Dimension Reduction of Chemical Process Simulation Data

In the analysis of combustion processes, simulation is a costefficient tool that complements experimental testing. The simulation models must be precise if subtle differences are to be detected. On the other hand, computational evaluation of precise models typically requires substantial effort. To escape the computational bottleneck, reduced chemical schemes, for example, ILDM-based methods or the flamelet approach, have been developed that result in substantially reduced computational effort and memory requirements. This paper proposes an additional analysis tool based on the Machine Learning concepts of Subgroup Discovery and Lazy Learning. Its goal is compact representation of chemical processes using few variables. Efficacy is demonstrated for simulation data of a laminar methane/air combustion process described by 29 chemical species, 3 thermodynamic properties (pressure, temperature, enthalpy), and 2 velocity components. From these data, the reduction method derives a reduced set of 3 variables from which the other 31 variables are estimated with good accuracy.