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

Content Provider	Springer Nature Link
Author	Isin, O. Uzunsoy, E.
Copyright Year	2013
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, CO$_{2}$, 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.
Starting Page	3485
Ending Page	3493
Page Count	9
File Format	PDF
ISSN	13198025
Journal	Arabian Journal for Science and Engineering
Volume Number	38
Issue Number	12
e-ISSN	21914281
Language	English
Publisher	Springer Berlin Heidelberg
Publisher Date	2013-09-06
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Internal combustion engines Exhaust emissions Neuro-fuzzy inference system ANFIS Engineering Science
Content Type	Text
Resource Type	Article

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