Application of GRNN for the prediction of performance and exhaust emissions in HCCI engine using ethanol

Highlights: • First GRNN model to predict performance and emission parameters in HCCI engine. • At 170 °C, a maximum of 43% thermal efficiency is found for ethanol HCCI engine. • Negligible NO_X and smoke are found for port injected ethanol HCCI operation. • A maximum of 2% error in the GRNN prediction shows promising for HCCI engines. - Abstract: The evaluation of the performance and emission parameters of an ethanol-fueled HCCI engine is a challenging one, because of non-linear nature of the parameters. In this investigation, a smart prediction tool was developed for measuring the performance, and emission characteristics of the ethanol-fueled HCCI engine. For this purpose, a study was conducted through a combination of experimental data analysis and generalized regression neural network (GRNN) modeling. This study used a set of experimental data obtained from the ethanol HCCI engine to characterize variations in performance measures such as brake thermal efficiency, and exhaust gas temperature, and the emission parameters such as unburned hydrocarbons (UHC), carbon monoxide (CO), nitric oxide (NO), and smoke opacity. The neural network was trained, validated and tested with the experimental data sets. In addition, grid search method was used to find the optimized kernel bandwidth to reduce the cross-validation error. The obtained results were validated and tested with the experimental HCCI performance and emission metrics. The validation results predicted that the output parameters those lie within 2% error. The results also showed that the GRNN models are advantageous for network simplicity and require less sparse data.