Study on the generation of active sites during low-temperature pyrolysis of coal and its influence on coal spontaneous combustion

煤低温热解过程中活性点位的产生及其对煤自燃的影响  

Fuel, Volume 241, 1 April 2019, Pages 283-296

Jinhu Li, Zenghua Li, Yongliang Yang, Xiaoyan Zhang

摘要：The coal after low-temperature pyrolysis is more prone to self-heating and spontaneous combustion. To study the original heat source, this paper conducted sequentially isothermal pyrolysis, room-temperature oxidation and low-temperature re-oxidation on four different coal samples. Formation laws of active sites were studied through gas emission rules during isothermal pyrolysis of coal. Besides, the influence of pyrolyzed coal at low temperatures on spontaneous combustion in following mining process was stimulated by the subsequent room-temperature oxidation and low-temperature re-oxidation of the coal. Experimental results reveal that during low-temperature pyrolysis of coal samples, oxygen-containing functional groups are decomposed. They produce numerous active sites which are stabilized in anaerobic conditions. These sites are so active that they can be oxidized at room temperature to release massive CO and CO2 and huge heat. Therefore, the viewpoint of room-temperature oxidation of active sites is proposed. The exothermic oxidation of active sites at room temperature is the initial heat source of self-heating and spontaneous combustion of the pyrolyzed coal. Furthermore, room-temperature oxidation of the pyrolyzed coal presents object proof of direct burn-off reaction, which is of great significance to the interpretation of coal spontaneous combustion mechanism.

Ultrasonic extraction and oxidation characteristics of functional groups during coal spontaneous combustion

煤自燃期间官能团的超声提取与氧化特征

Fuel, Volume 242, 15 April 2019, Pages 287-294

Yutao Zhang, Chaoping Yang, Yaqing Li, Yao Huang, Qipeng Li

摘要：The reactions between oxygen molecules and functional groups are the internal cause leading to heat release and the corresponding coal spontaneous combustion. However, the difficult discrimination of the multitudinous functional groups tremendously limits the deep insight into the mechanism of coal-oxygen reactions. Ultrasonic extraction technology, in this case, was utilized to extract the different functional groups from coals. The extracted coal samples were further collected for tests of Fourier infrared (FTIR) spectrum, electron paramagnetic resonance (EPR) spectrum, and thermogravimetric (TG) analysis. The experimental results indicated that Dimethylacetamide (DMAC) had the largest extraction rate in terms of the changes of mass or free radicals. While Acetone exhibited great extraction ability for CH3 and Cyclohexanone (CYC) could effectively extract COC. The greatest extraction rate of OH was possessed by N-methylpyrrolidone (NMP). Both TG and differential scanning calorimeter (DSC) tests proved that OH played a dominant role in the oxidation stage of coal. Compared to OH, other functional groups like CH2, CH3 and COC had fewer effects on the mass change and heat release during coal spontaneous combustion.

Influence of organic and inorganic properties of coal-shale on spontaneous combustion liability

煤页岩有机与无机特性对自燃倾向性的影响

International Journal of Mining Science and Technology, In press, corrected proof, Available online 4 March 2019

M. Onifade, B. Genc, N. Wagner

摘要：Coal and coal-shale undergo low-temperature oxidation when exposed to air, potentially leading to spontaneous combustion. Coal-shale found in association with coal seams vary considerably in their intrinsic properties and spontaneous combustion liability index. Fourteen coal-shale samples collected from four different coal mines in Witbank Coalfield, South Africa, were experimentally investigated. The influence of coal-shale intrinsic properties and spontaneous combustion liability indices (determined by the Wits-Ehac Index and the Wits-CT Index) were established. The liability indices indicate relationships with the intrinsic factors and thus, identifying the major intrinsic factors affecting liability toward spontaneous combustion in these coal-shale samples. The XRF analysis indicated that the coal-shale samples are rich in SiO2, Al2O3 and Fe2O3, while the XRD showed that same coal-shale samples are generally dominated with kaolinite and quartz. The coal-shale occurred in association with medium Rank C bituminous coal and contained varying proportion of macerals. The Wits-Ehac Index was unable to reliably determine liability indices of some coal-shale samples, and hence the Wits-CT Index was developed. The results obtained from the characterisation tests may be used as a tool to predict the spontaneous combustion liability in carbonaceous material and may serve as a reference when comparing coal-shale from different coal mines.

Forecasting spontaneous combustion of coal in underground coal mines by index gases: A review

基于指标气体的地下煤矿煤自燃预测综述

Journal of Loss Prevention in the Process Industries, Volume 57, January 2019, Pages 208-222

Yuntao Liang, Jian Zhang, Liancong Wang, Haizhu Luo, Ting Ren

摘要：Spontaneous combustion of coal in underground coal mine is a long-standing thermal dynamic hazard. The hazard is harmful in diverse aspects: causing loss of coal resource, raising safety concerns, and giving off noxious/greenhouse effect gases. Detection and trending analysis of a few particular gaseous products liberated during coal oxidation is the most fundamental spontaneous combustion forecasting technique in practice. This study mainly reviewed the mechanism and practical knowledge by using such technique to forecast spontaneous combustion. To give more insights in emerging order of fire gas, this study critically reviewed and analysed the detailed production sequences of these key gaseous products. It was indicated production of carbon oxide, hydrogen, methylene and some other hydrocarbon gases can be used to forecast early heating of coal. This study also summarised and discussed the interpretation of the index gases through absolute concentration of key gas indicators and composite ratios. Six common gas monitoring techniques (i.e. tube bundle system, telemetric system, portable system, gas chromatography, infrared spectroscopy, and tunable diode laser adsorption spectroscopy) are discussed in terms of their advantages and limitations in this study. Lastly, a practically demonstrated spontaneous combustion hazard management plan (i.e. TARP) is introduced. TARP uses different gas ratios in various locations to indicate escalating levels of severity of a heating event.

