Prediction model for spontaneous combustion temperature of coal based on PSO-XGBoost algorithm.

ABSTRACT: The construction of a predictive model that accurately reflects the spontaneous combustion temperature of coal in goaf is fundamental to monitoring and early warning systems for thermodynamic disasters, including coal spontaneous combustion and gas explosions. In this paper, on the basis of programming temperature experiment and industrial analysis, 381 data sets of 9 coal types are established, and feature selection was executed through the utilization of the Pearson correlation coefficient, ultimately identifying O₂, CO, CO₂, C₂H₄, C₃H₈, C₃H₈/CH₄, C₂H₄/CH₄, C₂H₄/C₃H₈, CO₂/CO, and CO/O₂ as inpu

SUBMITTER: Zhuo H 

PROVIDER: S-EPMC11751149 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature