Kinematic viscosity estimation of fuel oil with comparison of machine learning methods
Yazarlar (4)
Dr. Öğr. Üyesi Enes CENGİZ Sinop Üniversitesi, Türkiye
Mustafa Babagiray
Afyon Kocatepe Üniversitesi, Türkiye
Afyon Kocatepe Üniversitesi, Türkiye
Dr. Öğr. Üyesi Faruk Emre Aysal Afyon Kocatepe Üniversitesi, Türkiye
Prof. Dr. Fatih Aksoy Afyon Kocatepe Üniversitesi, Türkiye
| Makale Türü | Özgün Makale (SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale) | ||
| Dergi Adı | Fuel (Q1) | ||
| Dergi ISSN | 0016-2361 Dergi Bilgileri (2022) | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | Türkçe | Basım Tarihi | 05-2022 |
| Kabul Tarihi | – | Yayınlanma Tarihi | 01-05-2022 |
| Cilt / Sayı / Sayfa | 316 / 1 / 123422–0 | DOI | 10.1016/j.fuel.2022.123422 |
| Makale Linki | http://dx.doi.org/10.1016/j.fuel.2022.123422 | ||
| UAK Araştırma Alanları |
Yapay Zeka
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| Özet |
| It is known that one of the important parameters affecting the emission and performance values of the liquid fuels used in thermal machines is the viscosity value. Therefore, many different studies have been carried out on the determination of dynamic and kinematic viscosities of liquid fuels. In this study, the kinematic viscosity value of Fuel Oil 4 at a constant temperature of 100 °C was estimated using machine learning methods. Extreme Learning Machine (ELM), Multi-Layer Perceptron (MLP), and K Nearest Neighbor (K-nn) methods were used to perform kinematic viscosity estimations. Two different distance metrics are considered in the K-nn algorithm. The experimentally obtained water content, density and flash point properties of the fuel were used as input data for machine learning approaches. Thus, four different models were developed for the kinematic viscosity estimation of Fuel oil fuel. The success rates of … |
| Anahtar Kelimeler |
| Extreme learning machine | Fuel oil | Kinematic viscosity | Machine learning |
Science Direct
Kinematic viscosity estimation of fuel oil with comparison of machine learning methods
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Web of Science | 35 |
| Scopus | 36 |
| Google Scholar | 41 |