Predictive model of mixed oil length for sequential transportation of multi-product pipeline by combining mechanism and Gaussian mixture regression algorithm

被引：4

作者：

Yuan Z. ^{[1
,2
]}

Liu G. ^{[1
,2
]}

Chen L. ^{[1
,2
]}

Shao W. ^{[2
]}

Zhang Y. ^{[3
]}

机构：

[1] College of Pipeline and Civil Engineering in China University of Petroleum(East China), Qingdao

[2] Shandong Provincial Key Laboratory of Oil & Gas Storage and Transportation Safety, Qingdao

[3] Qingdao Operation Area, Shandong Branch, PipeChina, Qingdao

来源：

Zhongguo Shiyou Daxue Xuebao (Ziran Kexue Ban)/Journal of China University of Petroleum (Edition of Natural Science) | 2023年 / 47卷 / 02期

关键词：

Gaussian mixture regression; local modeling; mechanism-data; mixed oil length; multi-product pipeline;

D O I：

10.3969/j.issn.1673-5005.2023.02.014

中图分类号：

学科分类号：

摘要：

The oil mixing phenomenon occurs during the sequential transportation of the multi-product pipeline, and the accurate prediction of the length of the mixed oil is of great significance for the cutting batch segment. The mechanism model is faced with problems such as low accuracy and complex numerical simulation. In the current global predictive models derived from machine learning algorithms, the multi-mode characteristics of actual operating conditions are ignored, thus the predictive accuracy is limited. The Gaussian mixture regression algorithm cannot accurately characterize the complex nonlinear relationship among variables if it is directly introduced to identify the data mode. Based on the existing mechanism equation and the Gaussian mixture regression algorithm, we develop a local modeling algorithm that integrates the mechanism knowledge. Based on the real product oil pipeline sequential transportation mixed oil length data set, a comparison experiment among different models was carried out, and the results show that the mechanism and local modeling algorithm can effectively characterize the functional relationship of variables, and the predictive accuracy of the new model has obvious advantages. © 2023 University of Petroleum, China. All rights reserved.

引用

页码：123 / 128

页数：5

共 22 条

[1] HE Guoxi, LIN Mohan, WANG Baoying, Et al., Experimental and numerical research on the axial and radial concentration distribution feature of miscible fluid interfacial mixing process in products pipeline for industrial applications[J], International Journal of Heat & Mass Transfer, 127, pp. 728-745, (2018)
[2] SHAHANDEH H, LI Z., Modeling and optimization of the upgrading and blending operations of oil sands Bitumen, Energy & Fuels, 30, pp. 5202-5213, (2016)
[3] MORADI S, MIRHASSANI S A., Robust scheduling for multi-product pipelines under demand uncertainty [ J], The International Journal of Advanced Manufacturing Technology, 87, 9, pp. 2541-2549, (2016)
[4] CAFARO V G, CAFARO D C, MENDEZ CA., Optimization model for the detailed scheduling of multi-source pipelines, Computers & Industrial Engineering, 88, pp. 395-409, (2015)
[5] AUSTIN J E, PALFREY J R., Mixing of miscible but dissimilar liquids in serial flow in a pipeline, P Mech Eng B-J Eng, 178, pp. 377-389, (1963)
[6] SUN Jianfei, LIANG Yongtu, Research progress on the mixed oil models for the batch transportation in products pipeline [ J], Oil & Gas Storage and Transportation, 38, 5, pp. 496-502, (2019)
[7] XIA Zengyan, LIU Qingquan, Numerical simulation of the contamination between batches in multi-product pipeline transport, Mechanics in Engineering, 32, 6, pp. 13-17, (2010)
[8] MA Gang, BAI Rui, Study on the two-dimensional mixed oil theory numerical analysis for product oil pipelines, Oil-Gas Field Surface Engineering, 37, 7, pp. 54-59, (2018)
[9] WU Yuguo, Research on technology of the batch transportation of cold and hot crude oils, (2010)
[10] HE Yurong, SONG Zhichao, ZHANG Yanming, Et al., Review on application of machine learning in hydraulic fracturing, Journal of China University of Petroleum (Edition of Natural Science), 45, 6, pp. 127-135, (2021)

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