Prediction of the performance and exhaust emissions of a compression ignition engine using a wavelet neural network with a stochastic gradient algorithm

R. Rahimi molkdaragh, S. Jafarmadar, Sh Khalilaria and H. Soukht Saraee

Energy, 2018, vol. 142, issue C, 1128-1138

Abstract: The purpose of this research is to use a wavelet neural network (WNN) and stochastic gradient algorithm (SGA) to predict the performance and exhaust emissions of a compression ignition engine with nanoparticles-diesel fuel. The percentage of the additive of nanoparticles to the fuel ranges between 20 and 80 ppm. A model of WNN has been applied in order to predict the relationship between the power, fuel consumption (FC), specific fuel consumption (SFC), CO, NOx, and HC with the amount of nanoparticles at different speeds. The input variables are of two parameters (the percentage of nanoparticles and engine speed), while the output variables are of six parameters (power, FC, SFC, CO, NOx, and HC). In this work, considering the characteristics of the utilized wavelet function and application of the SGA method, satisfactory results were obtained in prediction of exhaust emissions and performance of the target engine. In addition, two common artificial neural networks (ANNs) (back propagation (BP) and non-linear autoregressive with exogenous input (NARX)) were used in predicting the performance of internal combustion engines compared with WNN results. Therefore, evaluation results of these three networks showed that the WNN with the SGA are very accurate and useful method to perform the prediction and model nonlinear phenomena of internal combustion engines.

Keywords: WNN; BPNN; NARXNN; Stochastic gradient algorithm; Compression ignition engine; Performance; Emissions (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:142:y:2018:i:c:p:1128-1138

DOI: 10.1016/j.energy.2017.09.006

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