Predicting the Exhaust Emissions of a Spark Ignition Engine Using Adaptive Neuro-Fuzzy Inference System

Abstract

This paper presents a fuzzy logic-based prediction method to reveal the performance and emission characteristics of a single cylinder spark ignition (SI) engine, which uses different fuel mixtures (gasoline–water macro-emulsions, which contains isopropanol). Adaptive neuro-fuzzy inference system, ANFIS, was used to determine some characteristic parameters due to the combustion, such as exhaust emissions (CO, CO2, HCs). Experimental data such as engine power, torque, engine speed, brake mean effective pressure, brake specific fuel consumption were used as training and checking inputs for the ANFIS model to provide a predictive algorithm. The main purpose of this study is to provide a reliable model that can reveal different performance characteristics, which can be obtained from various gasoline–water macro-emulsions and doing this by the elimination of new experiments. The preliminary results show that an acceptable ANFIS model can also be used in experimental design procedures, by providing quick data handling and the results.