Model predictive control with inline parameter adaptation for direct crystal growth rate regulation

Abstract In batch cooling crystallization, many interactive factors, including supersaturation, reactor dimensions, and operating conditions, govern crystal growth and significantly influence product properties. Variables such as temperature or refractive index are used as surrogate control variables but have limitations in capturing growth rate complexity. Model predictive control can account for process dynamics and multiple variables but requires real‐time growth rate estimates. In this study, a growth kinetics‐based MPC with an inline single‐crystal growth rate sensor was implemented for glycine, enabling inline adaptation of kinetic parameters and supporting stable closed‐loop performance. The growth rate of the proxy crystal was verified by scanning sampled crystal populations using micro‐computed tomography. Results indicate a physical limitation of constant‐growth‐rate control: Increasing supersaturation during cooling can promote nucleation and approach cooling capacity limits. The proposed MPC substantially reduces the need for retuning under changing process conditions and allows parameter adaptation, making it generalizable to other crystallization systems.