Toward AI-Assisted Interpretation of Total Volatile Organic Compound Signals from Combustion Processes: Exploratory Machine Learning and Clustering-Based Pseudo-Speciation for Sustainable Emission Monitoring

Volatile organic compounds (VOCs) emitted during solid-fuel combustion contribute to air pollution, secondary organic aerosol formation, and adverse environmental impacts. Improving the interpretation of VOC emissions is therefore important for developing more sustainable combustion systems and emission-monitoring strategies. Although online flame ionization detector systems enable continuous monitoring of total volatile organic compounds (TVOCs), the resulting measurements remain chemically non-specific and provide limited information about the composition of emitted mixtures. This study investigates whether data-driven approaches can improve the interpretation of TVOC signals generated during controlled solid-fuel combustion and proposes a descriptor-space-based pseudo-speciation framework. Continuous laboratory measurements of TVOCs and combustion parameters demonstrated that the integrated TVOC signal contains meaningful information about combustion dynamics, while preliminary machine-learning models confirmed that a substantial fraction of TVOC variability can be explained using routinely monitored process variables. To address the limited chemical specificity of TVOC measurements, principal component analysis and hierarchical clustering were applied to combustion-related VOCs described by molecular and physicochemical descriptors. The resulting framework organized VOCs into representative physicochemical groups, providing an intermediate interpretation layer between bulk TVOC measurements and compound-specific analysis. The proposed methodology demonstrates how artificial intelligence and chemoinformatics can enhance the interpretation of chemically non-specific TVOC signals and support more sustainable emission monitoring, combustion diagnostics, and environmental management.