.png){kind=icon}
返回
Subspace identification based inferential control applied to a continuous pulp digester
.png){kind=icon}
PRE
AI
DOI：10.1016/S0959-1524(99)00010-4.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The idea of constructing a data-driven stochastic system model through subspace identification for the purpose of inferential control is investigated. Various available methods for designing an inferential controller are discussed and their limitations are brought out, particularly in applications involving multi-variable processes. Practical issues that arise in identifying a system model geared toward inferential control using a subspace method are discussed. They include: handling of nonstationary disturbances, handling of multi-rate measurements/missing data, and secondary measurement selection. With the identified stochastic system model, a multi-rate Kalman filter can be designed and coupled with a model predictive controller. The method is applied to a continuous pulp digester, which is a complex distributed parameter system involving heterogeneous reactions. The application study indicates much potential for the data-based approach. (C) 1999 Published by Elsevier Science Ltd. Ail rights reserved.
Keyword:
inferential control
Kappa number control
continuous digester
Kamyr digester
subspace identification
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.9
论文数:
3.5K
被引数:
7.3K
机构
暂无机构信息
引用论文
The formation of the Late Cretaceous Xishan Sn–W deposit, South China: Geochronological and geochemical perspectives.png)
Lithos
LithosIF0
PREAI
分享收藏Preparation of a Zirconia-Based Ceramic Membrane and Its Application for Drinking Water Treatment氧化锆基陶瓷膜的制备及其在饮用水处理中的应用Symmetry
OAAI
SymmetryIF0
OAAI分享收藏分享收藏Mechanically stable tuning fork sensor with high quality factor for the atomic force microscopeScanningOAAI
ScanningIF0
OAAI分享收藏
查看更多内容