Taking the axial heat conduction of wax deposition layer into account, a two-dimensional heat transfer model of calculating the temperature profile inside wax deposition layer was deduced and established based on the energy balance equation, the finite difference method was used to solve this model, and the influence of axial heat conduction on the distribution law of temperature profile inside the wax deposition layer under different boundary conditions and thickness were discussed. The results showed that: Temperature profile inside wax deposition layer in middle region of testing pipe section was mainly influenced by axial heat conduction under boundary conditions of constant wall temperature while the region near the inlet was mainly influenced under boundary conditions of variable wall temperature. With the increasing thickness of wax deposition layer, the influence of axial heat conduction became more conspicuous. All in all, in laboratory flow loop experiment of wax deposition, although axial heat conduction had an influence on the temperature profile inside wax deposition layer under different conditions, the influence was small, thus could be ignored.
A model for predicting wax deposition rate in pipeline transportation is constructed to predict wax deposition in actual pipeline, which can provide decision support for the flow guarantee of waxy crude oil in pipeline transportation. This paper analyzes the working principle of Back Propagation Neural Networks (BPNN). Aiming at the problems of BPNN model, such as over learning, long training time, low generalization ability and easy to fall into local minimum, the paper proposes an improved scheme of using Whale Optimization Algorithm (WOA) to optimize BPNN model(WOA-BPNN).Taking 38 groups of crude oil wax deposition experimental data in Huachi operation area as an example, the simulation calculation is carried out in MATLAB, and the Genetic Algorithm optimized BPNN(GA-BPNN) and the non Optimized BP neural network are used as comparative models for comparative analysis. The results show that the Mean Relative Error (MRE) of WOA-BPNN model in predicting wax deposition rate is 2.72% and the coefficient of determination(R 2 ) is 0.9966, which are better than those of BPNN and GA-BPNN models. It is proved that WOA-BPNN model has higher accuracy and robustness in predicting wax deposition rate.
The loop experiment device plays an important role in the study of wax deposition, and the calculation of the temperature distribution of the test section is the key to establish the wax deposition model. In the conditions of the wax deposition was not formed and constant wall temperature of the tube, the energy balance equation is solved by using separation of variables and combining the Kummer equation (S-K method), the distribution law of temperature in the test section is obtained, and the solution results was compared with Svendsen method, the difference between the results obtained by the two methods and the experimental results is also analyzed. The results showed that, the temperature distribution of the test section is consistent with the two methods, and the computing result of Svendsen method is generally higher than the S-K method. Under different axial distances for the take value, the maximum difference of computing results between the two methods is large when the position is farther away from the entrance, and the maximum difference is 0.27℃. Under different radial distances for the take value, with the increase of the axial distance, the difference between the results obtained by the two methods increases gradually in general, and the greater the radial distance, the greater the difference, the maximum difference is 0.27 ℃, thus the calculation results of these two kinds of methods have a higher coincide degree. The results obtained by the two methods are higher than the experimental results, but the difference is small, the results obtained by the S-K method are closer to the experimental results, and this method can avoid solve the numerical integration (Svendsen method) and the inconvenience of Bessel function, so it has a certain advantages.
In order to accurately obtain the wax deposition rate model, according to the kinetic principle of wax deposition, several factors affecting the wax deposition rate were selected, and by a optimization software of First Optimization(1stOpt), The parameters of two typical wax deposition rate models are solved respectively based on optimization algorithm combined by Levenberg-Marquardt (L-M) algorithm and global optimization and the calculated data were compared. The results show that: compared with the model parameters obtained by least squares method, the model parameters obtained by this optimization algorithm can describe the variation of wax deposition rate more accurately. The maximum error is reduced from 30% to 10%, and the average error is reduced from 10.3% to 2.42%; Alike, the mathematical model obtained by this optimization algorithm is also better than that solved by L-M algorithm alone. The maximum error is reduced from 13.62% to 11%, and the average error is reduced from 6.46% to 4.77%. To a certain extent, this optimization algorithm avoids the premature phenomenon caused by using Levenberg-Marquardt alone. In addition, the use of the optimization algorithm does not require suitable initial values, prior knowledge and programming, easy to use, and has important use value.
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