(647c) Fault Detection-Based Triggers for Rescheduling of Batch Process Operations

Touretzky, C., The University of Texas at Austin
Harjunkoski, I., ABB Corporate Research
Baldea, M., The University of Texas at Austin

The scheduling of batch process operations has been the subject of significant research in the past decades. The state task network (STN) and resource task network (RTN) formulations [1, 2] and their modifications can now be used to determine the sequence and duration of operations to execute within the production line in both sequential and more complex, flow shop setups. Handling disturbances and recomputing the production schedules in the presence of unforeseen events (e.g., arrival of urgent orders, unexpected changes in feed materials or operating conditions, and unit break-downs) is equally important. This challenge can be addressed in an “ab-initio” fashion via scheduling under uncertainty [3, 4]. Reactive approaches that correct the schedule upon the occurrence of an event (i.e., performing a rescheduling calculation) have also been proposed [5].

Rescheduling calculations must, however, be performed judiciously, as imposing frequent schedule changes leads to schedule “nervousness” [6] and possibly deleterious economic effects. This, in turn, calls for a careful choice of the rescheduling trigger, and requires the response to unforeseen events to find a balance between the economical optimum and potential upset on the process.

Rescheduling approaches discussed in the literature thus far are largely reactive, in the sense that rescheduling is only triggered once an event has affected system performance, and focus largely on events that affect directly the scheduling layer (e.g., new/rush order arrival or machine break-down). On the other hand, dealing with events that affect the process dynamics (i.e., disturbances affecting operation of one unit within the system, without leading to unit break-down) has received less attention, under the likely implicit assumption that their impact will be addressed by the local control system. However, recent studies have shown that accounting for process dynamics at the scheduling level can lead to significant benefits [7], and suggest that disturbances must be dealt with in an integrated fashion at both the scheduling and control levels.

Motivated by the above, in this work, we focus on integrating dynamics and control considerations in the scheduling by developing dynamics-based metrics and triggers for rescheduling calculations. In particular, we rely on an integrated scheduling and dynamic optimization calculation to develop reference trajectories for the process states and inputs. Subsequently, we compute the discrepancy between real-time data and these reference values.  Based on this, we show that deviations of the input (rather than state or output) trajectories of the process can be used for process fault detection and as a proactive trigger for rescheduling calculations. Furthermore, we illustrate that using these metrics enables us to detect issues during production before the end of an event, significantly mitigating the impact of potential deviation in, e.g., product quality.  We present a case study demonstrating these concepts using a flowshop example, and show that rescheduling of operations (whether right-shifting or complete rescheduling) in the presence of dynamic disturbances can lead to significant economic benefits.

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[2] C.C. Pantelides, "Unified frameworks for optimal process planning and scheduling." Proceedings on the second conference on foundations of computer aided operations. New York: Cache Publications, 1994.

[3] Z. Li and M.G. Ierapetritou. "Process scheduling under uncertainty: Review and challenges." Computers & Chemical Engineering 32:715-727, 2008.

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[5] J.P. Vin, and M.G. Ierapetritou. "A new approach for efficient rescheduling of multiproduct batch plants." Industrial & engineering chemistry research 39:4228-4238, 2000.

[6] G.E. Vieira, J.W. Herrmann, and E. Lin. "Rescheduling manufacturing systems: a framework of strategies, policies, and methods." Journal of scheduling 6:39-62, 2003.

[7] Y. Nie, L.T. Biegler, J.M. Wassick, “Integrated Scheduling and Dynamic Optimization of Batch Processes Using State Equipment Networks”, AIChE Jounal, 58(11):3416-3432, 2012.