  Given their increasing participation in fast-changing markets, the
integration of scheduling and control is an important consideration in chemical
process operations. This generally involves computing optimal production
schedules using dynamic models, which is challenging due to the nonlinearity
and high-dimensionality of the models of chemical processes. In this paper, we
begin by observing that the intrinsic dimensionality of process dynamics (as
relevant to scheduling) is often much lower than the number of model state
and/or algebraic variables. We introduce a data mining approach to "learn"
closed-loop process dynamics on a low-dimensional, latent manifold. The
manifold dimensionality is selected based on a tradeoff between model accuracy
and complexity. After projecting process data, system identification and
optimal scheduling calculations can be performed in the low-dimensional,
latent-variable space. We apply these concepts to schedule an air separation
unit under time-varying electricity prices. We show that our approach reduces
the computational effort, while offering more detailed dynamic information
compared to previous related works.
