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Hybrid experimental–ML–RSM framework for optimizing diesel engine performance with waste tire oil blends

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  • Sahin, Seda
  • Eryilmaz, Tanzer
  • Orhan, Nuri
  • Ertuğrul, Murat

Abstract

This study presents an integrated approach combining experimental investigation, machine learning (ML), and response surface methodology (RSM) to assess and optimize the performance and emissions of a diesel engine fueled with low-percentage waste tire pyrolysis oil (TO) blends. Diesel–TO blends at 2 % (TO2) and 7 % (TO7) were tested alongside pure diesel (D100) across engine speeds from 1100 to 2400 rpm. Key metrics such as brake thermal efficiency (BTE), brake specific fuel consumption (BSFC), and emissions (NOx, CO2, HC, exhaust gas temperature) were measured.

Suggested Citation

  • Sahin, Seda & Eryilmaz, Tanzer & Orhan, Nuri & Ertuğrul, Murat, 2025. "Hybrid experimental–ML–RSM framework for optimizing diesel engine performance with waste tire oil blends," Energy, Elsevier, vol. 334(C).
    • Handle: RePEc:eee:energy:v:334:y:2025:i:c:s0360544225031330
    DOI: 10.1016/j.energy.2025.137491
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