(629a) Real Time Constrained Model Predictive Control for the Continuous Manufacturing of Pharmaceuticals
- Conference: AIChE Annual Meeting
- Year: 2018
- Proceeding: 2018 AIChE Annual Meeting
- Group: Computing and Systems Technology Division
- Time: Thursday, November 1, 2018 - 8:00am-8:19am
This research presents an approach to control system design for the continuous production of pharmaceuticals. First, a dynamic model of the continuous drug manufacturing process is developed that leads to a system of nonlinear, differential algebraic equations. This model is then linearized around typical process operating conditions. The linearized model is subsequently discretized and is used to develop a real time online model predictive control system that ensures the integrity of the pharmaceutical production process and helps satisfy the stringent FDA requirements typically required for safe drug production. Specifically, a constrained infinite time linear quadratic regulator control strategy is employed that can satisfy hard constraints in real time. For example, if there is variability in the quality of raw materials, the controller can ensure, in real time, that FDA requirements on drug purity are met.
The proposed approach is demonstrated with a case study based on a continuous Ibuprofen manufacturing process, as developed for steady-state conditions by Jolliffe & Gerogiorgis (2014), and references therein. In this work, a dynamic model of this process is developed, linearized, and controlled as described above.