Fast Model Predictive Control of Modular Systems for Continuous Manufacturing of Pharmaceuticals

In this chapter, methodologies are described for the plant-wide optimization and control of nonlinear modular systems for continuous-flow pharmaceutical manufacturing. First-principles models for such modular systems are formulated as partial differential-algebraic equations (PDAEs). The spatial discretization of PDAEs, to enable their numerical solution with standard solvers, usually results in a very high number of states. Quadratic dynamic matrix control (QDMC) is an implementable approach for the real-time control of systems with very high state dimension, by using a linear input-output model so that the resulting optimization does not depend on the number of states. To address the strong nonlinearities in these modular systems, linear input-output model construction methodologies are described that can provide good closed-loop performance. Additionally, dynamic optimization of dynamical plant operations such as startup is formulated as a nonlinear program. The dynamic optimization determines optimal trajectories which are used as setpoints for QDMC to control the plant. Results are presented for a computational case study for the upstream synthesis of atropine in a modular continuous-flow system. Dynamic optimization and closed-loop simulation results are presented for plant operations under various disturbance and time-invariant parametric uncertainty scenarios.