Comparison of empirical equations and artificial neural network results in terms of kinematic viscosity prediction of fuels based on hazelnut oil methyl ester

The blasting method is one of the best hard rock excavation methods in mining activities. This method has negative impacts, one of which is the vibrations generated by the residual energy of the explosion. This impact will affect the environment around the blasting area, both slope stability, tunnels, infrastructure, and human settlements if it is close to the blasting site. Therefore, it needs initial planning and prediction to anticipate the blasting vibration that occurs. In general, the blast vibration can be predicted using the scale distance method which uses two parameters, namely the maximum amount of explosive material per time delay and the distance of measurement from the location of the explosion. This method has been widely researched to produce several empirical equations from each explosion location studied. However, as technology develops, several studies have tried to use artificial intelligence technology, one of which is the artificial neural network algorithm as a new approach for predicting detonation vibrations. In this method, the development of the parameters used in predicting the weighting of the most influential parameters from the formation of detonation vibrations can be carried out. This study will review several studies related to the use of artificial neural networks in predicting blasting vibrations in the studies that have been carried out and also compare with prediction methods using several empirical equations.

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Viscosity prediction by computational method and artificial neural network approach: The case of six refrigerants

The Journal of Supercritical Fluids ◽

10.1016/j.supflu.2013.04.017 ◽

2013 ◽

Vol 81 ◽

pp. 67-78 ◽

Cited By ~ 16

Author(s):

Forouzan Ghaderi ◽

Amir Hosein Ghaderi ◽

Bijan Najafi ◽

Noushin Ghaderi

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Computational Method ◽

Network Approach ◽

Neural Network Approach ◽

Viscosity Prediction ◽

Artificial Neural ◽

Artificial Neural Network Approach

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Adaptive Neuro-Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN) for Predicting the Kinematic Viscosity and Density of Biodiesel-Petroleum Diesel Blends

American Journal of Computer Science and Technology ◽

10.11648/j.ajcst.20180101.12 ◽

2018 ◽

Vol 1 (1) ◽

pp. 8 ◽

Cited By ~ 3

Author(s):

Youssef Kassem

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Fuzzy Inference System ◽

Kinematic Viscosity ◽

Fuzzy Inference ◽

Inference System ◽

Neuro Fuzzy ◽

Artificial Neural ◽

Artificial Neural Network Ann

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Artificial Neural Network Models for Prediction of Density and Kinematic Viscosity of Different Systems of Biofuels and Their Blends with Diesel Fuel. Comparative Analysis

Kemija u industriji ◽

10.15255/kui.2019.053 ◽

2020 ◽

Vol 69 (7-8) ◽

pp. 355-364

Author(s):

Souad Belmadani ◽

Mabrouk Hamadache ◽

Cherif Si-Moussa ◽

Maamar Laidi ◽

Salah Hanini

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Diesel Fuel ◽

Kinematic Viscosity ◽

Binary Systems ◽

Correlation Coefficients ◽

Network Models ◽

Neural Network Models ◽

Experimental Database ◽

Artificial Neural

In the present article, two models based on the artificial neural network methodology (ANN) have been optimised to predict the density (ρ) and kinematic viscosity (μ) of different systems of biofuels and their blends with diesel fuel. An experimental database of 1025 points, including 34 systems (15 pure systems, 14 binary systems, and 5 ternary systems) was used for the development of these models. These models use six inputs, which are temperature (T) in the range of −10 – 200 °C, volume fractions (X1, X2, X3) in the range of 0–1, and to distinguish these systems, we used kinematic viscosity at 20 °C in the range of 0.67–74.19 mm2 s–1 and density at 20 °C in the range of 0.7560–0.9188 g cm–3. The best results were obtained with the architecture of {6-26-2: 6 neurons in the input layer – 26 neurons in the hidden layer – 2 neurons in the output layer}. Results of comparison between experimental and simulated values in terms of the correlation coefficients were: R2 = 0.9965 for density, and R2 = 0.9938 for kinematic viscosity. A 238 new database experimental of 4 systems (2 pure systems, 1 binary system, and 1 ternary system) was used to check the accuracy of the two ANN models previously developed. Results of prediction performances in terms of the correlation coefficients were: R2 = 0.9980 for density, and R2 = 0.9653 for kinematic viscosity. Comparison of validation results with those of the other studies shows that the neural network models gave far better results.

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