Stage-based Variable Sampling Period Modeling and On-line Monitoring Strategy for Uneven-length Batch Processes
Batch processes are often characterized by uneven-length durations and multistage characteristics.To reflect the inherent stage nature to improve the performances of process monitoring,simultaneously considering dynamic characteristics within the process variables for some complicated cases,stage-based variable sampling period multi-model dynamic principal component analysis(VSP-MDPCA)modeling and on-line monitoring method is developed in this paper.Batch process is firstly divided into several stages by feature point(FP)extraction method.In the extraction of feature points,wavelet de-nosing is firstly used to prevent noise interference.For each uneven-length stage,process data are sampled with variable sampling period according to reaction intensity.Then dynamic time warping(DTW)algorithm is used to align the trajectory of each stage.DPCA model is built for each stage.The proposed method was used to detect faults in the fed-batch penicillin production.The simulation results clearly demonstrate the advantages of the proposed approach in comparison to MPCA.
Batch processes VSP-MDPCA Fed-batch penicillin production
Yajun Wang Zhizhong Mao Mingxing Jia Jing Bai
College of Information Science and Engineering,Northeastern University,Shenyang 110004;College of El College of Information Science and Engineering,Northeastern University,Shenyang 110004 College of Information Science and Engineering,Northeastern University,Shenyang 110004;College of El
国际会议
长沙
英文
4501-4506
2014-05-31（万方平台首次上网日期，不代表论文的发表时间）