Syngas Calorific Value Prediction for Underground Coal Gasification Based on Informer

Coal property and operational parameter variations cause, the underground coal gasification (UCG) systems to exhibits diverse gasification processes. As gasification reactions persist, the secondary combustion proportion of combustible gases, increases, leading to unstable gasifier reaction states. Consequently, the composition and calorific value of syngas decline after a stable production period, necessitating the intervention of new control methods to prevent further deterioration. This study proposes eight characteristic variables that influence the calorific value of syngas based on the mechanism analysis of the UCG process. Through similar simulation experiments, datasets were collected for UCG under various characteristic variables. Using the Pearson analysis results from high-quality datasets, different input combinations were established, and a syngas calorific value prediction model was proposed. The model's performance was evaluated from different perspectives using three evaluation metrics: RMSE, MAE, and R². The prediction performance was studied under three types of gasification channels, three sampling frequencies, and four prediction lengths. The results indicate that the Informer model performs well with optimal input. In long-sequence predictions, the R² value under optimal input exceeds 80%.