Modeling and identification for soft sensor systems based on the separation of multi-dynamic and static characteristics  

作　　者：Pengfei Cao Xionglin Luo Xiaohong Song 

机构地区：[1]College of Electrical Engineering and Automation,Shandong University of Science and Technology,Qingdao 266590,China [2]Research Institute of Automation,China University of Petroleum,Beijing 102249,China [3]State Grid Shandong Electric Power Company Zibo Power Supply Company,Zibo 255000,China

出　　处：《Chinese Journal of Chemical Engineering》2018年第1期137-143,共7页中国化学工程学报（英文版）

基　　金：Supported by the Scientific Research Foundation of Shandong University of Science and Technology for Recruited Talents(2016RCJJ046);the National Basic Research Program of China(2012CB720500)

摘　　要：Data-driven soft sensor is an effective solution to provide rapid and reliable estimations for key quality variables online. The secondary variables affect the primary variable in considerably different speed, and soft sensor systems exhibit multi-dynamic characteristics. Thus, the first contribution is improving the model in the previous study with multi-time-constant. The characteristics-separation-based model will be identified in substep way,and the stochastic Newton recursive(SNR) algorithm is adopted. Considering the dual-rate characteristics of soft sensor systems, the proposed model cannot be identified directly. Thus, two auxiliary models are first proposed to offer the intersample estimations at each update period, based on which the improved algorithm(DAM-SNR) is derived. These two auxiliary models function in switching mechanism which has been illustrated in detail. This algorithm serves for the identification of the proposed model together with the SNR algorithm, and the identification procedure is then presented. Finally, the laboratorial case confirms the effectiveness of the proposed soft sensor model and the algorithms.Data-driven soft sensor is an effective solution to provide rapid and reliable estimations for key quality variables online. The secondary variables affect the primary variable in considerably different speed, and soft sensor systems exhibit multi-dynamic characteristics. Thus, the first contribution is improving the model in the previous study with multi-time-constant. The characteristics-separation-based model will be identified in substep way, and the stochastic Newton recursive （SNR） algorithm is adopted. Considering the dual-rate characteristics of soft sensor systems, the proposed model cannot be identified directly. Thus, two auxiliary models are first proposed to offer the intersample estimations at each update period, based on which the improved algorithm （DAM-SNR） is derived. These two auxiliary models function in switching mechanism which has been illustrated in detail. This algorithm serves for the identification of the proposed model together with the SNR algorithm, and the identification procedure is then presented. Finally, the laboratorial case confirms the effectiveness of the proposed soft sensor model and the algorithms.

关 键 词：soft sensor Modeling Characteristics separation System identification Double auxiliary models 

分 类 号：TP212[自动化与计算机技术—检测技术与自动化装置] TP301[自动化与计算机技术—控制科学与工程]