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Application of Py-GC-MS for monitoring polymer waste in commercially available solid biofuels
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DOI：10.1016/j.biombioe.2025.107907.png)
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Abstract
En 中文
Biomass is the primary renewable solid fuel used for domestic heating, district heating networks and distributed energy generation. However, the contamination of biomass fuels with plastic waste poses a serious environmental and health risk, as combustion systems designed for clean biomass are unable to reduce harmful emissions. Model samples containing 0.1-10.0 % by weight of polymer mixtures were analysed to identify volatile organic markers and to evaluate the interaction between biomass and polymers during pyrolysis. The volatile organic components were identified and the interactions between the biomass matrix and the polymer matrix during thermal conversion of solid samples were examined by pyrolysis-gas chromatography-mass-spectrometry. Chemometric methods such as principal component analysis and hierarchical cluster analysis were used to sort the samples into groups and determine their degree of contamination. The study showed that pyrolysis-gas chromatography-mass-spectrometry can accurately detect polymer markers such as styrene, alpha-methylstyrene and phthalic acid, even if the contamination is only 0.1 % by weight. A machine learning classification model using gradient boosted decision trees achieved an F1 score (harmonic mean of precision, recall) of 98 %, with 97 % accuracy for pure biomass and 100 % accuracy for contaminated samples. The study proved that pyrolysis-gas-chromatography-mass-spectrometry can be used as a fast and accurate detection way of polymeric contaminants in solid biofuels, even though no certified reference materials were available, and the interactions were complex. This research highlights the potential of pyrolysis-gas-chromatography-mass-spectrometry as a tool for monitoring biofuel purity, filling gaps in current regulatory systems and ensuring the environmental safety of solid biofuels.
Keywords:
Plastic waste
Py-GC-MS
Comparison analysis
Volatile organic compounds
Chemometric analysis
Machine learning
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