Developing correlations by extreme learning machine for calculating higher heating values of waste frying oils from their physical properties
Yükleniyor...
Tarih
2017-11-01
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Springer Nature
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Attribution-NonCommercial-ShareAlike 3.0 United States
Attribution-NonCommercial-ShareAlike 3.0 United States
Özet
In this study, a novel approach was proposed based on extreme learning machine (ELM) for developing correlations in order to calculate higher heating values (HHVs, kj/kg) of waste frying oils from their physical properties such as density (ρ, kg/m 3 ) and kinematic viscosity (v, mm 2 /s) values. These values can easily be determined by using laboratory equipment. For developing the correlations, an experimental dataset from the literature covering 35 samples was collected to be employed in the training and validation steps. The obtained optimum parameters of artificial neural network in the training stage by ELM were employed to develop new correlations. The HHVs calculated by using density-based correlation (HHV = 50823.183 − 12.34095ρ) showed the mean absolute and relative errors of 145.8048 kJ/kg and 0.3695 %, respectively. In the case of the viscosity-based correlation (HHV = 40172.85 − 17.93615v), they were found as 129.04 kJ/kg and 0.327 %, respectively. Additionally, new correlations were performed better than those available in the literature and those obtained by other machine learning methods; therefore, it is highly suggested that the proposed approach can be used for developing new correlations.
Açıklama
Anahtar Kelimeler
Extreme Learning Machine, Higher Heating Value, Mathematical Modeling, Waste Frying Oils
Kaynak
WoS Q Değeri
Q1
Scopus Q Değeri
Q1
Cilt
28
Sayı
11
Künye
Ertuğrul, Ö F., Altun, Ş. (2016). Developing correlations by extreme learning machine for calculating higher heating values of waste frying oils from their physical properties. Neural Computing and Applications, 28(11), pp. 3145-3152. https://doi.org/10.1007/s00521-016-2233-8
