Experimental study of hydrogen enriched compressed natural gas (HCNG) engine and application of support vector machine (SVM) on prediction of engine performance at specific condition

Autor: Ma Fanhua, Zhibin Nie, Xiaohui Ren, Sijie Luo, Roopesh Kumar Mehra, Duan Hao
Rok vydání: 2020
Předmět:
Zdroj: International Journal of Hydrogen Energy. 45:5309-5325
ISSN: 0360-3199
DOI: 10.1016/j.ijhydene.2019.04.039
Popis: The effect of excess air ratio (λ) and ignition advance angle (θig) on the combustion and emission characteristics of hydrogen enriched compressed natural gas (HCNG) on a 6-cylinder compressed natural gas (CNG) engine has been experimental studied in an engine test bench, aiming at enriching the sophisticated calibration of HCNG fueled engine and increasing the prediction accuracy of the SVM method on automobile engines. Three different fuel blends were selected for the experiment: 0%, 20% and 40% volumetric hydrogen blend ratios. It is noted that combustion intensity varies with the excess air ratio and the ignition advance angle, so are the emissions. The optimal value of λ or θig has been explored in the specific engine condition. Results show that blending hydrogen can enhance and advance the combustion and stability of CNG engine, and it also has some benefic influence on the emissions such as reducing the CO and CH4. Meanwhile, a simulation research on forecasting the engine performance by using the support vector machine (SVM) method was conducted in detail. The torque, brake specific fuel consumption and NOx emission have been selected to characterize the power, economic and emissions of the engine with various HCNG fuels, respectively. It can be seen that the optimal model built by the SVM method can highly describe the relationship of the engine properties and condition parameters, since the value of the complex correlation coefficient is larger than 0.97. Secondly, the prediction performance of the optimal model for torque or BSFC is much better than the case of NOx. Besides, the optimal model built by the PSO optimization method has the best prediction accuracy, and the accuracy of the model obtained based on the training group with 20% hydrogen blend ratio is the best compared with those of others. The upshots in this article provide experimental support and theoretical basis for the adoption of HCNG fuel on internal combustion engines as well as the application of intelligent algorithmic in the engine calibration technology field.
Databáze: OpenAIRE
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