Graduate Thesis Or Dissertation


Chemometrics-based Approach for Predicting Low Temperature Combustion Engine Fuel Performance Public Deposited

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  • Low temperature combustion (LTC) engines offer improved efficiency and reduced emissions over traditional gasoline spark-ignited and diesel compression-ignition engines. The ability to produce fuels optimized for LTC engines could help increase efficiency and therefore improve fuel economy, insomuch that Federal fuel economy standards could be met. Currently, a fuel performance rating that can indicate how a fuel will perform in LTC engines has not been well established; this is needed to aid in fuel design/optimization. Furthermore, traditional fuel performance metrics, e.g., Research Octane Number (RON), may not fully explain LTC performance and require expensive, time-consuming experiments. In this work, the LTC index fuel performance metric, which has been recently introduced, was chosen to represent a quantitative measure for LTC fuel performance. This fuel performance metric rates fuels by their potential fuel savings for useful real world operating conditions. In a parallel effort, statistical models based on chemometric techniques were developed to correlate liquid hydrocarbon infrared absorption spectra first to RON for validation and insight into the approach, and finally to the LTC index. Here we show that the developed chemometric models can predict the performance of the FACE gasolines (research grade gasoline-like fuels) for RON(±10for all 22 FACE) and the LTC index (±0.4for FACE A and C). To the author's knowledge, these models are novel in that actual gasoline samples are not used to inform the chemometric model; instead, only neat hydrocarbon components are used (individually or up to five component mixtures). The chemometric models could aid researchers and industry alike as an easy, cost-effective, and fast tool to determine LTC performance of gasoline fuels and help in an effort to design next-generation fuels.
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