Experimental investigation and artificial neural network-based modelling of thermal barrier engine performance and exhaust emissions for methanol-gasoline blends

In recent years, due to environmental concerns and the depletion of fossil fuels, alternative fuel use and alternative emission reduction methods have gained importance in the automotive industry. In addition, methanol is used as an alternative fuel in gasoline engines with coated piston engines. This study first presents an experimental investigation of engine performance and exhaust emissions for a partially thermal barrier lined piston engine operating on methanol-gasoline blends. In the second phase the obtained data is then used to develop an Artificial Neural Network (ANN) based model to predict engine performance and exhaust emissions for methanol-gasoline blends. The developed ANN model was trained and validated using MATLAB. The results of the experimental study showed that the use of methanol-gasoline blended fuel in the engine provides better engine performance and reduced exhaust emissions compared to gasoline fuel. According to the results obtained, an increase of 3.7 % in effective power and a decrease in NOx and HC emissions by 19 % and 18 %, respectively, compared to the STD case when both coating and alternative fuel are used in the engine. With the established ANN models, engine performance parameters and exhaust emission parameters were predicted with 99 % and 98 % accuracy respectively.