Viscosities of some fatty acid esters and biodiesel fuels from a rough hard-sphere-chain model and artificial neural network

Hosseini, Sayed Mostafa; Pierantozzi, Mariano; Moghadasi, Jalil

doi:10.1016/j.fuel.2018.08.088

Cited by 41 publications

(13 citation statements)

References 54 publications

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“…In addition, an ANN represents a quick and efficient method to modeling the mathematical relationships between the independent and dependent variables of a process [25][26][27]. Moreover, using an ANN tool offers a number of advantages over the mechanistic models including the non-requirement of the mathematical description of the phenomena involved.…”

Section: Methodsmentioning

confidence: 99%

Viscosity–Temperature–Pressure Relationship of Extra-Heavy Oil (Bitumen): Empirical Modelling versus Artificial Neural Network (ANN)

et al. 2019

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The viscosity data of two heavy oil samples X and Y, with asphaltene contents 24.8% w/w and 18.5% w/w, respectively, were correlated with temperature and pressure using empirical models and the artificial neural network (ANN) approach. The viscosities of the samples were measured over a range of temperatures between 70 °C and 150 °C; and from atmospheric pressure to 7 MPa. It was found that the viscosity of sample X, at 85 °C and atmospheric pressure (0.1 MPa), was 1894 cP and that it increased to 2787 cP at 7 MPa. At 150 °C, the viscosity increased from 28 cP (at 0.1 MPa) to 33 cP at 7 MPa. For sample Y, the viscosity at 70 °C and 0.1 MPa increased from 2260 cP to 3022 cP at 7 MPa. At 120 °C, the viscosity increased from 65 cP (0.1 MPa) to 71 cP at 7 MPa. Notably, using the three-parameter empirical models (Mehrotra and Svrcek, 1986 and 1987), the correlation constants obtained in this study are very close to those that were previously obtained for the Canadian heavy oil samples. Moreover, compared to other empirical models, statistical analysis shows that the ANN model has a better predictive accuracy (R2 ≈ 1) for the viscosity data of the heavy oil samples used in this study.

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Section: Methodsmentioning

confidence: 99%

Viscosity–Temperature–Pressure Relationship of Extra-Heavy Oil (Bitumen): Empirical Modelling versus Artificial Neural Network (ANN)

et al. 2019

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“…Less neurons in the hidden layer will lead to underfitting and large error, while more neurons in the hidden layer will result in overfitting and time-consuming error. After investigation of the previous work, 32 the mean-square error (MSE) was chosen as the optimization objective during the training to get the best BPNN structure. The MSE is expressed aswhere y i ref and y i cal are the experimental value and calculated values of the output variable, respectively.…”

Section: Resultsmentioning

confidence: 99%

General Model Based on Artificial Neural Networks for Estimating the Viscosities of Oxygenated Fuels

et al. 2019

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Oxygenated fuel is a promising alternative fuel for engines because of the advantage of low emission. In this work, a general model based on back-propagation neural networks was developed for estimating the viscosities of different kinds of oxygenated fuels including esters, alcohols, and ethers, whose input variables are pressure, temperature, critical pressure, critical temperature, molar mass, and acentric factor. The viscosity data of 31 oxygenated fuels (1574 points) at temperatures ranging from 243.15 to 413.15 K and at pressures ranging from 0.1 to 200 MPa were collected to train and test the back-propagation neural network model. The comparison result shows that the predictions of the proposed back-propagation neural network model agree well with the experimental viscosity data of all studied oxygenated fuels using the general parameters (weight and bias). The average absolute relative deviations for training data, validation data, and testing data are 1.19%, 1.27%, and 1.30%, respectively.

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“…The performance of both semi-theoretical and ANN model was checked by predicting dynamic viscosities over the temperature range within 283-393 K and pressures up to 140 MPa with the average absolute relative deviation of 3.10% (for 648 data points) and 0.91% (for 796 data points), respectively. The ANN model developed herein, has been trained, validated and tested for the set of data gathered, pointing that the efficiency of the neural network model was found excellent on the entire dataset [10].…”

mentioning

confidence: 93%

Estimating Ternary Blends Properties using ANNs Trained with Binary Blends

Sandru¹,

Onutu²

2019

Rev. Chim.

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The present study aimed to estimate the physical properties of the diesel-diesel-biodiesel ternary blends, using artificial neural networks, also known as ANNs. The input data used to estimate the properties was the percentage in which each component was used to obtain the blend. Using two hydrofined diesel fuels from a local refinery and three biodiesel samples synthesized in the university laboratory, a total of 114 blends, both binary and ternary, were obtained. The ANN training database was comprised of exclusively 96 binary blends, from the total of 114. The predictions were made on the remaining 18 ternary blends. All the predictions were within the error mentioned in the standard, concluding the fact that the created ANNs had a rate of 100% accuracy.

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Viscosities of some fatty acid esters and biodiesel fuels from a rough hard-sphere-chain model and artificial neural network

Cited by 41 publications

References 54 publications

Viscosity–Temperature–Pressure Relationship of Extra-Heavy Oil (Bitumen): Empirical Modelling versus Artificial Neural Network (ANN)

Viscosity–Temperature–Pressure Relationship of Extra-Heavy Oil (Bitumen): Empirical Modelling versus Artificial Neural Network (ANN)

General Model Based on Artificial Neural Networks for Estimating the Viscosities of Oxygenated Fuels

Estimating Ternary Blends Properties using ANNs Trained with Binary Blends

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