INTRODUCTION:Underground coal fires pose significant environmental and health risks due to releasing CO2 emissions. Predicting surface CO2 flux accurately in underground coal fire areas is crucial for understanding the distribution of spontaneous combustion zones and developing effective mitigation strategies. In recent years, advanced machine learning techniques have shown promise in various carbon-related studies. This research uses an experimental approach to explore the power of advanced machine learning schemes for predicting CO2 flux in underground coal fire areas. OBJECTIVES:By leveraging the power of advanced machine learning schemes and experimental approaches, this research aims to provide valuable insights into CO2 flux prediction in coal fire areas and inform environmental monitoring and management strategies. METHODS:The study involves the collection of an experimental dataset specific to underground coal fire areas, encompassing various parameters related to CO2 flux and underground coal fire characteristics. Innovative feature engineering techniques are applied to capture the unique characteristics of underground coal fire areas and their impact on CO2 flux. Different machine learning algorithms, including Natural gradient boosting regression (NGRB), Extreme gradient boosting (XGboost), Light gradient boosting (LGRB), and random forest (RF), are evaluated and compared for their predictive capabilities. The models are trained, optimized, and assessed using appropriate performance metrics. RESULTS:The NGRB model yields the best predictive performances with R2 of 0.967 and MAE of 0.234. The novel contributions of this study include the development of accurate prediction models tailored to underground coal fire areas, shedding light on the underlying factors driving CO2 flux. The findings have practical implications for delineating the spontaneous combustion zone and mitigating CO2 emissions from underground coal fires, contributing to global efforts in combating climate change.

Coal spontaneous combustion (CSC) threatens the safety of the coal industry, with moisture content and particle size being pivotal factors. This study examines the heating dynamics and critical self-ignition temperatures (CSITs) of Baiyinhua lignite stockpiles through wire-mesh basket (WMB) tests at two scales. The CSC process in coal stockpiles unfolds in four stages. Notably, Stage II is notable for significant moisture evaporation between 43 and 84 degrees C, while Stage IV marks the onset of self-heating. Moisture evaporation absorbs heat, linearly prolonging the Stage II duration, which accounts for 0%-70 % of the total time. Conversely, larger particle sizes enhance pore seepage, effectively shortening the heating time. The time for d = 20 mm (particle size) coal sample to reach ambient temperature is roughly half that of 1.5 mm. CSITs of raw coal increase by 10-15 degrees C compared to the dry coal, and CSIT of coal samples with d = 50 mm has increased by 17.5 degrees C compared to 10 mm. Therefore, both an increase in particle size and moisture content increase the CSIT, thereby reducing the propensity for CSC. FrankKamenetskii theory and dimensionless analysis predict spontaneous combustion risks in field-scale coal stockpiles. This investigation contributes valuable insights to the estimation and prevention of CSC.

Abandoned coal mine goaf is affected by air leakages and prone to spontaneous combustion, resulting in environmental pollution and geological disasters. Haizhou Open-pit Mine adopts both underground and open-pit mining methods. During the long-term mining process, the original stable stratum structure is constantly destroyed, and the slope slides, increasing cracks and severe air leakage around the goaf and roadway. The spontaneous combustion of coal is particularly prominent after the mine shut down. At present, there is no suitable indirect monitoring method to effectively explore the spontaneous combustion area in goaf. The study developed an all-weather monitoring plan and conducted multi-point continuous long-term measurements of the spontaneous combustion state in one abandoned coal mine goaf located in the eastern part of the Haizhou Open-pit Mine. We evaluated the dynamic correlation between surface CO2 flux (SCF) and changes in the underground fire areas, determined the scope and evolution trend of the fire areas, and identified the distribution and change laws of SCF. The results show a significant positive correlation between SCF and soil temperature; moreover, the SCF value was found to reflect the CO2 emission intensity of the goaf. The high SCF in the test area showed month-wise expansion and increase, while the CO2 emission gradually increased monthly, and the calculated annual total emission was approximately 7017 t. Hence, the study can further provide guidance for the monitoring of spontaneous combustion in shallow coal seams, goaf and the assessment of CO2 emissions from underground coal fires through the on-site monitoring and analysis results.

An optical sensing approach that balances portability with cost efficiency has been designed for the reliable monitoring of fugitive methane (CH4) emissions. Employing a LiTaO3-based pyroelectric detector integrated with micro-electro-mechanical systems and a broad infrared source, the developed gas sensor adeptly measured CH4 concentrations with a low limit of detection of about 5.6 ppmv and showed rapid response times with t90 consistently under 3 s. Notably, the novelty of our method lies in its precise control and reduction of CH4 levels, enhanced by wavelet denoising. This technique, optimized through meticulous grid search, effectively mitigated noise interference noticeable at CH4 levels below 10 ppmv. Postdenoising, nonlinear regression analyses based on the modified Beer-Lambert equation returned R2 values of 0.985 and 0.982 for the training and validation sets, respectively. In conclusion, this gas sensor has been shown to be able to meet the requirements for early warning of CH4 leakage on the surface in various carbon capture, utilization, and storage projects such as enhanced oil or gas recovery projects using CO2 injection.

