Sugar Precipitation Control of Chinese Medicine Solution Based on Nonlinear Model Predictive

This paper illustrates the benefits of a nonlinear model-based predictive control approach applied to a sugar precipitation process for Chinese medicine solution. This relevant approach proposes set 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 (CSD). A neural network model is used as an internal model to predict process outputs. An optimization problem is solved to compute future control actions taking into account real-time control objectives. The performance of the proposed control strategy, which apply to sucrose and glucose precipitation constitutes a real novelty, is tested via simulation in cases of set point tracking. The results reveal a significant improvement in terms of precipitation efficiency.