摘要
In the operation and control of chemical processes, automatic data logging systems generate large volumes of data. It is important for supervising daily operation how to make use of the valuable information about normal and abnormal operation, significant disturbance and changes in operational and control strategies. In this paper, principal component analysis(PCA)is clarified from the view of space, and every different subspace represents different operational mode and process performance. Based on that, the distance between two subspaces is calculated to evaluate the difference between them. The method is illustrated by a case study of a fluid catalytic cracking unit(FCCU) reactor-regenerator system.
In the operation and control of chemical processes, automatic data logging systems generate large volumes of data. It is important for supervising daily operation how to make use of the valuable information about normal and abnormal operation, significant disturbance and changes in operational and control strategies. In this paper, principal component analysis(PCA)is clarified from the view of space, and every different subspace represents different operational mode and process performance. Based on that, the distance between two subspaces is calculated to evaluate the difference between them. The method is illustrated by a case study of a fluid catalytic cracking unit(FCCU) reactor-regenerator system.
出处
《化工学报》
EI
CAS
CSCD
北大核心
2004年第1期151-154,共4页
CIESC Journal
基金
国家高技术研究发展计划资助项目 (No 2 0 0 1AA413 110 )~~
关键词
主元分析
统计过程控制
空间距离
工况识别
催化裂化
principal component analysis, statistical process control, space distance, process performance region identification, FCCU
