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

Seda Sahin, Tanzer Eryilmaz, Nuri Orhan and Murat Ertuğrul

Energy, 2025, vol. 334, issue C

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.

Keywords: Waste tire biodiesel; Machine learning; Engine optimization; Response surface methodology (RSM) (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544225031330
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:334:y:2025:i:c:s0360544225031330

DOI: 10.1016/j.energy.2025.137491

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().