The Application of Artificial Neural Network in Prediction of the Performance of Spark Ignition Engine Running on Ethanol-Petrol Blends

Abstract

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.

An Assessment of Potential Resources for Biomass Energy in Nigeria

“…Investigations on the production of ethanol from various biomass materials have been carried out by Ndukwe et al [300], Ogali et al [301], Otaraku et al [302], Omotosho and Amori [303], and Etsuyankpa et al [304]. The performance of ethanol blends with gasoline in spark ignition engines has been investigated by Igbokwe et al [305], Nwufo et al [306], and Okoronkwo et al [307]. Yahuza and Dandakouta [308] reviewed the performance of ethanol blends with diesel in compression ignition engines.…”

Evaluation of Emission Pattern of Compression Ignition Engines Fuelled With Blends of Orange Peel Oil Based Biodiesel Using Artificial Neural Network Model

“…Zhiqiang et al [45] predicted the performance and emissions of a single cylinder PFI engine running in blends of butanol-gasoline, using compression ratio, equivalence ratio, blend percentage and engine brake load as input parameters. Nwufo et al [46] investigated the performance of a single cylinder G200 IMEX spark ignition engine using artificial neural network model approach. The model showed that the Levenberg Marquardt algorithm and logistic sigmoid activation function for a model of structure 4-10-2 provided a sufficiently high prediction accuracy.…”