperatures ranging from 200 to 450 °C. Improved fuel characteristics with reduced oxidized compounds which become more like coal were depicted by the CHO index and Van Krevelen diagram. The CHO index based on molecular C, H, and O data obtained for 450 °C was − 0.65. Statistical Grindability Index (SGI) gradually decreased with the lowest value for 450 °C while the fuel ratio increased with increase in torrefaction temperature. The spontaneous combustion of sewage sludge was analyzed and the average spontaneous combustion temperature of 211.4 °C was obtained.

Characteristics of polycyclic aromatic hydrocarbon release during spontaneous combustion of coal and gangue in the same coal seam

Based on the influence of temperature, coal quality, oxygen supply and other factors, the formation characters of PAHs during spontaneous combustion of coal and gangue in the same coal seam were experimentally studied. The results showed that the amount of PAHs released from coal and gangue first increased and then decreased with the rising of coal and gangue spontaneous combustion temperature. And it reached a peak value about 5.76–7.17 times of the minimum value at 400 °C–500 °C. The types of PAHs released during the high temperature stage of spontaneous combustion were up to 9. The release amount of PAHs generally increased with the rising of the volatile content, sulfur content, carbon content, fixed carbon content and calorific value of coal and gangue, but decreased with the rising of ash content. The amount of PAHs produced by spontaneous combustion of raw coal was generally higher than that of gangue, and the difference was more obvious at high-temperature stage. The total toxicity amount of PAHs produced by spontaneous combustion of raw coal was higher than that of gangue, up to 9.93 times. Its law of change with temperature was consistent with the total amount of PAHs, which increased and then decreased with the rising of temperature; and reached the peak at 300 °C–400 °C with a peak value of 6.72–14.60 times of the minimum value.

A new numerical method to predict the growth temperature of spontaneous combustion of 1/3 coking coal

Hazards caused by spontaneous coal combustion are of deep concern. To explore the process of spontaneous coal combustion, we focused on the key aspects of growth temperature (Tg) and the apparent activation energy (Ea), where Tg is associated with the Ea. We used four different heating rates to perform thermogravimetric (TG) experiments to calculate Ea values through an improved Kissinger–Akahira–Sunose method. The findings revealed a sudden change point in Ea values (that is, Tg), and comparisons between experimental data and calculated results demonstrated favorable agreement. The grey correlation analysis method was used to analyze the relationship between the samples’ Tg and intrinsic properties. A quasi-Newton method and general global optimization model were adopted to derive a predictive relationship for spontaneous coal combustion. The results are beneficial and crucial for elucidating the mechanism of spontaneous coal combustion, which could facilitate a thorough understanding of this process and help provide proactive loss prevention measures.

О возникновении пожаров в лесах из-за возгорания лесного опада

Fires in forests cause great environmental damage to our country due to loss of wood, destruction and damage to residential and industrial buildings and structures. Often fires in forests are accompanied by death of people. Large fires affect the pollution of the atmosphere, climate change in a certain area, generally have a negative impact on the environment of the affected regions, increase incidence and mortality rates of the population, create social tension in society. The paper analyzes the causes of anthropogenic and natural origin of fires. The main attention is paid to the conditions of spontaneous combustion of forest litter (litter) in coniferous forests, which can occur under certain conditions, even in winter, after snowfall. A physicochemical justification for the process of spontaneous combustion of forest litter is presented, and corresponding thermophysical calculations are made. It is shown that in the layer of litter of a certain thickness, a process of self-heating takes place and an increase in temperature due to an exothermic reaction, because of which the temperature rises in the litter - up to the temperature of spontaneous combustion. Such a picture will be observed until a certain self-heating temperature is reached at which the exothermic transformations in the material (decomposition, oxidation) begin, leading to self-heating of the material. When a certain temperature is reached, selfignition of the forest litter may occur. Specific examples of spontaneous combustion of forest litter in coniferous forests of the Voronezh region in the beginning of winter under a layer of snow and possible causes of spontaneous combustion of coniferous litter in forests during the summer are considered. It is shown that the process of spontaneous combustion can occur when certain climatic conditions are combined, as well as factors such as thickness, humidity and dispersed state, and the thermophysical characteristics of the litter, temperature and mobility of the atmosphere. Based on the solution of the equation of non-stationary Fourier thermal conductivity with an internal heat source, it is planned to determine the conditions for heat exchange on the surface and the thickness of the fallen layer of litter, in which a selfignition process may occur.

