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Scopus YÖKSİS DOI Eşleşti SJR Q1

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

Energy · Ekim 2025

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YÖKSİS Kayıtları
Hybrid experimental–ML–RSM framework for optimizing diesel engine performance with waste tire oil blends
Energy · 2025 SCI-Expanded
Prof. Dr. TANZER ERYILMAZ →
Hybrid experimental-ML-RSM framework for optimizing diesel engine performance with waste tire oil blends
ENERGY · 2025 SCI-Expanded
Prof. Dr. TANZER ERYILMAZ →
Hybrid experimental–ML–RSM framework for optimizing diesel engine performance with waste tire oil blends
Energy · 2025 SCI-Expanded
Doç. Dr. NURİ ORHAN →
YÖKSİS ISSN Eşleşmesi

Bu dergide (ISSN eşleşmesi) kurumun 20 kaydı bulundu.

YÖKSİS Kayıtları — ISSN Eşleşmesi
From public policy towards the green energy transition: Do economic freedom, economic globalization, environmental policy stringency, and material productivity matter?
2024 ISSN: 0360-5442 SCI-Expanded Q1
Dr. Öğr. Üyesi NAZLI DEMİRTAŞ →
Investigation and modeling of the tractive performance of radial tires using off road vehicles
2015 ISSN: 03605442 SCI-Expanded
Prof. Dr. ŞERAFETTİN EKİNCİ →
Investigation and modeling of the tractive performance of radial tiresusing off road vehicles
2015 ISSN: 0360-5442 SCI
Prof. Dr. HUMAR KAHRAMANLI ÖRNEK →
Investigation and modeling of the tractive performance of radial tiresusing off road vehicles
2015 ISSN: 0360-5442 SCI-Expanded
Prof. Dr. KAZİM ÇARMAN →
Comparison of energy of irrigation regimes in sugar beet production in a semi arid region
2010 ISSN: 03605442 SCI
Prof. Dr. RAMAZAN TOPAK →
Comparison of energy of irrigation regimes in sugar beet production in a semi arid region
2010 ISSN: 03605442 SCI-Expanded Q1
Dr. Öğr. Üyesi SİNAN SÜHERİ →
A comperative analysis of the engine performance, exhaust emissions and combustion behaviors of a compression ignition engine fuelled with biodiesel/diesel/1-butanol(C4 alcohol) and biodiesel/diesel/n-pentanol (C5 alcohol) fuel blends
2018 ISSN: 0360-5442 SCI
Prof. Dr. TANZER ERYILMAZ →
Rule-based Mamdani-type fuzzy modeling of heating and cooling performances of counter-flow Ranque–Hilsch vortex tubes with different geometric construction for steel
2013 ISSN: 03605442 SCI
Prof. Dr. ADNAN BERBER →
The Use of Energy in Milk Production: A case from Konya Province of Turkey
2019 ISSN: 0360-5442 SCI
Doç. Dr. AYSUN YENER ÖGÜR →
Polynomial regression method for optimization of biodiesel production from black mustard (Brassica nigra L.) seed oil using methanol, ethanol, NaOH, and KOH
2020 ISSN: 0360-5442 SCI-Expanded
Prof. Dr. TANZER ERYILMAZ →
Experimental investigation of the effects of orientation angle on heat transfer performance of pin-finned surfaces in natural convection
2011 ISSN: 0360-5442 SCI-Expanded Q1
Prof. Dr. AHMET ALİ SERTKAYA →
A new energy crop onopordum spp.: A research on biofuel properties
2022 ISSN: 0360-5442 SCI Q1
Dr. Öğr. Üyesi EYÜB CANLI →
A new energy crop onopordum spp.: A research on biofuel properties
2022 ISSN: 0360-5442 SCI-Expanded Q1
Prof. Dr. MUSTAFA ACAROĞLU →
A new energy crop onopordum spp.: A research on biofuel properties
2022 ISSN: 0360-5442 SCI Q1
Prof. Dr. HASAN AYDOĞAN →
High-performance triboelectric nanogenerator based on carbon nanomaterials functionalized polyacrylonitrile nanofibers
2022 ISSN: 0360-5442 SCI-Expanded Q1
Prof. Dr. MUSTAFA ERSÖZ →
Sustainability analysis of zero energy consumption data centers with free cooling, waste heat reuse and renewable energy systems: A feasibility study
2023 ISSN: 0360-5442 SCI-Expanded Q1
Doç. Dr. GÜL NİHAL GÜĞÜL →
Experimentally detected aerodynamic drag coefficient of the agricultural tractor form considering effects of windshield angle and hood front shape
2024 ISSN: 0360-5442 SCI-Expanded Q1
Dr. Öğr. Üyesi EYÜB CANLI →
Experimental investigation of the effect of variable valve lift on combustion stability and exhaust emissions in a diesel/methane CRDI engine
2024 ISSN: 0360-5442 SCI-Expanded Q1
Dr. Öğr. Üyesi HALİL ERDİ GÜLCAN →
Experimental investigation of the effect of variable valve lift on combustion stability and exhaust emissions in a diesel/methane CRDI engine
2024 ISSN: 0360-5442 SCI-Expanded Q1
Prof. Dr. MURAT CİNİVİZ →
Assessment of trade-off, exergetic performance, and greenhouse gas impact-cost analysis of a diesel engine running with different proportions of TiO2, Ag2O, and CeO2 nanoadditives
2024 ISSN: 0360-5442 SCI-Expanded Q1
Dr. Öğr. Üyesi HALİL ERDİ GÜLCAN →

