Nonlinear model predictive control of crystal size in batch cooling crystallization processes

被引：2
|
作者：
Wang, Liangyong [1 ]
Zhu, Yaolong [1 ]
Gan, Chenyang [1 ]
机构：
[1] Northeastern Univ, State Key Lab Integrated Automation Proc Ind, Shenyang 110819, Peoples R China
来源：
JOURNAL OF PROCESS CONTROL | 2023年 / 128卷
基金：
中国国家自然科学基金;
关键词：
Cooling crystallization; Crystal size; Image analysis; Deep learning; Path following control; Nonlinear model predictive control; PARTICLE; SHAPE; IDENTIFICATION; SEGMENTATION; SENSOR;
D O I：
10.1016/j.jprocont.2023.103020
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
The paper proposes a model-based nonlinear model predictive control (NMPC) method for online control of crystal mean size and standard deviation in cooling crystallization process. Image analysis method using deep learning neural network and mathematical statistical method are performed to obtain the mean size and standard deviation of crystal population. The nonlinear prediction model for the NMPC is derived from the input and output data. The effectiveness of the proposed NMPC method is evaluated by the alum cooling crystallization experiments. Experimental results demonstrate the benefits of the proposed combination of image analysis and feedback control of the crystal mean size and standard deviation. The control performance of NMPC is superior to model-free path following control (PFC) method due to the prediction and optimization capabilities of NMPC. & COPY; 2023 Elsevier Ltd. All rights reserved.
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页数：9
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