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Artificial Neural Network Model for Predicting Exhaust Temperature of an Ethanol-Fueled HCCI Engine
Published May 23, 2012 by Society of Automotive Engineers of Japan in Japan
The Homogeneous Charge Compression Ignition (HCCI) running on ethanol as a substituted fuel is one of the possible ways to improve performance, fuel efficiency and emission. In this preliminary investigation, a modified single-cylinder Yanmar diesel engine was used as a reference platform in conjunction with a fuel port injection unit and tried a several settings. The exhaust temperature characteristics and combustion characteristics of the converted engine were investigated. Artificial neural network (ANN) model was developed for predicting exhaust temperature. It observed that ANN model can predict engine exhaust temperature with correlation coefficient equal .96. This study shows ANN model is an accurate model for predicting HCCI engine exhaust temperature, which has a bearing on exhaust emission.