Zhuo Chen^1,*, Ningning Wang², Wenbo Jin³, Dui Li¹

Energy Engineering, Vol.121, No.4, pp. 1007-1026, 2024, DOI:10.32604/ee.2023.045270 - 26 March 2024

Abstract 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… More > Graphic Abstract

Ruiqiong Wang¹, Tiantian Du², Jinchen Cao^2,*, Guoqiang Wang³

Frontiers in Heat and Mass Transfer, Vol.22, No.1, pp. 129-139, 2024, DOI:10.32604/fhmt.2024.047825 - 21 March 2024

Abstract With the advancement of oilfield extraction technology, since oil-water emulsions in waxy crude oil are prone to be deposited on the pipe wall, increasing the difficulty of crude oil extraction. In this paper, the mesoscopic dissipative particle dynamics method is used to study the mechanism of the crystallization and deposition adsorbed on the wall. The results show that in the absence of water molecules, the paraffin molecules near the substrate are deposited on the metallic surface with a horizontal morphology, while the paraffin molecules close to the fluid side are arranged in a vertical column More >

Qi Zhuang^a,* , Zhuo Chen^b, Dong Liu^c, Yangyang Tian^d

Frontiers in Heat and Mass Transfer, Vol.19, pp. 1-8, 2022, DOI:10.5098/hmt.19.19

Abstract In order to improve the accuracy and efficiency of wax deposition rate prediction of waxy crude oil in pipeline transportation, A GRA-IPSO-ELM model was established to predict wax deposition rate. Using Grey Relational Analysis (GRA) to calculate the correlation degree between various factors and wax deposition rate, determine the input variables of the prediction model, and establish the Extreme Learning Machine (ELM) prediction model, improved particle swarm optimization (IPSO) is used to optimize the parameters of ELM model. Taking the experimental data of wax deposition in Huachi operation area as an example, the prediction performance More >

Zhen Tian^a,*, Wenbo Jin^a, Lei Wang^b, Zhi Jin^c

Frontiers in Heat and Mass Transfer, Vol.5, pp. 1-8, 2014, DOI:10.5098/hmt.5.5

Abstract 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 More >