Underground coal fires release substantial carbon dioxide (CO2), posing significant environmental and health threats. Accurate prediction of surface CO2 emissions in these areas is crucial for understanding combustion zones and contributes to the global net zero carbon strategy. Traditional data analysis methods have been inadequate for CO2 flux prediction, highlighting the necessity for advanced machine learning (ML) techniques. This study introduces four optimized ML models-General Regression Neural Networks (GRNNs) and Radial Basis Function Neural Networks (RBFNNs) coupled with Grey Wolf Optimizer (GWO), Particle Swarm Optimization (PSO), and Whale Optimization Algorithm (WOA)-to rapidly predict CO2 flux in areas affected by underground coal fires. Utilizing 223 field test samples, these models consider six key variables: soil temperature at 30 cm depth (ST -30), ambient pressure/temperature/humidity (AP/AT/AH), soil water content (SWC), and wind speed (A -WS). The results underscore the superior predictive accuracy of the GRNN model, with an RMSE of 0.074 and an R2 of 0.9995. Sensitivity analysis reveals A -WS and ST -30 as the most influential factors. Compared to traditional methods, these ML models demonstrate enhanced accuracy and efficiency, marking a significant advancement in the field. The study's findings have broader applications beyond underground coal fires, suggesting potential for these ML models in other environmental monitoring contexts, such as emissions tracking in urban areas or integration with satellite data for global environmental assessment. This methodology represents a pivotal step in environmental management and monitoring, offering scalable and adaptable solutions for various ecological challenges. By rapidly and accurately estimating CO2 flux from underground coal fires, this study contributes significantly to achieving the global net zero carbon target and sets a new benchmark in environmental ML applications.

Abstract Affected by the air leakage, abandoned coal mine goaf is prone to spontaneous combustion, resulting in environmental pollution and geological disasters. Based on previous studies, this paper further conducted long-term, multi-measurements, and continuous measurements of the spontaneous combustion state in the abandoned mine goaf in the eastern part of the Haizhou Open-pit Mine. We evaluated the correlation between the surface CO2 flux and the changes in underground fire areas, the scope and evolution trend of the fire areas, and the distribution and change laws of surface CO2 fluxes. The results show that there is a significant positive correlation between the surface CO2 flux and soil temperature. The surface CO2 flux value reflects the CO2 emission intensity of goaf. The high surface CO2 flux in the test area showed month-wise expansion and increase. The CO2 emission gradually increases with the months, and the annual CO2 emission in the experimental area is approximately 7017 t.

There is a critical situation that a huge amount of greenhouse gases is emitted into the atmosphere from natural coal fires in underground coal seams and spontaneous combustions in coal piles in the world. To reduce the emissions of combustion gases, such as CO2, CH4, etc., some technical challenges and researches are required related to CO2 flux measurements, developing an effective chemical water-solution extinguishes coal fires and inhibits spontaneous combustion, constructing a numerical simulation model to treat the aging effect in coal heating rate. In this article, firstly, the previous studies were reviewed to explain measurement results of CO2 emission from coal fire/spontaneous combustion fields and the aging model used to simulate coal heating rate. Secondary, a trial development of some chemical solutions has been introduced to inhibit microscopic and macroscopic coal oxidations by forming a gel. Especially, sodium meta silicate solution showed a relatively good performance shifting the critical self-ignition temperature (CSIT) of a low rank coal to be 4 to 5℃ higher than that of water by forming gel with CO2 gases in the air and combustion gases emitted from coals. The brooking effect in macro scale by forming gel is also expected to extinguish coal seam fires by reducing aperture width in fractures connecting to the atmosphere. A numerical simulation using ANSI FLUENT has showed an extinguishing fire process of an underground coal seam including a horizontal fracture that is connected inlet and outlet fractures being air paths for air and combustion gas, respectively. Finally, the the research challenges required for technical developments have been proposed to carry a project extinguishing the coal seam fires and spontaneous combustions of coal piles with reducing CO2 emissions.

Self-heating of coal mine goaf or shallow coal seams can release an outbreak of unimaginable pollution disaster under suitable circumstances. As an indicator gas, CO2 is always used to determine the coal spontaneous combustion state during the selfheating process. Based on this, the paper investigated the influence of abandoned coal mine goaf CO2 on the surface environment by measuring the CO2 concentration in the borehole connected to the goaf and CO2 flux on the soil surface. Furthermore, rainfall and atmospheric temperature effects are discussed to illustrate the correlation between the CO2 concentration and surface soil CO2 flux in the closed mine goaf. Subsequently, the tracer gas experimental method is employed to analyze the effect of air leakage from an openpit slope on CO2 flux. The experimental results demonstrated that the distribution of CO2 concentration in the borehole confirms the continuous diffusion of goaf CO2 onto the surface. The value of CO2 flux in the goaf is significantly higher than that of a normal area. Temperature is one of the primary factors that affect the CO2 flux on the field. Air leakage from the slope promotes the surface soil-overlying goaf CO2 diffusion. The study provides important reference data for the assessment of the mining area field environment and the determination of the spontaneous combustion risk of the residual coal in the goaf.