TY - JOUR
T1 - Estimating biofuel density via a soft computing approach based on intermolecular interactions
AU - Nabipour, Narjes
AU - Daneshfar, Reza
AU - Rezvanjou, Omid
AU - Mohammadi-Khanaposhtani, Mohammad
AU - Baghban, Alireza
AU - Xiong, Qingang
AU - Li, Larry K.B.
AU - Habibzadeh, Sajjad
AU - Doranehgard, Mohammad Hossein
N1 - Publisher Copyright:
© 2020 Elsevier Ltd
PY - 2020/6
Y1 - 2020/6
N2 - In this work, the density of biofuel is estimated using four intelligent models: a Least Square Support Vector Machine (LSSVM), a Radial Basis Function Artificial Neural Network (RBF-ANN), a Multi-layer Perceptron Artificial Neural Network (MLP-ANN), and an Adaptive Network-based Fuzzy Inference System (ANFIS). These models are used to estimate the density of biofuel based on intermolecular interactions and the van der Waals radii of the atoms. Various statistical analyses are performed on the original (experimental) and estimated data. It is found that the LSSVM model can provide more accurate predictions than the other three models. The R-squared value (R2) and the mean absolute relative error (MARE) for the LSSVM, RBF-ANN, MLP-ANN and ANFIS models are 0.847 & 0.056, 52.067 & 0.379, 57.385 & 0.371 and 65.096 & 0.678, respectively. This study shows that the LSSVM model is a promising tool for estimating the density of biofuel, offering an alternative to classic thermodynamic models.
AB - In this work, the density of biofuel is estimated using four intelligent models: a Least Square Support Vector Machine (LSSVM), a Radial Basis Function Artificial Neural Network (RBF-ANN), a Multi-layer Perceptron Artificial Neural Network (MLP-ANN), and an Adaptive Network-based Fuzzy Inference System (ANFIS). These models are used to estimate the density of biofuel based on intermolecular interactions and the van der Waals radii of the atoms. Various statistical analyses are performed on the original (experimental) and estimated data. It is found that the LSSVM model can provide more accurate predictions than the other three models. The R-squared value (R2) and the mean absolute relative error (MARE) for the LSSVM, RBF-ANN, MLP-ANN and ANFIS models are 0.847 & 0.056, 52.067 & 0.379, 57.385 & 0.371 and 65.096 & 0.678, respectively. This study shows that the LSSVM model is a promising tool for estimating the density of biofuel, offering an alternative to classic thermodynamic models.
KW - Adaptive network-based fuzzy inference system
KW - Biofuel
KW - Density
KW - Least square support vector machine
KW - Multi-layer perceptron artificial neural network
KW - Radial basis function artificial neural network
UR - https://www.webofscience.com/wos/woscc/full-record/WOS:000536949600089
UR - https://openalex.org/W3003293394
UR - https://www.scopus.com/pages/publications/85078920414
U2 - 10.1016/j.renene.2020.01.140
DO - 10.1016/j.renene.2020.01.140
M3 - Journal Article
SN - 0960-1481
VL - 152
SP - 1086
EP - 1098
JO - Renewable Energy
JF - Renewable Energy
ER -
