Lee Dae Sung, Vanrolleghem Peter A
Department of Applied Mathematics, Biometrics and Process Control, Ghent University, Coupure Links 653, B-9000 Gent, Belgium.
Biotechnol Bioeng. 2003 May 20;82(4):489-97. doi: 10.1002/bit.10589.
Multiway principal component analysis (MPCA) for the analysis and monitoring of batch processes has recently been proposed. Although MPCA has found wide applications in batch process monitoring, it assumes that future batches behave in the same way as those used for model identification. In this study, a new monitoring algorithm, adaptive multiblock MPCA, is developed. The method overcomes the problem of changing process conditions by updating the covariance structure recursively. A historical set of operational data of a multiphase batch process was divided into local blocks in such a way that the variables from one phase of a batch run could be blocked in the corresponding blocks. This approach has significant benefits because the latent variable structure can change for each phase during the batch operation. The adaptive multiblock model also allows for easier fault detection and isolation by looking at the relationship between blocks and at smaller meaningful block models, and it therefore helps in the diagnosis of the disturbance. The proposed adaptive multiblock monitoring method is successfully applied to a sequencing batch reactor for biological wastewater treatment.
最近提出了用于间歇过程分析和监测的多向主成分分析（MPCA）。尽管MPCA在间歇过程监测中得到了广泛应用，但它假定未来批次的行为与用于模型识别的批次相同。在本研究中，开发了一种新的监测算法——自适应多块MPCA。该方法通过递归更新协方差结构克服了过程条件变化的问题。多相间歇过程的一组历史操作数据被划分为局部块，使得批次运行某一阶段的变量可以被划分到相应的块中。这种方法具有显著优势，因为在间歇操作期间，每个阶段的潜在变量结构可能会发生变化。自适应多块模型还通过查看块之间的关系以及更小的有意义的块模型，便于进行故障检测和隔离，因此有助于干扰诊断。所提出的自适应多块监测方法成功应用于生物废水处理的序批式反应器。
