Higher Heating Value Estimation of Biomass Fuels
Summary
Higher heating value (HHV) is a fundamental parameter for assessing the energy potential of biomass feedstocks and designing combustion, gasification and pyrolysis processes. Conventionally determined using an adiabatic oxygen bomb calorimeter, HHV measurement is accurate but time-consuming and costly. Over recent decades, indirect estimation methods have proliferated, ranging from empirical correlations based on proximate and ultimate analyses to advanced soft-computing techniques such as artificial neural networks (ANNs) and adaptive neuro-fuzzy inference systems. These approaches exploit relationships between moisture, volatile matter, fixed carbon and ash contents, together with elemental composition (carbon, hydrogen, oxygen, nitrogen, sulphur), to predict calorific values. Torrefaction, hydrothermal carbonisation and heat treatment further complicate estimation by altering biomass composition and structural properties. Accurate HHV prediction supports efficient reactor design, optimises fuel blending and underpins life-cycle assessments of bioenergy systems. As nations strive for carbon neutrality, reliable, rapid and low-cost HHV estimation methods are crucial for unlocking the full potential of agricultural residues, energy crops and forestry by-products in a circular bioeconomy.
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Higher Heating Value Estimation of Biomass Fuels publication trend
The graph below shows the total number of articles in higher heating value estimation of biomass fuels across all publications each year (not limited to Nature Index journals).
Technical terms
Higher Heating Value (HHV): The total heat released per unit mass of fuel when combusted, including latent heat of condensation of water vapour in exhaust gases.
Proximate analysis: A series of tests quantifying moisture, volatile matter, fixed carbon and ash content to characterise the thermal behaviour of solid fuels.
Ultimate analysis: Determination of elemental composition, typically carbon, hydrogen, oxygen, nitrogen and sulphur, providing fundamental data for combustion and gasification modelling.
Artificial Neural Network (ANN): A computational model inspired by biological neural systems, used here to learn complex nonlinear relationships between biomass compositional data and HHV.
Thermogravimetric analysis (TGA): A technique measuring mass changes of a sample under controlled temperature programmes, employed for rapid proximate analysis.
References
- Predictions of elemental composition of coal and biomass from their proximate analyses using ANFIS, ANN and MLR. International Journal of Coal Science & Technology (2020).
- Predictability of higher heating value of biomass feedstocks via proximate and ultimate analyses – A comprehensive study of artificial neural network applications. Fuel (2022).
- Influence of Chemical Composition on Heating Value of Biomass: A Review and Bibliometric Analysis. Energies (2023).
- Thermogravimetric analysis-based proximate analysis of agro-byproducts and prediction of calorific value. Energy Reports (2022).
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