Proactive inertisation in longwall goaf for coal spontaneous combustion control-A CFD approach

长壁采空区煤自燃防治的积极“惰性”方案– 一CFD方法

Safety Science, Volume 113, March 2019, Pages 445-460

Jian Zhang, Hongtu Zhang, Ting Ren, Jianping Wei, Yuntao Liang

摘要：Spontaneous combustion of coal has long been a thermal dynamic hazard during coal mining, storage, and transport, posing a great threat to coal mine safety. Especially coal spontaneous combustion in longwall goaf poses a great threat to underground working crew. To investigate such a mine safety issue with more insights and “what if” scenarios, this study employs CFD technique to develop a proactive inertisation plan in a longwall goaf. Based on real on-site conditions of the longwall goaf, a three-dimensional transient non-equilibrium thermal CFD model was developed to study heating evolution and proactive inertisation plans of the longwall goaf. The theoretical model incorporated a set of governing equations including low temperature kinetics of coal oxidation, energy and mass conservation, momentum balance, and continuity equation. After the base model (1000 m) was validated and calibrated with field gas monitoring data, another model (500 m model) was studied to obtain an optimum inertisation plan. Both steady state and transient simulations were conducted to study the flow dynamics of air velocity, oxygen ingress, dispersion of gaseous products and heating evolution in the longwall goaf. Based on the flow dynamics field, proactive inertisation plans using nitrogen to suppress the onset and development of goaf heatings were then developed and studied.

Experimental study of the effects of stacking modes on the spontaneous combustion of coal gangue

堆积模式对煤矸石自燃影响的实验研究

Process Safety and Environmental Protection, Volume 123, March 2019, Pages 39-47

Yuguo Wu, Xiaoyang Yu, Shengyong Hu, He Shao, Yurong Fan

摘要：Coal gangue is a solid waste material which is generated during coal mining processes. Due to its low utilization value, it is normally discarded on gangue fields where it is stacked into very large hill formations. Spontaneous combustion is one of the major hazards which frequently occur during the long-term stacking of coal gangue. This study targeted the coal gangue stacks of the Chengzhuang Coal Mine, located in China’s Shanxi Province. The results of programmed heating tests helped determine that CO and C2H4 could be used as the index gases in the predictions of the self-heating degrees of coal gangue. Four of the typical coal gangue stacking modes which were often adopted by the Chengzhuang Mine were selected as the experimental objects of this study. The corresponding coal gangue models were piled in a field, and the self-heating degrees were measured with the passage of time. The entire experiment lasted for 60 days. The results showed that the loess isolation and loess stratified stacking methods could effectively suppress the self-heating processes. Also, the CO production and O2 consumption of the coal gangue were consequently reduced. The study also indicated that in the loess stratified stacking, the inhibitory effects on the spontaneous combustion of the coal gangue had increased with the increased thicknesses of the loess layers.

A comparison of random forest and support vector machine approaches to predict coal spontaneous combustion in gob

随机森林与支持向量机方法预测采空区煤自燃的比较

Fuel, Volume 239, 1 March 2019, Pages 297-311

Changkui Lei, Jun Deng, Kai Cao, Yang Xiao, Chimin Shu

摘要：The accurate prediction of coal temperature plays a vital role in preventing and controlling the spontaneous combustion of coal in coal mines. In this study, a long-term in-situ observation experiment was conducted in a fully mechanized caving face of the Dafosi Coal Mine, where the in-situ data of gases and temperature were obtained. Two machine learning approaches, random forest (RF) and support vector machine (SVM) were introduced and compared for predicting coal spontaneous combustion based on the in-situ monitoring data. The particle swarm optimization (PSO) was employed to optimize the RF and SVM by finding their optimal hyper-parameters. Principal component analysis (PCA) was used to transform the original input data into a new dataset of uncorrelated variables, reducing dimension for input variables. The results indicated that regardless of whether the models with or without PCA, the RF model was more robust than the SVM model and less affected by its own parameters, while the SVM model was highly sensitive to its parameters. Although the PSO could find the optimal hyper-parameters of the RF model, the RF model with default parameters could also accurately predict coal spontaneous combustion and possess satisfactory generalization. However, the predictive performance of the SVM model was dramatically improved in predicting after the PSO optimization. Moreover, the models with PCA also showed the above characteristics. These results suggest that both the RF and SVM methods can be used to predict coal spontaneous combustion, while the RF method can obtain accurate predictions without special parameter settings, it is more suitable for practical applications and can potentially be further employed as a reliable method for the determination of complicated relationships.