Prospects for Low-Resolution NDIR Sensors to Discern Ignition Properties of Fuels

Abstract

The cetane number (CN) is an important fuel property to consider for compression ignition engines as it is a measure of a fuel's ignition delay. Derived cetane number (DCN) already varies significantly within jet fuels. With the expected increasing prevalence of alternative jet fuels, additional variability is expected. DCN is usually assigned to fuels using ASTM methods that use large equipment like the ignition quality tester (IQT), which consumes a lot of fuel and is cumbersome to operate. Over the last decade, there have been advances in the development of chemometric models, which use machine learning to correlate infrared spectra of fuels to fuel properties like DCN, density, and C/H ratio, among many others. These techniques have certain advantages over the ASTM methods, and previous studies performed on samples of diesel fuels have shown high accuracies in DCN prediction. However, this accuracy is generally a result of high resolution, making the equipment expensive, relatively large for handheld sensors, and power-hungry. On the other hand, nondispersive infrared (NDIR) sensors, despite having a low resolution, are attractive because they can be compact, inexpensive, and power efficient. These characteristics are important for handheld or onboard fuel sensors. However, one would anticipate a tradeoff between these advantages and accuracy. This study investigates this tradeoff and the feasibility of low-resolution NDIR sensors to discern fuel properties such as DCN by using Machine Learning models trained on real FTIR data, and DCNs obtained from IQT. DCN predictions were made for blends of ATJ/F-24, CN fuels, and neat Jet A1, A2, and JP 5, with an error limit of 10%. It was found that there seems to be sufficient variability in the near infrared (NIR) range to discern DCN with a feasible number of channels, but the channels have to be narrow (e.g., FWHMs as narrow as 60 nm). For the dataset in the study, the performance of linear models was better than the nonlinear model. Finally, NIR region beyond 1050 nm was found to be more important in DCN prediction, primarily the regions consisting of the first and second C-H overtones and the C-H combination band.