ANFIS Model for Prediction of Performance-Emission Paradigm of a DICI Engine Fueled with the Blends of Fish Oil Methyl Ester, n-Pentanol and Diesel

Abstract

A precise, robust model for complex systems like IC Engines would be much beneficial because of environmental issues, fossil fuel depletion and accumulation of on-road vehicles. The present study depicts the compatibility of higher alcohols like n-pentanol that are produced in renewable ways as a promising blending additive with biodiesel fuels. Biodiesel prepared from the waste parts of the fishes is used to blend with petrodiesel. The methyl esters of fishoil biodiesel (MEFO) and n-pentanol are blended with petrodiesel at different proportions are tested on a four-stroke single cylinder DICI engine and results from witnesses the noble benefits of adding higher alcohols that are observed in both performance and as well as in emissions. The experimental paradigm is further fed to an artificial intelligent model to test the inherent predicting capability an Artificial Intelligent Adaptive Neuro-fuzzy Interface System (ANFIS). A sugeno network with brake power and percentage of biodiesel as input parameters and engine response paradigm such as BSFC, BTE, HC, CO and NOx as outputs are modelled and tested on a statistical platform. It was found that the proposed model is robust and efficient system identification tool to map the input-output response paradigm with high accuracy as the regression (R) values are ranging from 0.9967 to 0.9999, RMSE is ranging 0.000026 to 0.0000336 and MAPE is very low ranging from 0.0021 to 0.0028.