eprintid: 4060
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/00/40/60
datestamp: 2023-11-09 15:52:20
lastmod: 2023-11-09 15:52:20
status_changed: 2023-11-09 15:48:14
type: article
metadata_visibility: show
creators_name: Tamiru, A.L.
creators_name: Hashim, F.M.
title: Application of bat algorithm and fuzzy systems to model exergy changes in a gas turbine
ispublished: pub
note: cited By 16
abstract: Exergy analysis plays a major role in thermal systems. Using exergy, apart from finding components for a potential for further improvement, fault detection and diagnosis, performance optimization, and environmental impact assessment can be conducted. This chapter addresses the use of fuzzy systems for modeling exergy destructions in the main components of an industrial gas turbine. The details include: (i) system description and the challenges in developing first principle models, (ii) thermodynamic models for part load and full load operating conditions, (iii) model identification technique that uses fuzzy systems and a meta-heuristic nature inspired algorithm called Bat Algorithm, (iv) validation graphs for semi-empirical models, and (v) validation test for fuzzy models. In the validation of the fuzzy model, the inputs to the model are considered the same as the inputs as experienced by the gas turbine generator. The comparison tests between actual data and prediction demonstrate how promising the combined method is as compared to separate use of the fuzzy systems trained by a heuristic approach.
date: 2013
publisher: Springer Verlag
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84867562513&doi=10.1007%2f978-3-642-29694-9_26&partnerID=40&md5=a2b071d0a4007bda55b945377511dcd3
id_number: 10.1007/978-3-642-29694-9₂₆
full_text_status: none
publication: Studies in Computational Intelligence
volume: 427
pagerange: 685-719
refereed: TRUE
isbn: 9783642296932
issn: 1860949X
citation:   Tamiru, A.L. and Hashim, F.M.  (2013) Application of bat algorithm and fuzzy systems to model exergy changes in a gas turbine.  Studies in Computational Intelligence, 427.  pp. 685-719.  ISSN 1860949X