基于分布式贝叶斯隐马尔可夫回归的动态软测量建模方法  

Dynamic soft sensor modeling method based on distributed Bayesian hidden Markov regression

作　　者：邵伟明[1] 韩文学[1] 宋伟[2] 杨勇 陈灿[2] 赵东亚[1] SHAO Weiming;HAN Wenxue;SONG Wei;YANG Yong;CHEN Can;ZHAO Dongya(College of New Energy,China University of Petroleum,Qingdao 266580,Shandong,China;SINOPEC Qingdao Refining&Chemical Co.,Ltd.,Qingdao 266500,Shandong,China;Technical Detection Center,Shengli Oil Field of SINOPEC,Dongying 257000,Shandong,China)

机构地区：[1]中国石油大学(华东)新能源学院,山东青岛266580 [2]中国石化青岛炼化分公司,山东青岛266500 [3]中国石油化工股份有限公司胜利油田分公司技术检测中心,山东东营257000

出　　处：《化工学报》2023年第6期2495-2502,共8页CIESC Journal

基　　金：国家自然科学基金项目(62173344,61973315)。

摘　　要：利用软测量技术实时预测化工过程中的关键参数对生产过程的在线监测、自动控制、实时优化具有十分重要的意义。为此,提出了一种基于隐马尔可夫模型的动态软测量建模方法。首先,针对数据规模大导致模型计算效率低和数据缺失导致数据无法充分利用的问题,提出了一种基于分布式贝叶斯隐马尔可夫回归的预测模型;其次,针对该模型进一步提出了一种能够获得精确后验分布的分布式训练方法。最后,利用蜡油加氢过程对所提方法的有效性进行了验证。Real-time prediction of key parameters in the chemical process by using soft sensing technology is of great significance for on-line monitoring,automatic control,and real-time optimization of production process.Therefore,a dynamic soft sensor modeling method based on hidden Markov model is proposed.Firstly,aiming at the problem of low computational efficiency caused by large data scales and insufficient utilization of data due to missing data,a predictive model based on distributed Bayesian hidden Markov regression is proposed.Then,a distributed training method that can obtain accurate posterior distribution is proposed for model training.Finally,the effectiveness of the proposed model is verified by the wax oil hydrogenation process.

关 键 词：化工过程 动态建模 贝叶斯隐马尔可夫回归 软测量 分布式计算 

分 类 号：TK314[动力工程及工程热物理—热能工程]