Elman and cascade neural networks with conjugate gradient Polak-Ribière restarts to predict diesel engine performance and emissions fueled by butanol as sustainable biofuel

This study explores the possibility of utilizing green butanol, a promising type of biofuel, in diesel engines to measure its impact on engine performance and emissions. Experimental data were collected on several parameters, including brake-specific fuel consumption (BSFC), brake thermal efficiency...

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Main Authors: Hananto, A.L., Fauzi, A., Suhara, A., Davison, I., Spraggon, M., Herawan, S.G., Samuel, O.D., Yusuf, A.A., Idris, M., Veza, I.
Format: Article
Published: Elsevier B.V. 2023
Online Access:http://scholars.utp.edu.my/id/eprint/37372/
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85166321132&doi=10.1016%2fj.rineng.2023.101334&partnerID=40&md5=5f03e69f70cab6c32517b0d893ef6209
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spelling oai:scholars.utp.edu.my:373722023-10-04T11:26:08Z http://scholars.utp.edu.my/id/eprint/37372/ Elman and cascade neural networks with conjugate gradient Polak-Ribière restarts to predict diesel engine performance and emissions fueled by butanol as sustainable biofuel Hananto, A.L. Fauzi, A. Suhara, A. Davison, I. Spraggon, M. Herawan, S.G. Samuel, O.D. Yusuf, A.A. Idris, M. Veza, I. This study explores the possibility of utilizing green butanol, a promising type of biofuel, in diesel engines to measure its impact on engine performance and emissions. Experimental data were collected on several parameters, including brake-specific fuel consumption (BSFC), brake thermal efficiency (BTE), exhaust gas temperature, all of which are influenced by the biofuel type used, in this case, butanol. The study examined emissions such as carbon monoxide (CO), hydrocarbons (HC), nitrogen oxides (NOx), and smoke opacity. Such emissions have the potential to improve with the use of different biofuels ratios, such as butanol. Advanced machine learning techniques, Elman and Cascade Neural Networks, were employed to predict the performance and emission characteristics of engines using butanol. The models were trained using a Conjugate Gradient Learning Function with Polak-Ribière Restarts to simulate the effects of butanol as biofuel, on diesel engines. Key findings revealed that when incorporating butanol into diesel fuel blends, potential improvements in BTE and fuel efficiency were observed. Notably, using butanol as a biofuel reduced exhaust gas temperatures and CO emissions, demonstrating the potential of this particular biofuel. Conversely, there were observed increases in HC emissions and smoke opacity, signifying the complexities of using biofuels such as butanol. Cascade neural network proved to be highly accurate in predicting engine performance parameters fueled with butanol as biofuel. Overall, the study offers valuable insights into the use of butanol as a biofuel, its potential benefits, and challenges, underscoring the importance of continuous research in sustainable biofuels such as butanol. © 2023 The Authors Elsevier B.V. 2023 Article NonPeerReviewed Hananto, A.L. and Fauzi, A. and Suhara, A. and Davison, I. and Spraggon, M. and Herawan, S.G. and Samuel, O.D. and Yusuf, A.A. and Idris, M. and Veza, I. (2023) Elman and cascade neural networks with conjugate gradient Polak-Ribière restarts to predict diesel engine performance and emissions fueled by butanol as sustainable biofuel. Results in Engineering, 19. ISSN 25901230 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85166321132&doi=10.1016%2fj.rineng.2023.101334&partnerID=40&md5=5f03e69f70cab6c32517b0d893ef6209 10.1016/j.rineng.2023.101334 10.1016/j.rineng.2023.101334 10.1016/j.rineng.2023.101334
institution Universiti Teknologi Petronas
building UTP Resource Centre
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Petronas
content_source UTP Institutional Repository
url_provider http://eprints.utp.edu.my/
description This study explores the possibility of utilizing green butanol, a promising type of biofuel, in diesel engines to measure its impact on engine performance and emissions. Experimental data were collected on several parameters, including brake-specific fuel consumption (BSFC), brake thermal efficiency (BTE), exhaust gas temperature, all of which are influenced by the biofuel type used, in this case, butanol. The study examined emissions such as carbon monoxide (CO), hydrocarbons (HC), nitrogen oxides (NOx), and smoke opacity. Such emissions have the potential to improve with the use of different biofuels ratios, such as butanol. Advanced machine learning techniques, Elman and Cascade Neural Networks, were employed to predict the performance and emission characteristics of engines using butanol. The models were trained using a Conjugate Gradient Learning Function with Polak-Ribière Restarts to simulate the effects of butanol as biofuel, on diesel engines. Key findings revealed that when incorporating butanol into diesel fuel blends, potential improvements in BTE and fuel efficiency were observed. Notably, using butanol as a biofuel reduced exhaust gas temperatures and CO emissions, demonstrating the potential of this particular biofuel. Conversely, there were observed increases in HC emissions and smoke opacity, signifying the complexities of using biofuels such as butanol. Cascade neural network proved to be highly accurate in predicting engine performance parameters fueled with butanol as biofuel. Overall, the study offers valuable insights into the use of butanol as a biofuel, its potential benefits, and challenges, underscoring the importance of continuous research in sustainable biofuels such as butanol. © 2023 The Authors
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author Hananto, A.L.
Fauzi, A.
Suhara, A.
Davison, I.
Spraggon, M.
Herawan, S.G.
Samuel, O.D.
Yusuf, A.A.
Idris, M.
Veza, I.
spellingShingle Hananto, A.L.
Fauzi, A.
Suhara, A.
Davison, I.
Spraggon, M.
Herawan, S.G.
Samuel, O.D.
Yusuf, A.A.
Idris, M.
Veza, I.
Elman and cascade neural networks with conjugate gradient Polak-Ribière restarts to predict diesel engine performance and emissions fueled by butanol as sustainable biofuel
author_facet Hananto, A.L.
Fauzi, A.
Suhara, A.
Davison, I.
Spraggon, M.
Herawan, S.G.
Samuel, O.D.
Yusuf, A.A.
Idris, M.
Veza, I.
author_sort Hananto, A.L.
title Elman and cascade neural networks with conjugate gradient Polak-Ribière restarts to predict diesel engine performance and emissions fueled by butanol as sustainable biofuel
title_short Elman and cascade neural networks with conjugate gradient Polak-Ribière restarts to predict diesel engine performance and emissions fueled by butanol as sustainable biofuel
title_full Elman and cascade neural networks with conjugate gradient Polak-Ribière restarts to predict diesel engine performance and emissions fueled by butanol as sustainable biofuel
title_fullStr Elman and cascade neural networks with conjugate gradient Polak-Ribière restarts to predict diesel engine performance and emissions fueled by butanol as sustainable biofuel
title_full_unstemmed Elman and cascade neural networks with conjugate gradient Polak-Ribière restarts to predict diesel engine performance and emissions fueled by butanol as sustainable biofuel
title_sort elman and cascade neural networks with conjugate gradient polak-ribiã¨re restarts to predict diesel engine performance and emissions fueled by butanol as sustainable biofuel
publisher Elsevier B.V.
publishDate 2023
url http://scholars.utp.edu.my/id/eprint/37372/
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85166321132&doi=10.1016%2fj.rineng.2023.101334&partnerID=40&md5=5f03e69f70cab6c32517b0d893ef6209
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