Author/Authors :
He,Dakuo College of Information Science and Engineering - Northeastern University, China , Wang, Zhengsong College of Information Science and Engineering - Northeastern University, China , Yang, Le College of Information Science and Engineering - Northeastern University, China , Liu, Tongshan College of Information Science and Engineering - Northeastern University, China , Yao, Yao College of Information Science and Engineering - Northeastern University, China , Mao, Zhizhong College of Information Science and Engineering - Northeastern University, China
Abstract :
Optimized control of the drug extraction production process (DEPP) aims to reduce production costs and improve economic benefit while meeting quality requirements. However, optimization of DEPP is hampered by model uncertainty. Thus, in this paper, a strategy that considers model uncertainty is proposed. Mechanistic modeling of DEPP is first discussed in the context
of previous work. The predictive model used for optimization is then developed by simplifying the mechanism. Optimization for a single extraction process is first implemented, but this is found to lead to serious wastage of herbs. Hence, the optimization of a multiextraction process is then conducted. To manage the uncertainty in the model, a data-driven iterative learning control method
is introduced to improve the economic benefit by adjusting the operating variables. Finally, fuzzy parameter adjustment is adopted to enhance the convergence rate of the algorithm. The effectiveness of the proposed modeling and optimization strategy is validated through a series of simulations.
