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Comparative analysis of different engine operating parameters for on-board fuel octane number classification

The comparative analysis on combustion of commercial gasoline with research octane number (RON) 95, 97, and 100 was carried out on a spark ignition (SI) engine under different engine speeds, loads and spark advances. The RON classification procedure was investigated using regression analysis and art...

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Main Authors: Ghanaati, A., Muhamad Said, M. F., Darus, I. Z. M.
Format: Article
Published: Elsevier Ltd 2017
Subjects:
TJ Mechanical engineering and machinery
Online Access:http://eprints.utm.my/id/eprint/75356/
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85020838300&doi=10.1016%2fj.applthermaleng.2017.06.013&partnerID=40&md5=d15f3af1a18c0d93eedf7e352643098d
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spelling my.utm.753562018-03-21T00:39:34Z http://eprints.utm.my/id/eprint/75356/ Comparative analysis of different engine operating parameters for on-board fuel octane number classification Ghanaati, A. Muhamad Said, M. F. Darus, I. Z. M. TJ Mechanical engineering and machinery The comparative analysis on combustion of commercial gasoline with research octane number (RON) 95, 97, and 100 was carried out on a spark ignition (SI) engine under different engine speeds, loads and spark advances. The RON classification procedure was investigated using regression analysis and artificial neural network (ANN) by executing the combustion properties derived from the in-cylinder pressure signal and engine rotational speed signal. The results showed a special pattern for each fuel RON; these patterns were obtained using the peak in-cylinder pressure, maximum rate of pressure rise, and maximum amplitude of pressure oscillations. In addition, a pre-defined threshold or formula is necessary to restrict the implementation of these parameters for on-board fuel identification. Lastly, the confusion matrix that provided the ANN model efficiency for RON classification had the highest accuracy when the pressure signal was employed as the network input for all spark advance timing. However, the ANN model with rotational speed signal input could only identify the fuel octane number after a specific advance timing that was detected at the beginning of noisy combustion because of knock. The confusion matrix for the ANN with speed signal input increased from 68.1% to 100% when ignition advanced from −10° to −30° before top dead center. The results established the feasibility of use of the rotational speed signal as the input for an ANN model to identify different fuel octane number patterns. Elsevier Ltd 2017 Article PeerReviewed Ghanaati, A. and Muhamad Said, M. F. and Darus, I. Z. M. (2017) Comparative analysis of different engine operating parameters for on-board fuel octane number classification. Applied Thermal Engineering, 124 . pp. 327-336. ISSN 1359-4311 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85020838300&doi=10.1016%2fj.applthermaleng.2017.06.013&partnerID=40&md5=d15f3af1a18c0d93eedf7e352643098d
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic TJ Mechanical engineering and machinery
spellingShingle TJ Mechanical engineering and machinery
Ghanaati, A.
Muhamad Said, M. F.
Darus, I. Z. M.
Comparative analysis of different engine operating parameters for on-board fuel octane number classification
description The comparative analysis on combustion of commercial gasoline with research octane number (RON) 95, 97, and 100 was carried out on a spark ignition (SI) engine under different engine speeds, loads and spark advances. The RON classification procedure was investigated using regression analysis and artificial neural network (ANN) by executing the combustion properties derived from the in-cylinder pressure signal and engine rotational speed signal. The results showed a special pattern for each fuel RON; these patterns were obtained using the peak in-cylinder pressure, maximum rate of pressure rise, and maximum amplitude of pressure oscillations. In addition, a pre-defined threshold or formula is necessary to restrict the implementation of these parameters for on-board fuel identification. Lastly, the confusion matrix that provided the ANN model efficiency for RON classification had the highest accuracy when the pressure signal was employed as the network input for all spark advance timing. However, the ANN model with rotational speed signal input could only identify the fuel octane number after a specific advance timing that was detected at the beginning of noisy combustion because of knock. The confusion matrix for the ANN with speed signal input increased from 68.1% to 100% when ignition advanced from −10° to −30° before top dead center. The results established the feasibility of use of the rotational speed signal as the input for an ANN model to identify different fuel octane number patterns.
format Article
author Ghanaati, A.
Muhamad Said, M. F.
Darus, I. Z. M.
author_facet Ghanaati, A.
Muhamad Said, M. F.
Darus, I. Z. M.
author_sort Ghanaati, A.
title Comparative analysis of different engine operating parameters for on-board fuel octane number classification
title_short Comparative analysis of different engine operating parameters for on-board fuel octane number classification
title_full Comparative analysis of different engine operating parameters for on-board fuel octane number classification
title_fullStr Comparative analysis of different engine operating parameters for on-board fuel octane number classification
title_full_unstemmed Comparative analysis of different engine operating parameters for on-board fuel octane number classification
title_sort comparative analysis of different engine operating parameters for on-board fuel octane number classification
publisher Elsevier Ltd
publishDate 2017
url http://eprints.utm.my/id/eprint/75356/
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85020838300&doi=10.1016%2fj.applthermaleng.2017.06.013&partnerID=40&md5=d15f3af1a18c0d93eedf7e352643098d
_version_ 1643657040499834880
score 13.211869

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