Machine learning methods to develop predictive models for estimation of exhaust gas properties from internal combustion engines during cold starts from largescale real-world experimental data
| Name | Role at KCDS |
|---|---|
| Frank, Martin | Scientific Speaker, MATH Supervisor |
| Trimis, Dimosthenis | SEE Supervisor |
Abstract
The objective of the project is to apply machine learning (ML) methods to this inexhaustible amount of data to develop models capable of predicting the exhaust gas composition including particulate matter concentrations and nanostructural/molecular properties during variable non-stationary cold start conditions. The predictive models will be used to develop optimal control strategies and algorithms for low pollutant emissions at different cold start conditions and contribute to clean urban environments. The experimental data having engine performance parameters, detailed multi-component composition of the exhaust gas as well as corresponding properties of nanometer-sized particles compiled from electron microscopic images. Novel approach need to be developed incorperating both continuous experimental data and nanometerized particle soot images.