Monitoring odorous sulfur emissions using self-organizing maps for handling ion mobility spectrometry data

Possibilities for monitoring emissions of reduced sulfur compounds in pulp and paper mills were investigated using ion mobility spectrometry (IMS) and a self-organizing map (SOM) algorithm. The reduced sulfur compounds measured were hydrogen sulfide (H2S), dimethyl sulfide (DMS), and methyl mercaptan (MM). Attention was paid to momentary concentrations because there is no monitoring device able to measure peak concentrations of reduced sulfur compounds under field conditions. These methods were evaluated by measuring the reduced sulfur compounds first in the laboratory and then at a process monitoring site at a pulp factory. The aim was to find out whether it would be possible to use the laboratory measurements to recognize the same reduced sulfur compounds at the monitoring site. Data collection was followed by analysis using the SOM algorithm and Sammon's mapping. The results showed that the IMS spectra of reduced sulfur compounds and their mixtures can be distinguished from each other by computationally intelligent methods and that the spectra from the process monitoring site corresponded with the laboratory measurements to a certain extent.