Subspace identification for data‐driven modeling and quality control of batch processes

In this Chapter, a novel data-driven, quality modeling and control approach for batch processes is presented. Specifically, subspace identification methods are adapted for use with batch data to identify a state-space model from available process measurements and input moves. The resulting LTI, dynamic, state-space model is shown to be able to describe the transient behavior of finite duration batch processes. Next, the terminal quality is related to the terminal value of the identified states. Finally, the resulting model is applied in a shrinking-horizon, model predictive control scheme to directly control terminal product quality. The theoretical properties of the proposed approach are studied and compared to state-of-the-art latent variable control approaches. The efficacy of the proposed approach is demonstrated through a simulation study of a batch polymethyl methacrylate (PMMA) polymerization reactor. Results for both disturbance rejection and set-point changes (that is, new quality grades) are demonstrated.

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