Modelling process generated nanoparticles in industrial workplaces using reduced order models

Abstract

Process-generated nanoparticles (PGNP) are nanomaterials unintentionally released to workplace environments during high-energy processes such as burning fuels, plasma cutting, welding, metal grinding and ceramic tile firing. Currently, there is an absence of risk assessment tools for PGNP exposure assessment in industrial settings. Then, it is not possible to assess the exposure of workers to this kind of pollutants. In this context, there is a need to develop models to simulate the PGNP concentration in indoor spaces. Due to the small size of the PGNP (<100 nm) their behaviour is comparable to ideal gas molecules. Then, reduced order models used to simulate CO2 concertation in indoor spaces could be used to simulate PGNP concentrations in this kind of environments. The objective of this paper is to assess the potential of creating predictive models for a specific room using reduced order models to estimate future PGNP concentrations. First of all, a set of differential mass balance equations are defined. Then, the model parameters are estimated according a literature review. Finally, the model is validated using experimental data from an industrial location.