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• W4210499471 endingPage "123422" @default.
• W4210499471 startingPage "123422" @default.
• W4210499471 abstract "• The kinematic viscosity of the fuel oil was firstly estimated by using Machine Learning Methods. • Estimation was performed by using physical properties of the fuel oil as input data. • Estimated results obtained by machine learning methods was found compatible in terms of MRE. 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 the predictions were compared using the Mean Relative Error (MRE) and Mean Squared Error (MSE). As a result, it was seen that all of the methods discussed provide predictive ability in accordance with standard values and the best prediction data is provided by using ELM." @default.
• W4210499471 created "2022-02-08" @default.
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• W4210499471 date "2022-05-01" @default.
• W4210499471 modified "2023-10-03" @default.
• W4210499471 title "Kinematic viscosity estimation of fuel oil with comparison of machine learning methods" @default.
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• W4210499471 doi "https://doi.org/10.1016/j.fuel.2022.123422" @default.
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