Artificial Neural Network Modeling and Numerical Simulation of Syngas Fuel and Injection Timing Effects on the Performance and Emissions of a Heavy-Duty Compression Ignition Engine

Abstract

The use of syngas as an alternative fuel in compression ignition engines is a potential way to curb the emission of pollutants and optimize the performance of these engines. The present research studied the effect of changing fuel injection timing and adding syngas on the output of a heavy-duty diesel engine. The optimal mode of the turbulence model, fuel spray model, combustion model, and pollutant emission model was used to solve computational fluid dynamics. Changing fuel injection timing from 70 to 10° before top dead center (BTDC) and diesel variants, including conventional diesel, diesel + 20% syngas, and diesel + 40% syngas, is the strategy studied here. It was found that the use of syngas could reduce emissions significantly. The in-cylinder mean effective pressure was the highest for diesel + 20% syngas. On the other hand, increasing the rate of syngas and retarding injection timing reduced the ignition period and in-cylinder temperature. The lowest rate of CO emission was obtained from diesel + 40% syngas at a fuel injection timing of 70° BTDC, whereas the lowest particulate matter emission was related to diesel + 40% syngas at 40° BTDC injection timing. The lowest rate of CO2 emission was related to diesel + 40% syngas at a fuel injection timing of 10° BTDC, while the same timing for conventional diesel exhibited the lowest NO X emission rate. Finally, the performance parameters of the engine including indicated power, indicated fuel consumption, and indicated thermal efficiency were decreased with the increase in syngas fraction, so that their highest values were obtained from the conventional diesel at the injection timing of 40° BTDC. In addition, an artificial neural network (ANN) model based on a standard back-propagation learning algorithm was developed for modeling the performance and emissions of the engine. The results for the optimum ANN model showed that the optimal ANN has two hidden layers with 20–25 neurons and the transfer function of logsig–logsig for hidden layers 1 and 2, respectively, and can predict different parameters of the engine for different modes. The correlation coefficients (R-value) of optimal topology for training, validation, and testing are 0.99992, 0.96612, and 0.93424, respectively. The results for the optimum ANN model showed that the constructed model sufficiently predicts the performance and emissions of the CI diesel engine.

A misfire-integrated Gaussian process (MInt-GP) emulator for energy-assisted compression ignition (EACI) engines with varying cetane number jet fuels

“…Machine Learning (ML)-based surrogate modeling of internal combustion engines (ICE) has been widely used for a broad range of applications. 25–29 Data-driven ML approaches, in particular, are popular for building ICE surrogate models; such approaches include neural networks (NN), 30–45 Support Vector Machines (SVM), 46–49 Gaussian Processes (GPs, 50–60 also known as kriging 61 ), and other learning models. 62–68 In surrogate modeling applications with limited training runs from expensive simulators, GPs (and its recent non-stationary extensions) have several key advantages over alternate deep learning models.…”