Calorific Value Estimation in Coal Analysis

The calorific value of coal represents a key parameter for assessing fuel quality, energy content and environmental impact in mining, power generation and industrial applications. Established measurement techniques, notably the bomb calorimeter, provide direct determination of gross calorific value under standardised conditions. However, these methods are resource intensive and time consuming. Hence, coal analysis has long relied on correlations between calorimetric results and compositional data from proximate and ultimate analyses. Empirical models, based on fixed carbon and volatile matter, have underpinned calorific value estimation for decades but often lack generality across coal ranks and origins. Recent advances focus on integrating mineralogical and petrographic information alongside machine-learning and optimisation algorithms to enhance predictive accuracy and interpretability. These approaches enable rapid assessment of gross and lower calorific values from routine laboratory data, reduce dependence on calorimetric assays and support carbon-emission accounting. They also facilitate quality control in coal supply chains, process optimisation in combustion technologies and lifecycle analysis for decarbonisation strategies.

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Calorific Value Estimation in Coal Analysis publication trend

The graph below shows the total number of articles in calorific value estimation in coal analysis across all publications each year (not limited to Nature Index journals).

Technical terms

Gross calorific value (GCV): Total energy released by complete combustion of a unit mass of coal, including condensation of combustion products.

Lower calorific value (LCV): Net energy released per unit mass, excluding latent heat of water vapour formation.

Proximate analysis: Quantitative determination of moisture, volatile matter, ash and fixed carbon in coal.

Ultimate analysis: Measurement of elemental composition—carbon, hydrogen, oxygen, nitrogen and sulfur—within coal.

Gradient boosting (XGBoost): An optimised ensemble machine learning algorithm that builds sequential decision trees to improve predictive performance.

SHAP (Shapley Additive ExPlanations): A framework for interpreting the contribution of each input feature to a model’s output using concepts from cooperative game theory.

References

1. Prediction of gross calorific value from coal analysis using decision tree-based bagging and boosting techniques. Heliyon (2023).
2. A Fast Screening Method of Key Parameters from Coal for Carbon Emission Enterprises. Energies (2023).
3. Estimation of gross calorific value based on coal analysis using an explainable artificial intelligence. Machine Learning with Applications (2021).