Control Systems Center, School of Electrical and Electronic Engineering, The University of Manchester, Manchester, UK.
ISA Trans. 2014 Mar;53(2):584-90. doi: 10.1016/j.isatra.2013.12.020. Epub 2014 Jan 13.
Batch processes are commonly characterized by uneven trajectories due to the existence of batch-to-batch variations. The batch end-product quality is usually measured at the end of these uneven trajectories. It is necessary to align the time differences for both the measured trajectories and the batch end-product quality in order to implement statistical process monitoring and control schemes. Apart from synchronizing trajectories with variable lengths using an indicator variable or dynamic time warping, this paper proposes a novel approach to align uneven batch data by identifying short-window PCA&PLS models at first and then applying these identified models to extend shorter trajectories and predict future batch end-product quality. Furthermore, uneven batch data can also be aligned to be a specified batch length using moving window estimation. The proposed approach and its application to the control of batch end-product quality are demonstrated with a simulated example of fed-batch fermentation for penicillin production.
批处理过程通常具有不均匀的轨迹特征，这是由于批间变化的存在。批处理最终产品质量通常在这些不均匀轨迹的末尾进行测量。为了实施统计过程监测和控制方案，有必要对齐测量轨迹和批处理最终产品质量之间的时间差异。除了使用指示变量或动态时间扭曲来同步具有不同长度的轨迹之外，本文提出了一种通过首先识别短窗口 PCA&PLS 模型然后应用这些识别模型来扩展较短轨迹并预测未来批处理最终产品质量的新方法来对齐不均匀批处理数据。此外，还可以使用移动窗口估计将不均匀的批处理数据对齐为指定的批处理长度。所提出的方法及其在批处理最终产品质量控制中的应用通过青霉素生产的补料分批发酵的模拟示例进行了演示。
