The study, published in Natural Resources Research, was led by Changkui Lei and Qi Qiao of Taiyuan University of Technology, together with Jun Deng and Jingyu Zhao of Xi’an University of Science and Technology, Chuanbo Cui, and Weigang Wang of the Gansu Bureau of the National Mine Safety Administration. Their central innovation is a hybrid model that pairs support vector regression, a powerful but notoriously parameter-sensitive prediction method, with the grey wolf optimization algorithm, a metaheuristic that mimics the leadership hierarchy and hunting strategy of wolf packs. The result, dubbed GWO-SVR, achieved a coefficient of determination of 0.9902 on test samples, meaning the model explained more than 99 percent of the variance in coal temperature during oxidation experiments.

To understand why this matters, it helps to grasp the physics of spontaneous combustion. When coal is exposed to oxygen at ambient temperatures, it oxidizes slowly, releasing heat. In loose coal piles, goaf areas behind longwall mining faces, and stockpiles, that heat can accumulate faster than it dissipates. As temperature rises, oxidation accelerates exponentially, creating a runaway feedback loop. The researchers’ large-scale coal oxidation experiment, using samples from the Paner Coal Mine, traced this process in detail. They observed that the high-temperature point inside their experimental furnace did not stay put: it migrated dynamically from the middle-upper section toward the lower section, eventually settling at the air inlet position, where fresh oxygen continuously fed the reaction.

The experimental campaign also quantified the characteristic indicators that signal a developing fire. The oxygen consumption rate, the generation rates of indicator gases, and the exothermic strength of the oxidation reaction all followed exponential growth trends as temperature climbed. These gas signatures, including carbon monoxide and other products of low-temperature oxidation, are what mine safety engineers monitor in the field. The challenge has always been translating gas concentrations measured in a mine roadway back into an accurate estimate of the coal temperature deep inside a goaf, where no thermometer can reach. That inverse problem is exactly where machine learning excels, provided the model’s internal parameters are tuned correctly.

This is where the grey wolf enters the story. Support vector regression depends critically on hyperparameters, such as the penalty factor and kernel settings, that govern how it fits nonlinear relationships in data. Set poorly, the model underfits or overfits; set well, it generalizes beautifully. Traditionally, engineers have relied on trial and error or grid searches. The grey wolf optimization algorithm instead treats the parameter search as a simulated hunt: candidate solutions are ranked as alpha, beta, delta, and omega wolves, and the pack iteratively encircles and converges on the optimal parameter combination. The researchers found that this optimization step was decisive. On the experimental test set, the root mean square error of the GWO-SVR model dropped to 2.4725, while a grey wolf-optimized back propagation neural network achieved 2.9024, both substantially better than standalone SVR and BPNN models.

Laboratory results alone rarely convince mining engineers, so the team took a further step that distinguishes this work: field verification. They validated the GWO-SVR model against in situ monitoring data from the Sanhejian Coal Mine, a real underground environment with all the messiness that entails, including variable airflow, moisture, and heterogeneous coal distributions. Under these genuine field conditions, the GWO-SVR model posted a root mean square error of just 0.9310 degrees in its temperature predictions, compared with 1.7778 for standalone SVR, 2.5469 for the BPNN, and 1.2791 for the GWO-BPNN. The comparison underscores that the optimization algorithm, not merely the choice of base model, drives the performance gain.

The implications for mine safety are significant. Spontaneous combustion fires in goaf areas are extraordinarily difficult to detect early because they develop out of sight, behind sealed zones, and by the time smoke or elevated carbon monoxide reaches sensors, the fire may already be well established. A model that can convert routinely measured indicator gas concentrations into a reliable temperature estimate gives fire prevention teams a quantitative gauge of how close the coal is to critical stages, allowing targeted interventions such as nitrogen injection, grouting, or adjusted ventilation before conditions become dangerous. The authors’ earlier work, including comparisons of random forest and support vector machine approaches and studies of high-temperature point migration, laid the groundwork for this refined approach.

The study also situates itself within a broader scientific effort to tame coal’s reactivity. Recent research has explored biomass aerogels that inhibit combustion at the microstructural level, shape memory hydrogels and plastogels for fire prevention, and thermokinetic analyses of low-rank coals during low-temperature oxidation. Other teams have applied genetic algorithm-optimized SVR to predict coal temperature from carbon monoxide and used back propagation networks for spatio-temporal temperature prediction. What the new study adds is a rigorous, experimentally grounded pipeline: a large-scale oxidation experiment to characterize the indicators, a hybrid model whose hyperparameters are intelligently optimized, and validation at both laboratory and field scales, a combination that few previous studies have achieved end to end.

From a technical standpoint, the exponential growth patterns observed in oxygen consumption, gas generation, and exothermic strength explain why linear or simple empirical models have historically struggled. The relationship between indicator gases and coal temperature is strongly nonlinear, with different gases dominating different temperature ranges. Machine learning models can capture these nonlinearities, but only if trained on representative data and tuned with care. The GWO-SVR framework addresses both requirements, and its strong generalization performance on unseen test samples suggests it is not merely memorizing the experimental data but learning the underlying physics of coal oxidation.

For an industry still central to global energy supply, and for the hundreds of thousands of miners who work underground every day, tools like this represent a quiet but meaningful advance in the long fight against one of mining’s oldest hazards. The research was supported by the National Natural Science Foundation of China and the Basic Research Program of Shanxi Province. As mines push deeper and coal seams become more prone to self-heating, the ability to forecast the invisible heat before it becomes an inferno may prove not just scientifically elegant but genuinely lifesaving.

Subject of Research: Machine learning prediction of coal spontaneous combustion temperature using grey wolf optimization and support vector regression

Article Title: Enhanced Prediction of Coal Spontaneous Combustion Temperature via GWO-SVR with Experimental and Field Verification

Article References: Lei, C., Qiao, Q., Deng, J., Zhao, J., Cui, C., & Wang, W. (2026). Enhanced Prediction of Coal Spontaneous Combustion Temperature via GWO-SVR with Experimental and Field Verification. Natural Resources Research. https://doi.org/10.1007/s11053-026-10779-9

Image Credits: AI Generated

DOI: 10.1007/s11053-026-10779-9

Keywords: coal spontaneous combustion, grey wolf optimization, support vector regression, machine learning, mine safety, indicator gases, temperature prediction, coal oxidation, nonlinear prediction, field verification, Enhanced, Prediction