Experimental Investigation and Prediction of Combustion Parameters using Machine Learning in Ethanol - Gasoline Blended Engines

Authors

  • Shailesh Sonawane Symbiosis Institute of Technology (SIT) Pune Campus, Symbiosis International (Deemed University) (SIU), Pune, 412115, Maharashtra, India https://orcid.org/0000-0002-8464-0923
  • Ravi Sekhar Symbiosis Institute of Technology (SIT), Pune Campus, Symbiosis International (Deemed University) (SIU), Pune, 412115, Maharashtra https://orcid.org/0000-0002-4732-5246
  • Arundhati Warke Symbiosis Institute of Technology (SIT), Pune Campus, Symbiosis International (Deemed University) (SIU), Pune, 412115, Maharashtra https://orcid.org/0000-0002-6902-7434
  • Sukrut Thipse Automotive Research Association of India (ARAI), Pune, Maharashtra
  • Sandeep Rairikar Automotive Research Association of India (ARAI), Pune, Maharashtra
  • Chetan Varma Automotive Research Association of India (ARAI), Pune, Maharashtra https://orcid.org/0009-0007-6119-8902

Abstract

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

How to Cite

Shailesh Sonawane, Ravi Sekhar, Arundhati Warke, Sukrut Thipse, Sandeep Rairikar, Chetan Varma (2025). Experimental Investigation and Prediction of Combustion Parameters using Machine Learning in Ethanol - Gasoline Blended Engines . Journal of Engineering and Technological Sciences, Vol. 57 No. 1 (2025): Vol. 57 No. 1 (2025): February, 27-47. https://doi.org/10.5614/j.eng.technol.sci.2025.57.1.3

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Date Log

Submitted
April 12, 2024
Accepted
December 11, 2024
Published
January 22, 2025

Keywords

  • ANN
  • combustion
  • E0
  • E10
  • Grey Relational Analysis
  • machine learning
  • Random Forest
  • SI engines

How to Cite

Experimental Investigation and Prediction of Combustion Parameters using Machine Learning in Ethanol - Gasoline Blended Engines . (2025). Journal of Engineering and Technological Sciences, 57(1), 27-47. https://doi.org/10.5614/j.eng.technol.sci.2025.57.1.3

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