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[1]刘刚,袁子云,孙庆峰,等.成品油顺序输送管道混油信息软测量方法[J].油气储运,2024,43(12):1413-1425.[doi:10.6047/j.issn.1000-8241.2024.12.009]
　LIU Gang,YUAN Ziyun,SUN Qingfeng,et al.Research on soft-sensing for oil mixing information in batching transport pipelines of product oils[J].Oil & Gas Storage and Transportation,2024,43(12):1413-1425.[doi:10.6047/j.issn.1000-8241.2024.12.009]

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成品油顺序输送管道混油信息软测量方法

《油气储运》[ISSN:1000-8241/CN:13-1093/TE] 卷: 43 期数: 2024年12期页码: 1413-1425 栏目: 运行与管理出版日期: 2024-12-25

Title:: Research on soft-sensing for oil mixing information in batching transport pipelines of product oils

文章编号:: 1000-8241（2024）12-1413-13

作者:: 刘刚¹; 袁子云¹; 孙庆峰²; 陈雷¹; 李苗³; 潘元昊¹; 吴昱辰¹; 王子涵¹; 1.中国石油大学（华东）储运与建筑工程学院 ? 山东省油气储运安全重点实验室;2.山东港源管道物流有限公司;3.国家管网集团华南分公司

Author(s):: LIU Gang¹; YUAN Ziyun¹; SUN Qingfeng²; CHEN Lei¹; LI Miao³; PAN Yuanhao¹; WU Yuchen¹; WANG Zihan¹; 1.College of Pipeline and Civil Engineering, China University of Petroleum (East China)//Shandong Provincial Key Laboratory of Oil & Gas Storage and Transportation Safety; 2.Shandong Gangyuan Pipeline Logistics Company; 3.PipeChina South China Company

关键词:: 成品油; 顺序输送管道; 混油; 软测量; 后行油品密度; 界面定位; 浓度分布; 智慧物流

Keywords:: product oils; batch transport pipeline; oil mixing; soft-sensing; density of trailing oil; interface positioning; concentration distribution; smart logistics

分类号:: TE832

DOI:: 10.6047/j.issn.1000-8241.2024.12.009

文献标志码:: A

摘要:: 【目的】混油控制是成品油顺序输送管道亟需解决的难题之一，混油信息则是优化顺序输送管道运行效率的基础数据。现场安装传感器的监测结果存在偏差，且无法满足现场提前获取混油信息的需求；纯数据驱动模型建立的混油软测量方法未考虑管输工艺与仪表测量特性，预测精度欠佳。【方法】通过分析顺序输送管道的管输工艺与测量仪表的监测过程，提出融合物理认知的变分贝叶斯高斯混合回归模型，并结合新一维混油浓度演化模型，构建顺序输送管道混油信息软测量方法：基于站场“硬”传感器（即真实传感器）获取的管输运行参数，建立表征顺序输送混油信息变化规律的高精度“软”传感器，其主要功能是实现混油界面的定位、后行油品密度测量值的预测、混油界面密度分布信息的预测。【结果】新建软测量方法具备更好的适用性，混油界面到站时间预测误差较传感器测量方法减少了76.2％，后行油品测量密度预测均方根误差（Root Mean Square Error,RMSE）较纯数据驱动软测量方法降低了41.4％，混油界面密度分布预测曲线RMSE低于0.9kg/m3，且计算耗时少于30s。【结论】融合物理认知与数据的顺序输送管道混油信息软测量方法可实现混油信息的高效预测，辅助现场操作人员掌握管输批次状态，优化油品批次管理，为顺序输送智慧物流提供技术支撑。（图6，表5，参17）

Abstract:: [Objective] Oil mixing control is identified as one of the urgent challenges to be addressed for the batching transport pipelines of product oils. Accurate oil mixing information provides essential data for optimizing the operational efficiency of these pipelines. However, monitoring results obtained from on-site sensors often fall short of meeting the field need for access to oil mixing information in advance, primarily due to deviations. Predictions made using soft-sensing methods based on purely data-driven models tend to exhibit low accuracy, as they do not consider the complexities of the pipeline transportation process and the characteristics of instrument measurements. [Methods] This paper presents a variational Bayesian Gaussian mixture regression model incorporating physical cognition, developed from an analysis of the pipeline transportation process and the monitoring process of measurement instruments along batching transport pipelines. Additionally, it proposes a soft-sensing method for oil mixing information of these pipelines, by introducing a novel one-dimensional oil mixing concentration evolution model. Based on the operational parameters of pipeline transportation acquired by physical sensors mounted at stations and yards, high-precision “soft” sensors are developed to characterize the variation patterns of oil mixing information in batch pipelining. They are designed primarily to position oil mixing interfaces, predict the density measurements of trailing oils, and predict the density distributions at oil mixing interfaces, ultimately enabling the accurate prediction of oil mixing information. [Results] The proposed soft-sensing technique demonstrated improved applicability. The predicted arrival times of oil mixing interfaces at stations exhibited an error reduction of 76.2％, compared with the method based on sensor measurements. The predicted density measurements for trailing oils showed a 41.4％ fall in Root Mean Square Error (RMSE) compared with the purely data-driven soft-sensing method. The curves for predicated density distributions at oil mixing interfaces remained below 0.9 kg/m?. Furthermore, the computation time for these predictions was less than 30 s. [Conclusion] The soft-sensing method for oil mixing information in batch transport pipelines facilitates efficient predictions by integrating physical cognition with data. This approach provides on-site operators with more accurate information to identify the batch status in pipeline transportation and optimize the batch management of oils. Consequently, this technique serves as a technical support system for smart logistics in batch pipelining. (6 Figures, 5 Tables, 17 References)

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备注/Memo

刘刚，男，1975年生，教授，博士生导师，2004年博士毕业于中国石油大学（华东）油气储运工程专业，现主要从事油气管道系统数据挖掘与智能决策的应用研究工作。地址：山东省青岛市黄岛区长江西路66号，266580。电话：0532-86981819。Email：liugang@upc.edu.cn
基金项目：国家自然科学基金面上项目“基于机理-数据融合的管输混油拖尾信息识别理论方法研究”，52174068；国家重点研发计划“机器学习与混合整数规划融合的复杂油气管网优化新算法及工程应用”，2021YFA1000104-1；中央高校自主创新项目“大型油气管网仿真与运行优化软件研发”，22CX01001A-5。
● Received: 2024-06-28● Revised: 2024-08-06● Online: 2024-09-11

更新日期/Last Update: 2024-12-25