429630 Optimal Campaign Continuous Manufacturing

Tuesday, November 10, 2015: 3:40 PM
Salon E (Salt Lake Marriott Downtown at City Creek)
Ali M. Sahlodin, Process Systems Engineering Laboratory, Dept. of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA and Paul I. Barton, Process Systems Engineering Laboratory, Massachusetts Institute of Technology, Cambridge, MA

Campaign continuous manufacturing (CM), characterized by relatively short operational windows such as a few weeks [1], is being explored as an alternative to traditional batch-wise manufacturing in the pharmaceutical industry. However, optimal operation in campaign CM can be challenging due to the significance of startup and shutdown phases, which can negatively affect on-specification production and plant economy. In this work, the effectiveness and computational tractability of several known optimization approaches when applied to campaign CM are investigated. Inspired by the turnpike property [2,3] in optimal control, a new approach is then proposed, which aims to maximize on-specification production directly rather than minimizing the startup/shutdown times as commonly adopted in high-volume industries. A main contribution in the new approach is that the resulting optimization formulation is guaranteed to be differentiable, despite the underlying hybrid dynamic system [4]. Thus, it can be solved reliably using gradient-based algorithms. Case studies are presented to demonstrate the effectiveness of the proposed approach.

[1] Ali M. Sahlodin, Paul Barton. Optimal Campaign Continuous Manufacturing, submitted.

[2] McKenzie, L. (1976). Turnpike theory. Econometrica, 44, 841–865.

[3] Zaslavski, A. J. (2006). Turnpike Properties in the Calculus of Variations and Optimal Control. volume 80 of Nonconvex Optimization and Its Applications. Springer.

[4] Galán, S., Feehery, W. F., & Barton, P. I. (1999). Parametric sensitivity functions for hybrid discrete/continuous systems. Applied Numerical Mathematics, 31, 17 – 47.

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See more of this Session: Dynamic Simulation and Optimization II
See more of this Group/Topical: Computing and Systems Technology Division