Abstract
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.
Highlights
Batch/semibatch processing plays a significant role in the production of low-volume, high valueadded products such as specialty polymers, pharmaceuticals and fine chemicals
Due to the complexity of batch processes, it is usually difficult to develop mechanistic models based on physicochemical principles
This paper has studied an approach to align uneven batch trajectories and the corresponding batch end-product quality values
Summary
Batch/semibatch processing plays a significant role in the production of low-volume, high valueadded products such as specialty polymers, pharmaceuticals and fine chemicals. An alternative approach is to predict the future batch end-product quality values at the synchronized batch duration time for shorter batches using a pre-determined model Such a model can be identified using the method proposed in [26], where a series of created pseudo batches are synchronized to their batch endpoints and a PLS model is identified from the synchronized pseudo batch data. As the synchronized pseudo batch data come from various windows of batch runs, the identified PLS model is essentially a moving-window PLS model It can be used for predicting future batch end-product quality as long as the process dynamics does not change for the time period that the moving windows cover.
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