Experimental Investigation and Prediction of Combustion Parameters using Machine Learning in Ethanol - Gasoline Blended Engines Article Swipe
Shailesh Sonawane
,
Ravi Sekhar
,
Arundhati Warke
,
S. S. Thipse
,
S. D. Rairikar
,
Chetan Varma
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.5614/j.eng.technol.sci.2025.57.1.3
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.5614/j.eng.technol.sci.2025.57.1.3
Alternative fuels play an important role in eco-friendly transport solutions. Wider adoption of alternative blended fuels in automobiles is dependent on a better understanding of the blended fuel engine characteristics. This paper presents an experimental investigation on the part load combustion characteristics of a multi cylinder spark ignition (SI) engine fueled by E0 and E10 ethanol blends. Full factorial Taguchi experimental design was employed to include multi-level engine speed (rpm) and load (throttle %) variations. High-speed data acquisition was used to record combustion parameters viz. maximum pressure (Pmax), indicative mean effective pressure (IMEP), start of combustion (SOC), mass burn fraction (MBF) and burn duration (Brn_drn) over 300 combustion cycles for each experimental run. Grey Relational Analysis (GRA) was used to determine the optimum best and worst engine operating conditions based on Pmax, IMEP, MBF and Brn_drn. Cycle-to-cycle variations of Pmax were also examined in detail to identify the worst engine operating condition. Random Forest machine learning algorithm was employed to accurately model Pmax and SOC in terms of the engine part load operating conditions. This model can be used to predict Pmax and SOC characteristics of an E0/E10 fueled SI engine under different operating conditions, eliminating the need for extensive testing
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.5614/j.eng.technol.sci.2025.57.1.3
- OA Status
- gold
- Cited By
- 2
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4407038081
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4407038081Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.5614/j.eng.technol.sci.2025.57.1.3Digital Object Identifier
- Title
-
Experimental Investigation and Prediction of Combustion Parameters using Machine Learning in Ethanol - Gasoline Blended EnginesWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-01-22Full publication date if available
- Authors
-
Shailesh Sonawane, Ravi Sekhar, Arundhati Warke, S. S. Thipse, S. D. Rairikar, Chetan VarmaList of authors in order
- Landing page
-
https://doi.org/10.5614/j.eng.technol.sci.2025.57.1.3Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.5614/j.eng.technol.sci.2025.57.1.3Direct OA link when available
- Concepts
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Mean effective pressure, Automotive engineering, Throttle, Combustion, Gasoline, Petrol engine, Ignition system, Computer science, Environmental science, Internal combustion engine, Process engineering, Engineering, Compression ratio, Waste management, Chemistry, Aerospace engineering, Organic chemistryTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 2Per-year citation counts (last 5 years)
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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