Abstract
This paper presents a method using a large steady-state engine operation data matrix to provide necessary information for successfully training a predictive network, while at the same time eliminating errors produced by the dispersive effects of the emissions measurement system. The steady-state training conditions of compound fuel allow for the correlation of time-averaged in-cylinder combustion variables to the engine-out NOx and HC emissions. The error back-propagation neural network (EBP) is then capable of learning the relationships between these variables and the measured gaseous emissions, and then interpolating between steady-state points in the matrix. This method for NOx method for NOx and HC has been proved highly successful.
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Shi-chuan, S., Zhao-da, Y., Guang-jie, Y. et al. A method for predicting in-cylinder compound combustion emissions. J. Zhejiang Univ. Sci. A 3, 543–548 (2002). https://doi.org/10.1631/jzus.2002.0543
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DOI: https://doi.org/10.1631/jzus.2002.0543