Machine learning of weighted superposition attraction algorithm for optimization diesel engine performance and emission fueled with butanol-diesel biofuel Article Swipe
Ibham Veza
,
Aslan Deniz Karaoğlan
,
Şener Akpınar
,
Martin Spraggon
,
Muhammad Idris
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1016/j.asej.2024.103126
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1016/j.asej.2024.103126
Machine learning (ML) is a subset of artificial intelligence (AI) and computer science that employs data and algorithms and mimics human learning to self-enhance its accuracy. In biofuel research, butanol is widely recognized as a prospective alternative biofuel. Butanol addition in diesel or combustion engine has been more and more studied recently. Gaining a comprehensive comprehension of butanol performance and emission characteristics using machine learning approach is an essential milestone in investigating alcohol-based biofuel addition in diesel engines. However, few studies investigated butanol effect on diesel engine emissions using machine learning for optimization. A novel optimization study is needed. This work aims to investigate the newly developed and efficient machine learning, weighted superposition attraction (WSA) algorithm, to optimize the emission and performance of diesel engines fuelled with butanol-diesel biofuel. Mathematical modeling between the factors (butanol (vol.%) and BMEP (bar)) and the responses (BTE (%), BSFC (g/kWh), Exhaust Temperature Texh (oC), NOx (g/kWh), CO (g/kWh), HC (g/kWh), and Smoke Opacity (%)) are governed using regression modeling. The optimized and best factor levels are determined employing the machine learning of WSA Algorithm. Confirmations are carried out. Optimization results indicate that the BTE is maximized, and the remainder of the responses are minimized.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.asej.2024.103126
- OA Status
- gold
- Cited By
- 7
- References
- 28
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- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4403659785Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1016/j.asej.2024.103126Digital Object Identifier
- Title
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Machine learning of weighted superposition attraction algorithm for optimization diesel engine performance and emission fueled with butanol-diesel biofuelWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-10-22Full publication date if available
- Authors
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Ibham Veza, Aslan Deniz Karaoğlan, Şener Akpınar, Martin Spraggon, Muhammad IdrisList of authors in order
- Landing page
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https://doi.org/10.1016/j.asej.2024.103126Publisher landing page
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
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https://doi.org/10.1016/j.asej.2024.103126Direct OA link when available
- Concepts
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Diesel fuel, Biofuel, Butanol, Diesel engine, Automotive engineering, n-Butanol, Superposition principle, Algorithm, Computer science, Environmental science, Process engineering, Engineering, Chemistry, Mathematics, Waste management, Ethanol, Organic chemistry, Mathematical analysisTop concepts (fields/topics) attached by OpenAlex
- Cited by
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7Total citation count in OpenAlex
- Citations by year (recent)
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2025: 7Per-year citation counts (last 5 years)
- References (count)
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28Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.its | 24 |
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| abstract_inverted_index.(%), | 143 |
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| abstract_inverted_index.(ML) | 2 |
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| abstract_inverted_index.that | 13, 187 |
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| abstract_inverted_index.work | 100 |
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| abstract_inverted_index.(oC), | 149 |
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| abstract_inverted_index.novel | 94 |
| abstract_inverted_index.study | 96 |
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| abstract_inverted_index.effect | 83 |
| abstract_inverted_index.engine | 44, 86 |
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| abstract_inverted_index.levels | 170 |
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| abstract_inverted_index.subset | 5 |
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| abstract_inverted_index.Gaining | 52 |
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| abstract_inverted_index.between | 131 |
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| abstract_inverted_index.approach | 65 |
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| abstract_inverted_index.indicate | 186 |
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| abstract_inverted_index.investigated | 81 |
| abstract_inverted_index.optimization | 95 |
| abstract_inverted_index.self-enhance | 23 |
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| abstract_inverted_index.comprehensive | 54 |
| abstract_inverted_index.investigating | 71 |
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| abstract_inverted_index.superposition | 112 |
| abstract_inverted_index.butanol-diesel | 127 |
| abstract_inverted_index.characteristics | 61 |
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| cited_by_percentile_year.min | 98 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 5 |
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| sustainable_development_goals[0].display_name | Affordable and clean energy |
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| citation_normalized_percentile.is_in_top_10_percent | True |