Laboratory Study on the Rising Temperature of Spontaneous Combustion in Coal Stockpiles and a Paste Foam Suppression Technique

The spontaneous combustion of coal stockpiles is one of the major hazards during coal mining, storage, and transportation. To overcome the shortcomings of large-scale experiments and CFD modeling techniques, a mathematical model of rising temperature in a hemispherical coal stockpile with a constant heat source in the center was derived and simplified based on a previous study on the spatiotemporal temperature and heat transfer in simple bodies. From the rising temperature experiments in a coal stockpile, the actual temperature rise was greater than the predicted value from the theoretical mathematical model, and the difference in these values increased with an increase in test time. The distribution of the temperature field caused by the spontaneous combustion of coal was fit to revise the mathematical model for the temperature rise in a coal stockpile. In addition, a novel material, called paste foam, was prepared to prevent the spontaneous combustion of a coal stockpile, and the working principle of th...

THE CONCEPT OF AN INTEGRATED LABORATORY CONTROL SYSTEM FOR A DUAL-FUEL DIESEL ENGINE

The need to limit greenhouse gases emissions and to increase the share of renewable fuels in the general energy balance forces people to look for new alternative energy sources. Methane or biogas, in which methane is the main combustible component, is one of such fuels, which is believed to be a promising diesel engine fuel. It is worth emphasizing that biogas might be believed to be one of the cleanest fuels due to the fact that it can be produced from waste, which in normal conditions decomposes releasing methane into the atmosphere. Methane (biogas) combustion in diesel engines requires a dual-fuel feed system, in which the liquid fuel initiating auto-ignition will be injected into the combustion chamber. It comes as a consequence of a relatively high spontaneous combustion temperature of methane, which is not able to self-ignite in the chamber without an external source of ignition. This article presents a concept of constructing an integrated laboratory control system, which will provide optimal feed control of complex composition gaseous fuel and of the injection of an appropriate pilot dose of a liquid fuel into the combustion chamber. The solution proposed to control a dual-fuel diesel engine, which was mounted on the test bed, enables performing comprehensive diesel engine research, ensuring not only stable working conditions for the engine, but also granting an opportunity to smoothly regulate the composition of the artificial biogas which feeds the engine and to control basic parameters of the liquid fuel pilot dose.

Thermal analysis of milling products and its implications in self-ignition

Thermal degradation of soft wheat, durum wheat, and maize as well as native starch and maize dextrin used to make fireworks, under nitrogen atmosphere, was studied by differential scanning calorimetry and thermogravimetric analysis. The results indicate that bran-free products give rise to a decomposition via transglycosylation-volatilization. On the contrary, wheat products containing bran or ligno-cellulosic residues decompose via exothermic reaction of dehydration-charring. For each product, the curve is determined as a function of its physical form, i.e., flour, ground product, and grain. The comparison of the conversion calculated as a first-order kinetics and the experimental, exhibits a very low mean square error. On this basis, the pyrolysis was assumed to obey first-order kinetics. The kinetic results were then used to estimate the temperature of spontaneous combustion, when these products are stored in silos. In particular, Frank-Kamenetskii theory is applied to prove how the products, under particular conditions, can become extremely explosive.

An investigation of the factors associated with interpretation of mine atmosphere for spontaneous combustion in coal mines

The risk of spontaneous combustion of coal is highly serious especially in gaseous underground coal mines. In many cases such a spontaneous combustion is a source of initiation of methane-explosive mixture with tragic consequences. Early indication of spontaneous combustion and determination of its seat temperature is in a given environment a key part of safety of underground coal mines. A commonly used method for the detection of spontaneous combustion is an interpretation of coal oxidation gas products whose historical roots go back to the first halve of the 20th century as Graham's ratio. In the second half of this century a wide research on application of desorbed higher hydrocarbons C1–C4 and hydrogen for the given purpose took place. Until now, the interpretations in question encounter significant inaccuracies. The paper describes the research on factors affecting the accuracy of the determination of coal spontaneous combustion temperature on conditions of underground coal mines. The research was focused on the accuracy of interpretation of mine gases in connection with the use of laboratory verified gas characteristics of thermal oxidation of coal. Three chromatograph laboratories had been involved to test thermal oxidation of coal to determine a deviation of spontaneous combustion gases. The variability among the laboratories and used techniques were insignificant. Besides the laboratory methods, it may not reflect processes in coal mines and one of the results was that coal itself had been a source of substantial variability of desorption of spontaneous combustion gases. The spontaneous combustion data still requires human interpretation. Simple criteria for providing warning by a computer do not exist just yet.

Coal proximate analyses correlations with airborne respirable dust and spontaneous combustion temperature

Coal proximate analyses correlations with airborne respirable dust and spontaneous combustion temperature