Sampling to analysis: simultaneous quantification of siloxanes and sulfur compounds in biogas for cleaner energy

Abstract

The use of biogas as a renewable energy source is expanding rapidly, propelled by increasingly stringent climate policies that reduce fossil-fuel reliance and greenhouse gas emissions. However, trace impurities, particularly siloxanes and sulfur compounds, pose significant challenges to biogas utilization in energy systems. While regulatory standards like EN 16723 set strict limits on these impurities, the absence of standardized, validated methods traceable to reference standards complicates compliance, especially for small- and medium-scale biogas plants without advanced analytical capabilities. This study introduces an accessible method for the simultaneous quantification of siloxanes and condensable sulfur compounds in biogas, utilizing gas chromatography coupled with inductively coupled plasma mass spectrometry (GC-ICP-MS). A liquid quench sampling system (LQ) is employed to preconcentrate and store analytes, enabling biogas plants without specialized analytical tools to collect samples for centralized analysis. By consolidating sulfur and siloxane measurements into a single procedure, the method streamlines the process, reducing both time and complexity compared to conventional approaches. Validated across multiple biogas sources, including digesters and wood gasifiers, this methodolgy improves the understanding of impurity variations due to feedstock, seasonality, and operational factors. By streamlining compliance and plant optimization, the method supports policy goals for renewable gas, while helping valorize biowaste through reliable biomethane production, thereby advancing a circular, low-carbon economy

The authors acknowledge the financial support by the Swiss National Foundation (project 184817). Financial support was also obtained from the Energy Systems Integration (ESI) Platform at PSI. LT’s salary was partially funded by the SynFuels-PSI-EMPA joint initiative, Competence Center for Product Stream Analytics, Grant (320022)

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