Laboratory method for investigating the influence of industrial process conditions on the emission of Polycyclic aromatic hydrocarbons from carbonaceous materials

This work is dedicated to developing a laboratory method for assessing emissions of polycyclic aromatic hydrocarbons (PAHs) from different carbon-based materials at elevated temperatures. The method will additionally contribute to enhancing the fundamental knowledge about the formation and decomposition of these compounds during various process conditions. Developing a method entails designing a setup for laboratory-scale experiments utilizing different furnace configurations and off-gas capturing media. To demonstrate the method's applicability, different carbon materials were tested under identical conditions, and analysis results for the same material in different furnace setups were compared. In this article, we have focused on the procedure for obtaining the “fingerprint” of PAH emissions under conditions characteristic of industrial processes.Two setups for investigation of the influence of temperature on PAH emissions were designed and tested for three types of carbon materials.The collected off-gas samples underwent analysis in two different laboratories to capture intra-laboratory differences and to evaluate the significance of the instrument detection limit.The results of PAH 16 (16 EPA PAH) and PAH 42 analysis were compared to showcase the influence of the expanded list on the overall emission of PAH.The novel methodology enables the determination and comparison of PAH emissions during the thermal treatment of individual carbon materials under laboratory conditions. This could potentially be a new approach for predicting the PAH emissions in metallurgical industries that use these carbon materials as reducing agents in their processes and their control by optimizing process parameters and raw materials used. In addition to being suitable for simulating various conditions in the metallurgical industry, the utilization of low-hazard PAH solvents makes it a promising method.