Prediction of emission and performance characteristics of reactivity-controlled compression ignition engine with the intelligent software based on adaptive neural-fuzzy and neural-network

Abstract

Abstract A fuel reactivity-controlled compression ignition concept promises to overcome increasingly stringent emission standards with ultra-low nitrogen oxide and soot emissions and very high fuel efficiency. In the present work, it was developed an intelligent software-based on adaptive neural-fuzzy inference systems in order to predict the emission and performance values of reactivity-controlled compression ignition engine fueled with natural gas and diesel under different operating conditions through an experimentally validated computational fluid dynamics model with the four cases generated in the study for intelligent software-based on neural-fuzzy systems simulation considering different input and output parameters. The engine model was run at input variables as initial pressure (1.67–2.79\xa0bar), total fuel mass (60–130\xa0mg), exhaust gas recirculation ratio (0–20%), the start of injection (32–56° before top dead center) and total 180 different conditions were generated while output responses such as indicated power, maximum in-cylinder pressure, soot, and nitrogen oxide emissions. The main operating parameters of the reactivity-controlled compression ignition engine were determined for different load conditions, enabling the engine to operate in a wide range with high efficiency and low emission. For the neural-fuzzy inference model presented study, the different number Gaussian curve membership functions (5, 6, 7, 8) (gaussmf) have been used considering different adaptive neural-fuzzy cases, and generally, about 13500 training cycles is found to be optimum neural-fuzzy inference parameters and minimum error value. Mean square error, mean error percentage, and the absolute fraction of variance (R2) were used to assess and compare the performance of the adaptive neural-fuzzy and artificial neural network. Results of the adaptive neural-fuzzy confirm that the model successfully predicts the performance and emission of the engine with the R2 value % 99.8, %99.9, and %96.5 for the output parameters indicated power, maximum in-cylinder pressure, and nitrogen oxide, respectively. Moreover, the performance of the neural-fuzzy inference algorithm compared with artificial neural network and consequently, it was observed that neural-fuzzy inference gives more accurate predict value according to the artificial neural network. Moreover, generating a neural-fuzzy inference architecture using the proposed mathematical foundation given the study shows that neural-fuzzy inference is a very effective and helpful technique to predict the reactivity-controlled compression ignition engine performance and emission parameters.