一种新型的基于Levenshtein距离层次聚类的时序操作优化方法

现代流程工业过程中,DCS采集并存储了大量的操作时序数据,若能将其中有价值的操作经验和操作信息提取出来,则可大大提高操作系统的性能。然而,操作经验概念较为模糊,无法具体量化。因此,将具有时序特征的操作数据符号化,使操作经验以区块化形式表示,并提出一种基于Levenshtein距离的时序层次凝聚聚类算法,通过对操纵变量的历史时序操作数据进行相似性搜索,进而获得多种相似的操作模式,并将每种类型的操作模式对应的过程变量进行性能分析,从而得到并保存实际工作过程中所需的操作经验,以达到生产过程操作优化的目的。为了验证所提出方法,将其用于连续组分精馏操作过程,实验结果表明所提出的基于Levenshtein距离层次聚类的操作优化方法的有效性。

New operation optimization method with time series based on Levenshtein distance hierarchical clustering

In the modern process industry process, DCS collects and stores a large amount of operational temporal data. If valuable operational experience and operational information can be extracted, the performance of the operating system can be greatly improved. However, operational experience is vague and cannot be quantified by value. Therefore, the operational data with time series is symbolized so that the operational experience is represented in a block form. And we propose a hierarchical clustering algorithm based on Levenshtein distance for time series. By clustering of historical operational data in the time series of variables, a variety of similar operating modes are obtained, and the process variables corresponding to the type of operation mode perform performance analysis to obtain and preserve the operational experience required in the actual work process, thereby guiding the process operation of production. In order to verify the proposed method, it is applied to the continuous multi component distillation operation process. The results show the effectiveness of the proposed method.