Hybrid Model of Mass-Concentration Conservation and Neural Network for Zinc Leaching

被引：0
|
作者：
Zhou, Can [1 ]
Cheng, Cihang [1 ]
Huang, Keke [1 ]
Yang, Chunhua [1 ]
机构：
[1] Cent South Univ, Sch Automat, Changsha 410083, Peoples R China
来源：
JOM | 2023年 / 75卷 / 05期
基金：
中国国家自然科学基金;
关键词：
COPPER REMOVAL PROCESS; ASPEN PLUS; STRATEGY; BIOMASS; CSTR;
D O I：
10.1007/s11837-023-05755-0
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
To realize the optimal control of the zinc leaching process, an accurate mathematical model is necessary. However, the modeling of zinc leaching process faces the challenges of complex reaction rate, low data quality and error transmission of cascade modeling. To solve these problems, a hybrid model of mass-concentration conservation and neural network (MCCNN) is proposed. First, the method of integral regularization is introduced to process data and reduce the influence of measurement noise. Then, a model based on mass-concentration conservation mechanism model and data-driven model is proposed to build sub-models of each unit, which can solve the problems of insufficient data and complex reaction rate. Finally, a parallel error compensation structure is proposed to alleviate the error transfer caused by the cascade of sub-models. This hybrid framework provides a feasible and effective solution for zinc leaching process modeling, and its effect is verified by numerical simulation and zinc leaching process case.
引用
收藏
页码：1684 / 1694
页数：11
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