Modeling the Higher Heating Value of Spanish Biomass via Neural Networks and Analytical Equations

Anbarasan Jayapal, Fernando Ordonez Morales, Muhammad Ishtiaq, Se Yun Kim () and Nagireddy Gari Subba Reddy ()
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Anbarasan Jayapal: Department of Materials Engineering and Convergence Technology, Gyeongsang National University, Jinju 52828, Republic of Korea
Fernando Ordonez Morales: Department of Materials Engineering and Convergence Technology, Gyeongsang National University, Jinju 52828, Republic of Korea
Muhammad Ishtiaq: Department of Materials Engineering and Convergence Technology, Gyeongsang National University, Jinju 52828, Republic of Korea
Se Yun Kim: Department of Materials Engineering and Convergence Technology, Gyeongsang National University, Jinju 52828, Republic of Korea
Nagireddy Gari Subba Reddy: School of Materials Science and Engineering, Engineering Research Institute, Gyeongsang National University, Jinju 52828, Republic of Korea

Energies, 2025, vol. 18, issue 15, 1-22

Abstract: Accurate estimation of biomass higher heating value (HHV) is crucial for designing efficient bioenergy systems. In this study, we developed a Backpropagation artificial neural network (ANN) that predicts HHV from routine proximate/ultimate composition data. The network (9-6-6-1 architecture, trained for 15,000 epochs with learning rate 0.3 and momentum 0.4) was calibrated on 99 diverse Spanish biomass samples (inputs: moisture, ash, volatile matter, fixed carbon, C, H, O, N, S). The optimized ANN achieved strong predictive accuracy (validation R 2 ≈ 0.81; mean squared error ≈ 1.33 MJ/kg; MAE ≈ 0.77 MJ/kg), representing a substantial improvement over 54 analytical models despite the known complexity and variability of biomass composition. Importantly, in direct comparisons it significantly outperformed 54 published analytical HHV correlations—the ANN achieved substantially higher R 2 and lower prediction error than any fixed-form formula in the literature. A sensitivity analysis confirmed chemically intuitive trends (higher C/H/FC increase HHV; higher moisture/ash/O reduce it), indicating the model learned meaningful fuel-property relationships. The ANN thus provided a computationally efficient and robust tool for rapid, accurate HHV estimation from compositional data. Future work will expand the dataset, incorporate thermal pretreatment effects, and integrate the model into a user-friendly decision-support platform for bioenergy applications.

Keywords: artificial neural network; biomass; calorific value prediction; graphical user interface; higher heating value; proximate analysis; ultimate analysis; renewable fuels (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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