Abstract
The performance analysis of a single cylinder spark ignition engine fuelled with ethanol – petrol blends were carried out successfully at constant load conditions. E0 (Petrol), E10 (10% Ethanol, 90% Petrol), E20 (20% Ethanol, 80% Petrol) and E30 (30% Ethanol, 70% Petrol) were used as fuel. The Engine speed, mass flow rate, combustion efficiency, maximum pressure developed, brake specific fuel consumption and Exhaust gas temperature values were measured during the experiment. Using the experimental data, a Levenberg Marquardt Artificial Neural Network algorithm and Logistic sigmoid activation transfer function with a 4–10–2 model was developed to predict the brake specific fuel consumption, maximum pressure and combustion efficiency of G200 IMEX spark ignition engine using the recorded engine speed, mass flow rate, biofuels ratio and exhaust gas temperature as input variables. The performance of the Artificial Neural Network was validated by comparing the predicted data with the experimental results. The results showed that the training algorithm of Levenberg Marquardt was sufficient enough in predicting the brake specific fuel consumption, combustion pressure and combustion efficiency of the test engine. Correlation coefficient values of 0.974, 0.996 and 0.995 were obtained for brake specific fuel consumption, combustion efficiency and pressure respectively. These correlation coefficient obtained for the output parameters are very close to one (1) showing good correlation between the Artificial Neural Network predicted results and the experimental data while the Mean Square Errors were found to be very low (0.00018825 @ epoch 10 for brake specific fuel consumption, 1.0023 @ epoch 3 for combustion efficiency and 0.0013284@ epoch 5 for in-cylinder pressure). Therefore, Artificial Neural Network toolbox called up from MATLAB proved to be a useful tool for simulation of engine parameters. Artificial Neural Network model provided accurate analysis of these complex problems and has been found to be very useful for predicting the performance of the spark ignition engine. Thus, this has proved that Artificial Neural Network model could be used for predicting performance values in internal combustion engines, in this way it would be possible to conduct time and cost efficient studies instead of long experimental ones.
Highlights
The ever rising cost of fossil fuel internationally has forced major world economies, which are major importers of fossil fuel, to consider renewable and cheaper alternatives to fossil fuel to compliment their energy demands
The results showed that the artificial neural network (ANN) has the capability of generalizing between engine speeds, mass flow rate, biofuels mixtures and exhaust gas temperatures of input variables and combustion efficiency and combustion pressure of output variables reasonably well
The performance analysis of a single cylinder spark ignition engine fuelled with ethanol – petrol blends were carried out successfully at constant load conditions
Summary
The ever rising cost of fossil fuel internationally has forced major world economies, which are major importers of fossil fuel, to consider renewable and cheaper alternatives to fossil fuel to compliment their energy demands. The limited nature of oil resources has made studies on alternative energy sources much more important in internal combustion engines in which oil products are used as an energy source [1,2,3,4]. IJET Volume 12 conventionally on petrol fuels which is a fossil fuel and whose production and combustion result in the emission of gases that have adversely affected human health and environment [5]. The greenhouse gas emissions from the combustion of hydrocarbon fuels have been identified as the major causes of climate change and global warming. Climate change and global warning are serious contemporary challenges that face humanity. The numerous and varied effects of climate change on the environment, human life and the economy of the nations are becoming increasingly obvious and real
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