Makale Bilgileri

Dergi Energy
ISSN03605442
Yayın TarihiEkim 2025
Cilt / Sayfa334
DOI10.1016/j.energy.2025.137491
Scopus ID2-s2.0-105010929999
Özet 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 (NO<inf>x</inf>, CO<inf>2</inf>, HC, exhaust gas temperature) were measured. Three ML models Artificial Neural Network (ANN), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost) were trained on 45 experimental data points to predict engine behavior. RSM was applied using a 13point design to model nonlinear interactions and perform multi-objective optimization. Results showed a maximum BTE of 33 % for D100 and 30 % for both TO2 and TO7. BSFC was lowest at 198 gkWh<sup>-1</sup> for D100, with slightly higher values for TO blends. TO7 exhibited peak NO<inf>x</inf> emissions of 640 ppm but showed HC reduction to 8 ppm at higher speeds. CO<inf>2</inf> emissions declined with speed, reaching 11.7 % for TO7 at 1800 rpm. Among ML models, RF and XGBoost achieved the best predictive accuracy, with most predictions within ±10 % of experimental values. RSM optimization identified 1.2 % TO at 2344 rpm as optimal, predicting BTE of 25.81 %, BSFC of 328.10 g/kWh, and NO<inf>x</inf> of 306.97 ppm. This study confirms that low-percentage TO blends offer viable engine performance with lower emissions, and that ML–RSM integration enhances predictive and optimization capabilities.

Yazarlar (4)

1
Seda Şahin
ORCID: 0000-0003-1743-9530
2
Tanzer Eryilmaz
3
Nuri Orhan
ORCID: 0000-0002-9987-1695
4
Murat Ertuğrul
ORCID: 0009-0005-2036-7533

Anahtar Kelimeler

Engine optimization Machine learning Response surface methodology (RSM) Waste tire biodiesel

Kurumlar

Bozok Üniversitesi
Yozgat Turkey
Selçuk Üniversitesi
Selçuklu Turkey
Scimago Dergi (ISSN Eşleşmesi)
Energy
Q1
SJR Skoru2,211
H-Index274
YayıncıElsevier Ltd
ÜlkeUnited Kingdom
Building and Construction (Q1)
Civil and Structural Engineering (Q1)
Electrical and Electronic Engineering (Q1)
Energy Engineering and Power Technology (Q1)
Energy (miscellaneous) (Q1)
Fuel Technology (Q1)
Industrial and Manufacturing Engineering (Q1)
Management, Monitoring, Policy and Law (Q1)
Mechanical Engineering (Q1)
Modeling and Simulation (Q1)
Pollution (Q1)
Renewable Energy, Sustainability and the Environment (Q1)
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Metrikler

7
Atıf
4
Yazar
4
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SŞ
SEDA ŞAHİN
Doç. Dr.

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