article · Next Energy
This research developed machine learning models to predict the calorific value of biomass, addressing the labour-intensive and costly nature of traditional experimental methods. The models utilise data from proximate and ultimate analyses of raw biomass, briquettes, and charcoal derived from agricultural residues. A dataset of 600 samples was compiled from published literature. Among the evaluated machine learning algorithms, KNN and SVR demonstrated the best predictive performance, achieving high accuracy with low prediction errors. The proximate-analysis-based framework showed superior stability and comparable accuracy to the ultimate-analysis-based framework. These frameworks offer a rapid, interpretable, and cost-effective approach for estimating biomass calorific value.
Accurately knowing the energy content of biomass is crucial for its use as a renewable fuel. This research provides a faster and cheaper way to estimate this value, helping to identify suitable biomass sources more efficiently and support sustainable energy production from agricultural waste.
This research provides a reliable framework for estimating biomass calorific value, which could be used by biofuel producers and waste management organisations. The rapid and cost-effective nature of the machine learning models supports efficient biomass screening and sustainable waste-to-energy applications. This appears to be an applied research outcome, offering a tool for assessment and decision-making in biofuel production and agricultural waste management.
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Calorific value is among the most critical factors for assessing the quality of biofuels. Accurate estimation of biomass calorific value is essential for evaluating its suitability as a renewable energy source; however, conventional experimental determination is labor-intensive, costly, and time-consuming. This study developed machine learning (ML)-based predictive frameworks to estimate the calorific value of biomass using proximate and ultimate analyses across raw biomass, briquettes, and charcoal forms. A dataset comprising 600 biomass samples derived from 100 agricultural residues was compiled from published literature, incorporating raw, densified, and carbonized biomass materials. Four independent predictive frameworks were established using ultimate and proximate analyses. Multiple regression-based ML algorithms were comparatively evaluated. Model performance was assessed using root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R²). Among the evaluated algorithms, KNN and SVR achieved the best predictive performance, with R² values of 0.883 and 0.991 for ultimate-analysis-based briquette and charcoal prediction, respectively, and 0.879 and 0.997 for proximate-analysis-based prediction, with consistently low prediction errors. Comparative assessment with previously published empirical equations demonstrated competitive predictive capability of the developed models. The proximate-analysis-based framework significantly exhibited superior predictive performance and stability, achieving an accuracy of 96.5%, compared with 97% for the ultimate-analysis-based framework. Overall, the proposed machine learning frameworks provide a rapid, interpretable, and cost-effective approach for biomass calorific value estimation, supporting efficient biomass screening and sustainable waste-to-energy applications. These findings provide a reliable framework for calorific value prediction, supporting biofuel production and sustainable agricultural waste management.
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DOI: 10.1016/j.nxener.2026.100933
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