Minimum Ignition Energy (MIE) prediction models for ignition sensitive fuels using machine learning methods

Quantitative Structure-Property Relationship (QSPR) regression models for Minimum Ignition Energy (MIE) prediction of 60 flammable compounds have been developed using machine learning algorithms Random Forests (RF), Artificial Neural Networks (ANN) and a non-machine learning algorithm Genetic Function Approximation (GFA). RF algorithm was implemented for feature selection to identify the 13 molecular descriptors having maximum effect on MIE prediction accuracy (i.e. descriptors affecting MIE prediction accuracy > 1%). Thereafter, these descriptors were used as input parameters to develop the RF, ANN and GFA models. The optimized RF algorithm resulted in test set R2 of 0.85 and displayed high internal robustness and external predictability. The ANN and GFA algorithms displayed improved performance only on addition of an additional parameter Structure Parameter to the existing 13 descriptor set. The optimized ANN and the GFA models displayed a test set R2 of 0.79 and 0.71, respectively. The ANN model resulted in lower machine learning bias as compared to the RF model. The ANN model was observed to be more reliable than RF model for MIE prediction of small datasets. Based on this work, the RF algorithm for feature selection in QSPR modeling and the ANN, RF algorithms were observed to be promising options for MIE prediction applications.

Automated summary

The study successfully developed QSPR regression models for predicting MIE of 60 flammable compounds using machine learning algorithms and a non-machine learning algorithm. The results showed that the ANN model is more reliable for MIE prediction of small datasets with lower machine learning bias due to the addition of an extra parameter.