EVALUATION OF PERFORMANCE, EMISSION CHARACTERISTICS USING ARTIFICIAL NEURAL NETWORK IN an UNMODIFIED DIESEL ENGINE FUELED WITH ORANGE PEEL OIL & DIESEL BLENDS

Abstract

Energy is a fundamental necessity for economic growth. Every area of the Indian economy agriculture, manufacturing, transportation, commercial, and domestic-requires energy input. Since independence, economic growth plans have necessitated a growing quantity of energy. As a result, consumption of all types of energy has been gradually increasing across the country. The 70% of energy demand is fulfilled by petroleum fuels. This also the exponential decay of crude oil and environmental degradation forced the researchers to investigate alternative sustainable fuels. One of the promising alternatives to petroleum oil is orange peel oil. Orange peel oil has characteristics that are similar to diesel, and it is also simple to blend. In this paper performance and emission, parameters are investigated, and using an artificial neural network (ANN) its prediction and validation have been done. The blend shows the best results for 30% orange peel oil by vol. and 70% diesel. At peak load, the brake thermal efficiency (BTE) of this blend is 17.5 % higher than diesel, and the brake specific energy consumption (BSEC) is 20 % lower. These experimental results were used to perform prediction and validation. Using time series analysis, the prediction of data is made by means of Quasi- newton method algorithm and it is found that it best fits the linear regression analysis. The value of R2 for BTE and BSEC is 0.994 and 0.986 respectively. The suggested ANN model's performance and accuracy were totally satisfactory. There is a noticeable reduction in the emission parameters.