Abstract
The increase globally fossil fuel consumption as it represents the main source of energy around the world, and the sources of heavy oil more than light, different techniques were used to reduce the viscosity and increase mobility of heavy crude oil. this study focusing on the experimental tests and modeling with Back Feed Forward Artificial Neural Network (BFF-ANN) of the dilution technique to reduce a heavy oil viscosity that was collected from the south- Iraq oil fields using organic solvents, organic diluents with different weight percentage (5, 10 and 20 wt.% ) of (n-heptane, toluene, and a mixture of different ratio toluene / n-Heptane) at constant temperature. Experimentally the higher viscosity reduction was about from 135.6 to 26.33 cP when the mixture of toluene/heptane (75/25 vol. %) was added. The input parameters for the model were solvent type, wt. % of solvent, RPM and shear rate, the results have been demonstrated that the proposed model has superior performance, where the obtained value of R was greater than 0.99 which confirms a good agreement between the correlation and experimental data, the predicate for reduced viscosity and DVR was with accuracy 98.7%, on the other hand, the μ and DVR% factors were closer to unity for the ANN model.
Highlights
The oil is the main nerve of the energy in the world, oil prices are changed depending on supply and demand, the heavy crude oil (HO) price is half the price of light oil attributed to contains high quantities of sulfur and heavy metals like nickel and vanadium, which is difficulties through production, transportation in the pipeline, (Hasan, et al, 2010)
(Tavakoli, et al, 2017), predicted the density of Athabasca bitumen – tetradecane mix, at different conditions using a radial basis function neural network (RBF-NN) technique, they conclude the proposed model is a suitable model for density forecasting of bitumen – tetradecane mix. (Eghtedaei, et al.,2017), presented accuracy calculating for viscosity reduction by proposed a radial artificial neural network function(RBF-Artificial Neural Networks (ANNs)) for relationship between heavy oil viscosity and the Athabasca bitumen mix, as a function of the temperature, pressure and weight% of tetradecane when comparing the obtained results with previous studies
The literature focuses on a prediction of the effect of n-heptane, toluene, and mixture from different volume percentages of toluene/n-heptane as dilutes solvents on the viscosity reduction of heavy oil using intelligent model Back Feed Forward Artificial Neural Network (BFFANN) looks to be rare
Summary
The oil is the main nerve of the energy in the world, oil prices are changed depending on supply and demand, the heavy crude oil (HO) price is half the price of light oil attributed to contains high quantities of sulfur and heavy metals like nickel and vanadium, which is difficulties through production, transportation in the pipeline, (Hasan, et al, 2010). (Eghtedaei, et al.,2017), presented accuracy calculating for viscosity reduction by proposed a radial artificial neural network function(RBF-ANN) for relationship between heavy oil viscosity and the Athabasca bitumen mix, as a function of the temperature, pressure and weight% of tetradecane when comparing the obtained results with previous studies.
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