Article: Integration of computational algorithms in enhancing geological modelling and drilling efficiency in coal mine exploration Journal: International Journal of Oil, Gas and Coal Technology (IJOGCT) 2026 Vol.39 No.6 pp.1 - 27 Abstract: Coal calorific value calculation is crucial for power planning and mine exploration. Traditional lab techniques and empirical calculations often fail with high variability or limited data. We developed a machine learning framework for small-sample scenarios, combining compositional characteristics, data augmentation, and Bayesian hyperparameter adjustment to improve prediction accuracy. Four regression models (ANN, SVR, decision tree, and LightGBM) were trained on proximate, ultimate, and petrographic features to predict coal calorific value. Among these, the LightGBM model achieved the highest predictive performance with a test (R2 approximately 0.93) and the lowest error (RMSE approximately 0.19), outperforming the ANN (R2 approximately 0.91) and other models. Fixed carbon (FC) and volatile matter (VM) were the key predictors of calorific value, aligning with domain knowledge and model interpretability. The improved data-driven approach reliably estimates coal energy content from small samples, enabling evidence-based geological modelling and better drilling decisions for increased exploration efficiency. [Received: January 16, 2026; Accepted: May 8, 2026] Inderscience Publishers - linking academia, business and industry through research

Title: Integration of computational algorithms in enhancing geological modelling and drilling efficiency in coal mine exploration

Authors: Xingen Ma; Yubo Song; Qinghai Zou; Ying He; Bo Pan; Sixuan Ye; Guang Yang; Biwen Li; Fuhong Li; Kamel Bou-Hamdan

Addresses: Huaneng Coal Technology Research Co., Ltd., Beijing 100070, China; China University of Mining and Technology, Xuzhou 22111, China ' CCTEG Xi'an Research Institute (Group) Co., Ltd., Xi'an 710077, China ' Walnut Valley Coal Mine, Huaneng Qingyang Coal Power Co., Ltd., Qingyang 745300, China ' CCTEG Xi'an Research Institute (Group) Co., Ltd., Xi'an 710077, China ' Huaneng Coal Technology Research Co., Ltd., Beijing 100070, China; China University of Mining and Technology, Xuzhou 22111, China ' CCTEG Xi'an Research Institute (Group) Co., Ltd., Xi'an 710077, China ' Huaneng Coal Technology Research Co., Ltd., Beijing 100070, China ' CCTEG Xi'an Research Institute (Group) Co., Ltd., Xi'an 710077, China ' Walnut Valley Coal Mine, Huaneng Qingyang Coal Power Co., Ltd., Qingyang 745300, China ' Institute of Geoenergy Engineering, Heriot-Watt University, Edinburgh EH14 4AS, UK

Abstract: Coal calorific value calculation is crucial for power planning and mine exploration. Traditional lab techniques and empirical calculations often fail with high variability or limited data. We developed a machine learning framework for small-sample scenarios, combining compositional characteristics, data augmentation, and Bayesian hyperparameter adjustment to improve prediction accuracy. Four regression models (ANN, SVR, decision tree, and LightGBM) were trained on proximate, ultimate, and petrographic features to predict coal calorific value. Among these, the LightGBM model achieved the highest predictive performance with a test (R² approximately 0.93) and the lowest error (RMSE approximately 0.19), outperforming the ANN (R² approximately 0.91) and other models. Fixed carbon (FC) and volatile matter (VM) were the key predictors of calorific value, aligning with domain knowledge and model interpretability. The improved data-driven approach reliably estimates coal energy content from small samples, enabling evidence-based geological modelling and better drilling decisions for increased exploration efficiency. [Received: January 16, 2026; Accepted: May 8, 2026]

Keywords: calorific value prediction; light gradient boosting machine; LightGBM; artificial neural network; ANN; feature engineering; data augmentation; hyperparameter optimisation; coal characterisation; coal quality prediction.

DOI: 10.1504/IJOGCT.2026.154395

International Journal of Oil, Gas and Coal Technology, 2026 Vol.39 No.6, pp.1 - 27

Received: 26 Nov 2025
Accepted: 08 May 2026
Published online: 25 Jun 2026 *

Title: Integration of computational algorithms in enhancing geological modelling and drilling efficiency in coal mine exploration

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