**1. Introduction**

Coal spontaneous combustion is serious in China, resulting in a massive waste of resources. Coal spontaneous combustion monitoring and early warning technologies mainly include temperature detection methods, infrared detection methods, gas detection methods, magnetic force detection methods, transient electromagnetic methods, resistivity detection methods, etc. [1]. In recent years, bundle tube monitoring, distributed optical fiber temperature measurement, and wireless temporary network temperature measurement technology have also been widely used in coal spontaneous combustion disaster monitoring and early warning. However, these monitoring and early warning methods still have certain drawbacks [2]. Bundle tube monitoring temperature measurement and distributed fiber optic temperature measurement are subject to large environmental factors in practical applications, and the data is easily distorted in complex mine environments [3–5]. Wireless self-assembling network temperature measurement systems will not be affected by problems such as pipelines being damaged. Due to the complexity of the mining area environment, the wireless signal transmission is unstable, and difficult to achieve efficient monitoring [6].

**Citation:** Yin, J.; Shi, L.; Liu, Z.; Lu, W.; Pan, X.; Zhuang, Z.; Jiao, L.; Kong, B. Study on the Variation Laws and Fractal Characteristics of Acoustic Emission during Coal Spontaneous Combustion. *Processes* **2023**, *11*, 786. https://doi.org/10.3390/pr11030786

Academic Editors: Feng Du, Aitao Zhou and Bo Li

Received: 7 February 2023 Revised: 1 March 2023 Accepted: 2 March 2023 Published: 7 March 2023

**Copyright:** © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

AE detection technology based on coal heating and the heating process fracture to generate the elastic waves phenomenon for monitoring and early warning of coal spontaneous combustion is a non-contact, non-destructive testing technology. In recent years, many scholars have linked AE with the prediction of coal body thermodynamic disasters, realized non-contact detection of coal body thermodynamic disasters in coal mines through AE technology, and provided precursor information of coal body thermodynamic disasters through the characteristics of AE activities.

At present, scholars at home and abroad have conducted a large number of studies on the AE law of coal rock. Ganne et al. studied the AE characteristics of hard and brittle rocks and established the relationship between micro-rupture and AE parameters [7]. Ishida et al. studied the distribution characteristics of AE sources during misalignment tests [8]. Han et al. investigated the AE characteristics of different coal samples during the process under uniaxial compression and found that there were significant differences in the AE characteristics of coal with different strengths during the compression process [9]. Kong et al. [10–12] studied the AE nonlinear and fractal characteristics of AE under multiple heating and loading damage conditions of coal rocks. Chen, under laboratory conditions, analyzed the acoustic emission count, dissipation energy, and fracturing point distribution of different deformation stages of coal [13]. Zhang studied the AE signal pattern and multiple fractal characteristics of coal deformation damage under different conditions [14]. The AE response characteristics of anthracite coal were studied through ultrasonic experiments [15]. In uniaxial compression experiments, it was found that when the density was higher than a specific value, the wave velocity of the coal sample increased with the increase of density, and the distribution of AE events over time was approximately normally distributed. Li et al. studied the AE characteristics of different coal-thick coal-rock assemblies during the rupture process and found that the peak count of AE was negatively correlated with coal thickness, and the cumulative count of AE was positively correlated with coal thickness [16].

Scholars at home and abroad have also done a lot of research on the changing laws of the pore structure of coal bodies. They studied the pore structure and fractal characteristics of different coals and found that the pores and fractures in coal become simpler and simpler during the crushing of coal samples, which is more favorable for gas storage and transport [17,18]. Li et al. conducted uniaxial compression tests on coal samples with prefabricated cracks at different angles and found that the AE response showed significantly different characteristics at different stages [19]. Zhao et al. analyzed the influence of the question on the transformation of coal pore structure and concluded that the higher the temperature, the greater the pore fractal dimension of the large pore in the coal [20]. Yi et al. analyzed the pore evolution of hydrochloric acid-treated coal samples by the low-temperature gas adsorption (LTGA) method and found that fractal dimension D2 was significantly positively correlated with ash content and increased with the increase of hydrochloric acid concentration [21]. Xu et al. studied the effect of microwave-assisted oxidant stimulation based on low-temperature nitrogen adsorption on the pore structure and fractal characteristics of bituminous coal. They found that microwave-assisted oxidation expanded the internal pore space and promoted pore expansion [22].

The above studies show that during the heating process, the structure of the coal body will change significantly with the increase in temperature. At different temperatures, it will show different characteristics, mainly changes in the structure of pore fractures [23–30]. There are many similarities between the damage change of coal in the process of heating and the damage change at the time of load deformation and rupture, and both processes are accompanied by the generation of AE signal, which provides conditions for the monitoring and early warning of hidden fire sources under coal mines through AE technology. The fractal theory is primarily used in the study of uniaxial compression and thermal rupture damage of coal rocks, and in recent years, the research on AE of coal bodies has gradually increased, but these studies have less analysis of the law and generation mechanism of coal spontaneous combustion AE signal. The passive monitoring technology of AE can realize

the source finding and locating of underground fire sources and improve the efficiency of coal spontaneous combustion monitoring and early warning, and there is less research on the monitoring and early warning technology of the whole process of coal spontaneous combustion, which needs further research exploration in theory.

Based on the advantages of the acoustic method of temperature measurement, the application of AE technology for coal spontaneous combustion monitoring to achieve underground hidden fire source early warning has a good prospect. In contrast, the characteristics of AE signal changes during coal warming have not been studied in depth, and the mechanism of AE generation during coal warming has not been revealed. Based on this, this paper constructs a large-scale coal spontaneous combustion AE test system and a low-temperature nitrogen adsorption experimental platform, studies the thermal rupture and AE signal laws during the spontaneous combustion and heating of coal, analyzes the AE count and energy changes during the coal spontaneous combustion, reveals the temporal and frequency characteristics of AE signal by using Fourier transform, analyzes the fractal characteristics of low-temperature nitrogen adsorption and AE counts by fractal theory, and discusses the mechanism of the pores generated by thermal damage of coal bodies during coal spontaneous combustion heating process as the source of AE signal. The counting, energy, and spectral laws of the AE signal in the coal spontaneous combustion process are studied, and the fractal laws of BET of coal after different temperature treatments are analyzed. The nonlinear characteristics between AE and the pore structure of coal are revealed. This study lays a foundation for further exploring the heating coal body's AE signal generation mechanism and improving the monitoring and early warning of the spontaneous combustion fire of the AE signal coal.
