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Spray drying is a widely used unit operation in synthesis of particles for production of chemicals, food, and pharmaceuticals. However, accurate control and development of this unit remains an elusive task because of the complex interactions of variables and phenomena. In this paper, we adopt and present a dynamic model of the complete drying process for a laboratory spray dryer. The dynamic mathematical model, which integrates atomization, evaporation, and particle formation models, is described by mass and energy balances. The model can predict temperature, residual moisture, and particle size of the produced powder. Model predictions are verified through datasets collected from a lab-scale spray dryer. A data-driven model based on the dynamic model is produced to interface with the control strategy. A model predictive control (MPC) strategy is then adopted to address the highly cross-coupled effects among different components of the dryer and guarantee desired product quality measures. Successful MPC implementation on the drying system is addressed, including trajectory tracking and disturbance rejection scenarios.

Integrating a dynamic multiscale drying model into a closed loop control strategy for laboratory spray dryer