Predicting the Engine Performance and Exhaust Emissions of a Diesel Engine Fueled With Hazelnut Oil Methyl Ester: The Performance Comparison of Response Surface Methodology and LSSVM

Abstract

An experimental investigation was conducted to evaluate the suitability of hazelnut oil methyl ester (HOME) for engine performance and exhaust emissions responses of a turbocharged direct injection (TDI) diesel engine. HOME was tested at full load with various engine speeds by changing fuel injection timing (12, 15, and 18 deg CA) in a TDI diesel engine. Response surface methodology (RSM) and least-squares support vector machine (LSSVM) were used for modeling the relations between the engine performance and exhaust emission parameters, which are the measured responses and factors such as fuel injection timing (t) and engine speed (n) parameters as the controllable input variables. For this purpose, RSM and LSSVM models from experimental results were constructed for each response, namely, brake power, brake-specific fuel consumption (BSFC), brake thermal efficiency (BTE), exhaust gas temperature (EGT), oxides of nitrogen (NOx), carbon dioxide (CO2), carbon monoxide (CO), and smoke opacity (N), which are affected by the factors t and n. The results of RSM and LSSVM were compared with the observed experimental results. These results showed that RSM and LSSVM were effective modeling methods with high accuracy for these types of cases. Also, the prediction performance of LSSVM was slightly better than that of RSM.

Artificial intelligence‐based model prediction of biodiesel‐fueled engine performance and emission characteristics: A comparative evaluation of gene expression programming and artificial neural network

“…In another study, Atmanli et al 18 used RSM successfully to optimize the blending ratio of butanol, diesel, and cotton oil for the best engine output performance. A comparative study between RSM and least‐squares support vector machine (LSSVM) for optimization and predictive modeling of HOME‐powered turbocharged diesel engine was carried out by Yilmaz et al 19 Both RSM and LSSVM exhibited excellent prognostic capability, while LSSVM provided slightly better results than RSM.…”

Analysis and Optimization Study of Piston in Diesel Engine Based on ABC-OED-FE Method

“…e results indicate that large cleavage piston and the bigger compression ratio can improve the performance of the engine. ere are also some researches focusing on the dynamic modelling and parameters optimization of piston [22], effects of different piston bowl [23], multiobjective optimization of piston using response surface methodology [24], and so on [25][26][27].…”

Deep kernel learning approach to engine emissions modeling

“…Lughofer et al (2011) investigated the modeling of NO x emissions of a diesel engine using a fuzzy model directly from measurement data, which is then shown to be a good alternative to physics-based models. Yilmaz et al (2016) compared the response surface methodology (RSM), a commonly used surrogate model, with least-squared support vector machine (LSSVM) based on their performance in predicting the performance and exhaust emissions of a diesel engine fueled with hazelnut oil, and showed that LSSVM narrowly outperforms RSM. Ghobadian et al (2009) studied the application of a multilayer perceptron (MLP) to predicting exhaust emissions of a diesel engine using waste cooking biodiesel fuel and showed that MLP performs quite well in emissions prediction.…”