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

Işın Ö., Uzunsoy E.

ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING, vol.38, pp.3485-3493, 2013 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 38
  • Publication Date: 2013
  • Doi Number: 10.1007/s13369-013-0637-7
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.3485-3493
  • Keywords: Internal combustion engines, Exhaust emissions, Neuro-fuzzy inference system, ANFIS, DIESEL-ENGINE, PERFORMANCE, FUEL
  • Yıldız Technical University Affiliated: Yes


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.