Open Access

ARTICLE

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

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

1 The Second Oil Production Plant, Sinopec Northwest Oilfield Company, Urumqi, 830011, China
2 Yingmaili Oil and Gas Production Management Area, PetroChina Tarim Oilfield Company, Korla, 841001, China
3 College of Petroleum Engineering, Xi’an Shiyou University, Xi’an, 710065, China

* Corresponding Author: Zhuo Chen. Email:

Energy Engineering 2024, 121(4), 1007-1026. https://doi.org/10.32604/ee.2023.045270

Received 22 August 2023; Accepted 23 November 2023; Issue published 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 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.

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