Developing correlations by extreme learning machine for calculating higher heating values of waste frying oils from their physical properties

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....

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Bibliographic Details
Published in:Neural computing & applications 2017-11, Vol.28 (11), p.3145-3152
Main Authors: Ertuğrul, Ömer F., Altun, Şehmus
Format: Article
Language:English
Subjects:
Artificial Intelligence
Artificial neural networks
Computational Biology/Bioinformatics
Computational Science and Engineering
Computer Science
Correlation
Data Mining and Knowledge Discovery
Extreme values
Frying
Heating
Image Processing and Computer Vision
Laboratory equipment
Machine learning
Mathematical analysis
Neural networks
Original Article
Physical properties
Probability and Statistics in Computer Science
Training
Vegetable oils
Viscosity
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Summary: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 − 12.34095 ρ ) 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 − 17.93615 v ), 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.
ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-016-2233-8