22: Functional Hierarchical Model for Smoke Combustion Data

Music City Center 

Cancer and other chronic diseases associated with exposure to fireground smoke and contaminated Personal Protective Equipment (PPE) have become long-term health concerns for firefighters. A controlled laboratory smoke combustion simulation with high consistency and reproducibility can provide detailed records of smoke contaminants for studying the nature of fireground smoke and guiding the improvement of PPE safety in the future. In a lab-controlled environment, nine representative groups of smoke combustion experiment records, including Smoke Particle Size Distribution (SPC) and combustion Heat Release Rate (HRR), are collected to study the patterns of different smokes. We constructed a hierarchical model framework that integrates Beta Regression models for shifting registration, functional principal component analysis (FPCA) for functional data representation, and mixture models with temporal dependence for calibrating the smoke distribution during phase transition to identify and analyze the effects of heat and fuel materials in the combustion process. Additionally, we used Bootstrap methods to assess the model uncertainty and the quality of the synthetic smoke profiles. We use our proposed model to make inferences on the primary factors in combustion experiments, suggesting that different combustion conditions lead to distinct mechanisms within both the ignition and flaming phases, providing insights into the interaction between heat and fuel materials during the combustion process.

Functional data analysis

Lagged functional model

Bootstrap 

Section on Physical and Engineering Sciences