Model predictive control of a Chinese medicine sugar precipitation process

This paper illustrates the benefits of a nonlinear model-based predictive control approach applied to a sugar precipitation process for Chinese medicine mixed solution. This relevant approach proposes sets point tracking for the crystal mass/ concentration couple. In this purpose, a model dedicated to the stage crystallization is designed, without consideration of crystal size distribution. A neural network model is used as an internal model to predict process output. An optimization problem is addressed to compute future control action taking into consideration real-time control objective. The performance of the proposed control strategy, which applies to sucrose-glucose precipitation constitutes a real novelty, is tested via simulation in cases of set point tracking. The results reveal a good performance in terms of precipitation efficiency.