Development and validation of new methods for identification of bio-char as an alternative solid bio-fuel for power generation

- Developed new method for verifying the biomass origin of second generation fuels
- BFC-PowerGen delivers a classification accuracy of at least 96%.
- BFC-PowerGen provides a fast and accurate determination of the origin of the fuel.
- Two methods are compared to qualitatively confirm the origin of the materials.

The aim of this study was to develop and validate a new rapid method for the qualitative analysis of solid biofuels obtained from a thermal treatment process. A new method for the qualitative confirmation of the second-generation solid bio-fuel origin was compared and assessed. The new method was compared with a method based on the concentration of 14C carbon isotope in the studied material obtained from thermal conversion of a biomass. The developed method is intended to analyse the origin of the second-generation solid bio-fuels and is based on basic analytical fuel properties commonly measured in most laboratories. The process couples chemometric methods with proximate and ultimate analyses, heat of combustion and chemical composition of ash from second-generation solid bio-fuels.In light of the current European regulations, the second-generation bio-fuels obtained by the thermal conversion of biomass, e.g., bio-chars or torrefied biomass, are not classified as biomass. Consequently, the energy generated by these biofuels is not classified as energy generated from a renewable energy source. The currently available method, i.e., analysis of the 14C content in samples of interest, might not be sufficiently sensitive to characterise materials from the co-pyrolysis of biomass and non-biodegradable material. Prompted by this situation, a new method for the qualitative confirmation of the second-generation solid bio-fuel origin was proposed. The BioFuel Classifier for Power Generation (BFC-PowerGen) provides a rapid and accurate determination of the origin of a fuel and can be used for controlling the quality of fuel derived from the thermal conversion of biomass. This new classifier, which is based on a classification and regression tree model, delivers a classification accuracy of at least 96%.