Incorporating automation logic in online chemical production scheduling

In this paper, we address the problem of incorporating knowledge of the automation system in a chemical plant into the online scheduling problem. Optimization models for online scheduling necessarily omit some of the plant dynamics to ensure sufficiently fast solution times for use in online rescheduling. This can result in the computed schedules not being feasible when executed in the plant. To overcome this difficulty, we propose to use online analysis of a formal model of the automation logic to detect these infeasible schedules and avoid them when rescheduling. Model checking is applied to an abstraction of the automation system's dynamics to detect infeasible schedules, and a state-space resource task network scheduling model is used to account for the associated delay information. We demonstrate the techniques using an illustrative running example, and show the utility of the integrated approach using a case study involving multiple batch reactions.