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 depositi...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.展开更多
This paper investigated the effects of pre-heating treatment temperatures(T_(pre))on the flowability and wax deposition characteristics of a typical waxy crude oil after adding wax inhibitors.It is found that there is...This paper investigated the effects of pre-heating treatment temperatures(T_(pre))on the flowability and wax deposition characteristics of a typical waxy crude oil after adding wax inhibitors.It is found that there is little difference in wax precipitation exothermic characteristics of crude oils at different T_(pre),as well as the wax crystal solubility coefficient in the temperature range of 25-30℃.For the undoped crude oil,the flowability after wax precipitation gets much improved and the wax deposition is alleviated as T_(pre)increasing.At T_(pre)=50℃,the viscosity and wax deposition rate of crude oil adding wax inhibitors are higher than those of the undoped crude oil.When the T_(pre)increases to 60,70,and 80℃,the flowability of the doped crude oil are largely improved and the wax deposition is suppressed with the T_(pre)increase,but the wax content of wax deposit increases gradually.It is speculated that,on the one hand,the T_(pre)increase helps the dispersion of asphaltenes into smaller sizes,which facilitates the co-crystallization with paraffin waxes and generates more aggregated wax crystal flocs.This weakens the low-temperature gel structure and increases the solid concentration required for the crosslink to form the wax deposit.On the other hand,the decrease in viscosity increases the diffusion rate of wax molecules and accelerates the aging of wax deposits.The experimental results have important guiding significance for the pipeline transportation of doped crude oils.展开更多
Composition and molecular mass distribution of n-alkanes in asphaltenes of crude oils of different ages and in wax deposits formed in the borehole equipment were studied. In asphaltenes, n-alkanes from C12 to C60 were...Composition and molecular mass distribution of n-alkanes in asphaltenes of crude oils of different ages and in wax deposits formed in the borehole equipment were studied. In asphaltenes, n-alkanes from C12 to C60 were detected. The high molecular weight paraffins in asphaltenes would form a crystalline phase with a melting point of 80–90 ℃. The peculiarities of the redistribution of high molecular paraffin hydrocarbons between oil and the corresponding wax deposit were detected. In the oils, the high molecular weight paraffinic hydrocarbons C50–C60were found, which were not practically detected in the corresponding wax deposits.展开更多
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 ...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.展开更多
Wax deposits on the wall of a crude oil pipeline are a solid wax network of fine crystals, filled with oil, resin, asphaltene and other impurities. In this paper, a series of experiments on wax deposition in a laborat...Wax deposits on the wall of a crude oil pipeline are a solid wax network of fine crystals, filled with oil, resin, asphaltene and other impurities. In this paper, a series of experiments on wax deposition in a laboratory flow loop were performed under different conditions （flow rate, temperature differential between crude oil and pipeline wall, and dissolved wax concentration gradient）, and the wax deposits were analyzed, so quantitative relationships among wax content, wax appearance temperature （WAT）, shear stress, and radial concentration gradient of dissolved wax at the solid/liquid interface were obtained. Finally, a model was established to predict WAT and the wax content of the deposit.展开更多
文摘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.
基金The authors thank the financial support from the National Natural Science Foundation of China(51904327,U19B2012)China University of Petroleum Innovation Project(22CX06050A).
文摘This paper investigated the effects of pre-heating treatment temperatures(T_(pre))on the flowability and wax deposition characteristics of a typical waxy crude oil after adding wax inhibitors.It is found that there is little difference in wax precipitation exothermic characteristics of crude oils at different T_(pre),as well as the wax crystal solubility coefficient in the temperature range of 25-30℃.For the undoped crude oil,the flowability after wax precipitation gets much improved and the wax deposition is alleviated as T_(pre)increasing.At T_(pre)=50℃,the viscosity and wax deposition rate of crude oil adding wax inhibitors are higher than those of the undoped crude oil.When the T_(pre)increases to 60,70,and 80℃,the flowability of the doped crude oil are largely improved and the wax deposition is suppressed with the T_(pre)increase,but the wax content of wax deposit increases gradually.It is speculated that,on the one hand,the T_(pre)increase helps the dispersion of asphaltenes into smaller sizes,which facilitates the co-crystallization with paraffin waxes and generates more aggregated wax crystal flocs.This weakens the low-temperature gel structure and increases the solid concentration required for the crosslink to form the wax deposit.On the other hand,the decrease in viscosity increases the diffusion rate of wax molecules and accelerates the aging of wax deposits.The experimental results have important guiding significance for the pipeline transportation of doped crude oils.
文摘Composition and molecular mass distribution of n-alkanes in asphaltenes of crude oils of different ages and in wax deposits formed in the borehole equipment were studied. In asphaltenes, n-alkanes from C12 to C60 were detected. The high molecular weight paraffins in asphaltenes would form a crystalline phase with a melting point of 80–90 ℃. The peculiarities of the redistribution of high molecular paraffin hydrocarbons between oil and the corresponding wax deposit were detected. In the oils, the high molecular weight paraffinic hydrocarbons C50–C60were found, which were not practically detected in the corresponding wax deposits.
文摘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.
文摘Wax deposits on the wall of a crude oil pipeline are a solid wax network of fine crystals, filled with oil, resin, asphaltene and other impurities. In this paper, a series of experiments on wax deposition in a laboratory flow loop were performed under different conditions （flow rate, temperature differential between crude oil and pipeline wall, and dissolved wax concentration gradient）, and the wax deposits were analyzed, so quantitative relationships among wax content, wax appearance temperature （WAT）, shear stress, and radial concentration gradient of dissolved wax at the solid/liquid interface were obtained. Finally, a model was established to predict WAT and the wax content of the deposit.
