Artificial Neural Network based prediction of Specific Fuel Consumption in various Compression Ratio CI Engine Fueled with Various Plastic Composition Pyrolysis oil and Diesel

Abstract

This study looks at the usage of Artificial Neural Network Modeling for predicting Specific Fuel Consumption for compression ignition engines running on Diesel, Low-Density Polyethylene Pyrolysis Oil (LDPE PO), High-Density Polyethylene Pyrolysis Oil (HDPE PO), and Polypropylene Pyrolysis Oil (PP PO). Using preliminary practical findings, The Model of ANN was built to estimate SFC by adjusting the parameters of the engine. SFC is anticipated by adjusting the parameters and utilizing the orthogonal array. The experiment is then carried out using an orthogonal array. The outcomes of experimental work are used to create an ANN model. In the case of a non-linear mapping between input & output, the classic back-propagation method and a multi-layer perception network are utilized. The values of Mean Square Error (MSE), Root Mean Square Error (RMSE), and Regression Coefficient (R2) for LM10TP architecture are 1.5143×10–06, 0.0012 & 1 in training & the validation values 1.2185×10–06, 0.0011 and 0.9999 respectively. It was discovered that neural networks are useful tools for prediction since they performed well in specific fuel consumption prediction in both training and validation.