Batch-Wise Nonlinear Model Predictive Control of a Gas Antisolvent Recrystallization Process for the Uniform Production of Micronized HMX with Carbon Dioxide as the Antisolvent

Novel crystallization processes that use supercritical fluids have recently attracted considerable attention because they can overcome the problems associated with conventional crystallization processes. The gas antisolvent (GAS) process is one of the promising techniques and has been applied to many applications. However, control of the GAS process is a quite challenging problem and has not yet been studied due to the complex liquid–vapor equilibrium and particle formation kinetics. This work proposes a batch-wise nonlinear model predictive control (BNMPC) approach to the GAS process to obtain the desired particle size distribution (PSD) of HMX, a widely used explosive, which should be small and uniform for stability. Although a dynamic model of the GAS crystallization is required for BNMPC, the previously developed model is too complex for real-time applications. We propose a model simplification strategy for the conventional model using experimental data. We also employ a high-resolution method (HRM) to solve effectively a partial differential equation (PDE). The simulation results show that BNMPC can produce more uniform and smaller HMX particles.