Prediction Model of Wax Deposition Rate in Waxy Crude Oil Pipelines by Elman Neural Network Based on Improved Reptile Search Algorithm

A hard problem that hinders the movement of waxy crude oil is wax deposition in oil pipelines.To ensure the safe operation of crude oil pipelines,an accurate model must be developed to predict the rate of wax deposition in crude oil pipelines.Aiming at the shortcomings of the ENN prediction model,which easily falls into the local minimum value and weak generalization ability in the implementation process,an optimized ENN prediction model based on the IRSA is proposed.The validity of the new model was confirmed by the accurate prediction of two sets of experimental data on wax deposition in crude oil pipelines.The two groups of crude oil wax deposition rate case prediction results showed that the average absolute percentage errors of IRSA-ENN prediction models is 0.5476% and 0.7831%,respectively.Additionally,it shows a higher prediction accuracy compared to the ENN prediction model.In fact,the new model established by using the IRSA to optimize ENN can optimize the initial weights and thresholds in the prediction process,which can overcome the shortcomings of the ENN prediction model,such as weak generalization ability and tendency to fall into the local minimum value,so that it has the advantages of strong implementation and high prediction accuracy.

A prediction method for the wax deposition rate based on a radial basis function neural network

The radial basis function neural network is a popular supervised learning tool based on machinery learning technology.Its high precision having been proven,the radial basis function neural network has been applied in many areas.The accumulation of deposited materials in the pipeline may lead to the need for increased pumping power,a decreased flow rate or even to the total blockage of the line,with losses of production and capital investment,so research on predicting the wax deposition rate is significant for the safe and economical operation of an oil pipeline.This paper adopts the radial basis function neural network to predict the wax deposition rate by considering four main influencing factors,the pipe wall temperature gradient,pipe wall wax crystal solubility coefficient,pipe wall shear stress and crude oil viscosity,by the gray correlational analysis method.MATLAB software is employed to establish the RBF neural network.Compared with the previous literature,favorable consistency exists between the predicted outcomes and the experimental results,with a relative error of 1.5%.It can be concluded that the prediction method of wax deposition rate based on the RBF neural network is feasible.

The Stability Criterion Model and Stability Analysis of Waxy Crude Oil Pipeline Transportation System Based on Excess Entropy Production

Based on the theory of non-equilibrium thermodynamics, considering the dynamic effect of molecular diffusion and the change in thermodynamic parameters caused by wax precipitation, the phenomenological relations of different thermodynamic "force" and "flow" interactions were derived. The corresponding thermodynamic model of a waxy crude oil pipeline transportation system was built, and then, the excess entropy production expression was proposed. Furthermore, the stability criterion model of the pipeline transportation system was established on the basis of Lyapounov stability theory. Taking the oil pipeline in Daqing oilfield as an example, based on the four parameters of out-station temperature, out-station pressure, flow rate and water content, the stable and unstable regions of the system were divided, and the formation mechanisms of the two different regions were analyzed. The experimental loop device of wax deposition rate was designed, and then, the wax deposition rate under the four parameters was measured. The results showed that the stable region of the wax deposition rate fluctuation was basically in accordance with the stability region analyzed by the criterion model established in this paper, which proved that the stability criterion model was feasible for analyzing the stability of the waxy crude oil pipeline transportation process.