Experimental investigation and artificial neural network modeling of performance and emission of a CI engine using orange peel oil- diesel blends

Abstract

ABSTRACT The exponential decay of crude oil and environmental degradation forced 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 made. 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. The prediction of data is made employing the 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 performance and accuracy were totally satisfactory. There is a noticeable reduction in the emission parameters.