Self-optimizing Control
Assembly Line
Global self-optimizing control of batch processes
This work considers to achieve near-optimal operation for a class of batch processes by employing self-optimizing control (SOC). Comparing with a continuous one, a batch process exhibits stronger nonlinearity with dynamics because of the non-steady operation condition. This necessitates a global version of SOC to achieve satisfactory performance. Meanwhile, it also makes the existing global SOC (gSOC) not directly applicable to batch processes due to the causality amongst variables. Therefore, it is necessary to extend the original gSOC to batch processes. In addition to the nonconvexity challenge of the original gSOC problem, the new extension for batch processes has to face even more challenges. Particularly, the causality due to dynamics of batch processes brings in structural constraints on controlled variables (CVs), making a CV selection problem even more difficult. To address these challenges, the gSOC problem is recast in a vectorized formulation and it is proved that the structural constraints considered are linear in the vectorized formulation. Moreover, a novel shortcut method is proposed to efficiently find sub-optimal but more transparent solutions for this problem.
There are two main types of online optimization technologies for batch processes, namely run-to-run/batch-to-batch optimization and within-batch optimization. The former is based on the repetitiveness of the batch process to iteratively update batch-to-batch operations. Optimization actions for the next batch are carried out upon the completion of the previous batch, aiming to achieve incremental performance improvements throughout the iterations. Iterative Learning Control (ILC) is a widely recognized method in this field. However, a shortcoming of this type of approaches is its failure to account for uncertainties that arise within a single batch process. As a result of such uncertainties, the behavior of the same process can vary from one batch to another, rendering knowledge learned from one batch inapplicable to another. This limitation makes ILC approaches unsuitable for such processes.