Characteristic time scale as optimal input in Machine Learning algorithms: Homogeneous autoignition

Abstract

Considering temporally evolving processes, the search for optimal input selection in Machine Learning (ML) algorithms is extended here beyond (i) the readily available independent variables defining the process and (ii) the dependent variables suggested by feature extraction methods, by considering the time scale that characterizes the process. The analysis is based on the process of homogeneous autoignition, which is fully determined by the initial temperature T(0) and pressure p(0) of the mixture and the equivalence ratio ϕ that specifies the initial mixture composition. The aim is to seek the optimal input for the prediction of the time at which the mixture ignites. The Multilayer Perceptron (MLP) and Principal Component Analysis (PCA) algorithms are employed for prediction and feature extraction, respectively. It is demonstrated that the time scale that characterizes the initiation of the process τe(0), provides much better accuracy as input to MLP than any pair of the three independent parameters T(0), p(0) and ϕ or their two principal components. Indicatively, it is shown that using τe(0) as input results in a coefficient of determination R2 in the range of 0.953 to 0.982, while the maximum value of R2 when using the independent parameters or principal components is 0.660. The physical grounds, on which the success of τe(0) is based, are discussed. The results suggest the need for further research in order to develop selection methodologies of optimal inputs among those that characterize the process.