M. yari; Gh. Moradi; M. Abdolmaleki; Sh. Bashiri
Abstract
Biodiesel, as a renewable and environmentally friendly fuel, is a feasible alternative to fossil diesel, which has gained great popularity in recent years. However, due to some undesirable properties such as higher viscosity, biodiesel must be blended with diesel in order to be utilizable in a diesel ...
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Biodiesel, as a renewable and environmentally friendly fuel, is a feasible alternative to fossil diesel, which has gained great popularity in recent years. However, due to some undesirable properties such as higher viscosity, biodiesel must be blended with diesel in order to be utilizable in a diesel engine. Therefore, a reasonable approach is required for predicting the diesel-biodiesel blend properties. This study tries to estimate two substantial properties of blend, i.e. kinemattic viscosity (KV) and cetane number (CN), through neural network (NN) and empirical models which use pure properties of biodiesel (kinematic viscosity, boiling point, evaporation point, flash point, pour point, heat of combustion, cloud point, and specific gravity) as independent variables. In this regard, a three-layer feed-forward network with varying input parameters, training algorithms, transfer functions, and hidden neurons has been examined to predict the KV and CN of the diesel-biodiesel blend. Besides, the prediction capability of thirty empirical equations is investigated to determine the top equations describing blend properties. The result reveals that an ANN with three input parameters of biodiesel concentration (%), the CN of biodiesel, and biodiesel cloud point has the best prediction quality of CN with an R-value of 0.9961. Moreover, NN estimates the KV of blend with the highest correlation coefficient of 0.9985. The results corresponding to empirical equations also indicate that fractional-exponential equations are the best describer of the CN and KV of blend with R-values of 0.9947 and 0.9980, respectively.
Petroleum and Reservoir Engineering
A. Mohammadi Doust; M. Rahimi; M. Feyzi
Volume 13, Issue 1 , January 2016, , Pages 3-19
Abstract
In this study, response surface methodology (RSM) based on central composite design (CCD) was applied for investigation of the effects of ultrasonic waves, temperature and solvent concentration on viscosity reduction of residue fuel oil (RFO). Ultrasonic irradiation was employed at low frequency of 24 ...
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In this study, response surface methodology (RSM) based on central composite design (CCD) was applied for investigation of the effects of ultrasonic waves, temperature and solvent concentration on viscosity reduction of residue fuel oil (RFO). Ultrasonic irradiation was employed at low frequency of 24 kHz and power of 280 W. The results showed that the combination of ultrasonic waves and solvent injection caused to further reduce of viscosity. To obtain optimum conditions and significant parameters, the results were analyzed by CCD method. In this method, maximum viscosity reduction (133 cSt) was attained in ultrasonic irradiation for 5 min, temperature of 50 °C and acetonitrile volumetric concentration of 5 % by means of experimental and three dimensional response surface plots. The kinematic viscosity decreased from 494 cSt to 133 cSt at the optimum conditions. In addition, a multiple variables model was developed by RSM which the second-order effect of ultrasonic irradiation time was significant on viscosity reduction of FRO. Finally, a comparison between the RSM with artificial neural network (ANN) was applied. The results demonstrated that both models, , were powerful to predict of kinematic viscosity of RFO. The results demonstrated that both models, RSM and ANN, with R2 more than 0.99 were powerful to predict kinematic viscosity of RFO.