Using some performance parameters to predict exhaust gas emissions of a turboprop engine: adaptive neuro-fuzzy method
by Yasin Şöhret; Işıl Yazar; T. Hikmet Karakoç
International Journal of Sustainable Aviation (IJSA), Vol. 2, No. 1, 2016

Abstract: This paper presents an exhaust gas emissions prediction model for a turboprop engine depending on some performance parameters. Within this context, experimentally collected emissions data is used to develop a model in the adaptive network-based fuzzy inference system. For system identification in the adaptive network-based fuzzy inference system, grid partitioning is preferred as the clustering method, and the accuracy of the prediction model is acceptable to the best of the authors' knowledge. The root mean square error is found to be 0.12375, 4.7332, 0.081264 and 0.033515 for the emissions index prediction of carbon monoxide, carbon dioxide, nitrogen oxides and unburned hydrocarbons, respectively.

Online publication date: Fri, 22-Apr-2016

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Sustainable Aviation (IJSA):
Login with your Inderscience username and password:

    Username:        Password:         

Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email