基于WSDPC-RVR的多模态间歇过程软测量方法  

Multimode batch process soft sensor method based on WSDPC-RVR

作　　者：王喆 王建林[1] 李季 周新杰 随恩光 WANG Zhe;WANG Jianlin;LI Ji;ZHOU Xinjie;SUI Enguang(College of Information Science and Technology,Beijing University of Chemical Technology,Beijing 100029,China)

机构地区：[1]北京化工大学信息科学与技术学院,北京100029

出　　处：《化工学报》2023年第11期4656-4669,共14页CIESC Journal

基　　金：国家自然科学基金项目(61973025)。

摘　　要：间歇过程的多模态特性使得未考虑模态因素建立的软测量模型预测精度较低,现有的间歇过程模态划分方法对初始参数敏感且未考虑异常数据对模态划分结果的影响,其不合理的划分结果是制约多模态间歇过程软测量模型预测精度提升的一个重要因素。提出了一种基于密度加权和相似标签分配密度峰值聚类相关向量回归(weighted destiny and similar label allocation density peaks clustering-relevance vector regression, WSDPC-RVR)的多模态间歇过程软测量方法。首先,以不同数据点的密度贡献程度对低密度区域数据点的局部密度进行加权,准确选取聚类中心,并引入ε近邻结合数据点间的距离与局部密度构建剩余数据点的分配策略;然后,定义模态评价指标并分析不同模态的统计特性,构建异常模态判别策略获取有效模态数量,完成间歇过程模态划分;最后,建立各有效模态的RVR软测量模型,实现间歇过程主导变量的在线预测。青霉素发酵过程的仿真实验结果表明,所提方法能够实现合理的模态划分,有效地提高了软测量模型的预测精度。The multimode characteristics of batch processes make the soft sensor model without considering mode factors have low prediction accuracy.The existing batch processes mode partitioning methods are sensitive to initial parameters and do not consider the influence of abnormal data on the mode partitioning results.The unreasonable partitioning results are an important factor restricting the improvement of the prediction accuracy of the multimode batch process soft sensor model.In this paper,a soft sensor method for multimode batch processes based on weighted destiny and similar label allocation density peaks clustering-relevance vector regression(WSDPC-RVR)is proposed.First,the local density of data points in low density areas is weighted according to the density contribution of different data points,the cluster center is accurately selected,and the e-nearest neighbor is introduced to combine the distance between data points and the local density to construct an allocation strategy for the remaining data points.Then,the mode evaluation index is defined and the statistical characteristics of different modes are analyzed,and the abnormal mode discrimination strategy is constructed to obtain the number of effective modes and complete the mode partitioning of batch processes.Finally,the RVR soft sensor model of each effective mode is established to realize the online prediction of the dominant variables of batch processes.The simulation results of penicillin fermentation process show that the proposed method can achieve reasonable mode partitioningand effectively improve the prediction accuracy of the soft sensor model.

关 键 词：间歇式 密度峰值聚类 模态划分 模型 发酵 

分 类 号：TP274[自动化与计算机技术—检测技术与自动化装置]