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

Energy and Ai 2023年第4期14卷 238-252页

作者： mohammed i.radaideh Stelios Rigopoulos Dimitris A.Goussis Department of Mechanical Engineering Khalifa University of Science and TechnologyAbu Dhabi 127788United Arab Emirates imperial College London Mechanical Engineering South Kensington Campus London SW72AZUnited Kingdom Research and innovation Center on CO_(2)and H_(2) Khalifa University of Science and TechnologyAbu Dhabip127788United Arab Emirates

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 *** 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 ratio𝜙that specifies the initial mixture *** aim is to seek the optimal input for the prediction of the time at which the mixture *** Multilayer Perceptron(MLP)and Principal Component Analysis(PCA)algorithms are employed for prediction and feature extraction,*** is demonstrated that the time scale that characterizes the initiation of the process𝜐𝑓(0),provides much better accuracy as input to MLP than any pair of the three independent parameters T(0),p(0)and𝜙or their two principal ***,it is shown that using𝜐𝑓(0)as input results in a coefficient of determination R 2 in the range of 0.953 to 0.982,while the maximum value of R 2 when using the independent parameters or principal components is *** physical grounds,on which the success of𝜐𝑓(0)is based,are *** results suggest the need for further research in order to develop selection methodologies of optimal inputs among those that characterize the process.

关键词： Machine Learning Feature selection Autoignition

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