Data-Driven Quality Control of Batch Processes via Subspace Identification

In this work we present a novel, data-driven, quality modeling and control approach for batch processes. Specifically, we adapt subspace identification methods for use with batch data to identify a state-space model from available process measurements and input moves. We demonstrate that the resulting LTI, dynamic, state-space model is able to describe the transient behavior of finite duration batch processes. Next, we relate the terminal …