首页|Deploying artificial neural network to predict hybrid biodiesel fuel properties from their fatty acid compositions
Deploying artificial neural network to predict hybrid biodiesel fuel properties from their fatty acid compositions
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Measurement-related problems have spurred fuel properties prediction using machine learning techniques.Improved fuel properties offered by hybrid biodiesel (HB) via mixed oils were predicted from theirfatty acid compositions (FACs) using artificial neural network (ANN). FACs and fuel properties of HB sourcedfrom the literature were used to develop ANN models. FAC data were used as the input parameters topredict the fuel properties data (kinematic viscosity (KV), density, calorific value (CV), and flash point (FP))considered as the output parameters of the models. Using the multilayer perception ANN, the models weretrained using Levenberg-Marquardt back propagation learning algorithm coupled with different numbersof neurons and activation functions for the prediction of the fuel properties. The models were observed toaccurately predict these fuel properties with high prediction accuracy (R~2 = 1). The evaluated model performanceerrors were 0.1014 and 0.0504, 0.2905 and 0.4225, 0.1848, and 0.1038, and 0.4726 and 0.7833 forKV, density, CV, and FP using root mean square error and average absolute deviation respectively. Predictionperformance and error estimates were slightly better than those for single feedstock biodiesel. Hence,this study shows the ability of ANN to accurately predict the fuel properties of HB from the Fas.
Solomon O. Giwa、Samson A. Aasa、Raymond T. Taziwa、Mohsen Sharifpur
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Department of Applied Sciences, Faculty of Science Engineering and Technology,Walter Sisulu University, Mthatha, South Africa
Department ofMechanical Engineering, Olabisi Onabanjo University, Ibogun, Nigeria
Department of Mechanical and Aeronautical Engineering, University ofPretoria, Pretoria, South Africa||Department of Medical Research, China Medical University Hospital, China Medical University, Taichung, Taiwan
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