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- W4229074861 abstract "Methods for predicting autoignition temperatures (AIT) have been historically inaccurate and are rarely based on the underlying physical phenomena leading to observed AIT. Previous attempts at predicting AIT have been developed based on Quantitative Structure–Property Relationships (QSPR) and additive group contribution methods. This work presents an improved method for predicting AIT based on the method originally developed by the late Dr. William H. Seaton. The method of Seaton is described in detail including its use of first principles to predict AIT. A comprehensively evaluated data set is used for a new regression of Seaton method parameters. Improvements on the model of Seaton and underlying principles are presented and discussed. Finally, an improved AIT prediction model and parameter sets based on Seaton’s method are recommended. • Evaluated autoignition temperature (AIT) data are recommended. • A first-principles AIT prediction method by Dr. William H. Seaton is presented. • The Seaton Method is improved by relaxing assumptions. • New autoignition models can provide insight into the mechanisms that control AIT." @default.
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- W4229074861 date "2022-09-01" @default.
- W4229074861 modified "2023-10-16" @default.
- W4229074861 title "An improved method for predicting autoignition temperatures based on first principles" @default.
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- W4229074861 doi "https://doi.org/10.1016/j.fuel.2022.124245" @default.
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