Yang Hui, Kuang Zhiqin, Zhu Jianyong, Xu Fangping, Nie Feiping, Sun Shuchen
School of Electrical and Electronic Engineering, East China Jiaotong University, Nanchang, 330013, China.
Key Labtotary of Advanced Control and Optimization of Jiangxi Province, Nanchang, 330013, China.
Sci Rep. 2022 Aug 30;12(1):14727. doi: 10.1038/s41598-022-19090-y.
Digital twin can be defined as a digital equivalent of an object of which it can mirror its behavior and status or virtual replicas of real physical entities in Cyberspace. To an extent, it also can simulate and predict the states of equipment or systems through smart algorithms and massive data. Hence, the digital twin is emerging used in intelligent manufacturing Systems in real-time and predicting system failure and also has introduced into a variety of traditional industries such as construction, Agriculture. Rare earth production is a typical process industry, and its Extraction Process enjoys the top priority in the industry. However, the extraction process is usually characterized by nonlinear behavior, large time delays, and strong coupling of various process variables. In case of failures happened in the process, the whole line would be shut down. Therefore, the digital twin is introduced into the design of process simulation to promote the efficiency and intelligent level of the Extraction Process. This paper proposes the techniques to build the rare earth digital twin such as soft measurement of component content, component content process simulation, control optimization strategy, and virtual workshop, etc. At the end, the validity of the model is verified, and a case study is conducted to verify the feasibility of the whole Digital twin framework.
数字孪生可以定义为一个对象的数字等效物，它能够反映其行为和状态，或者是网络空间中真实物理实体的虚拟副本。在一定程度上，它还可以通过智能算法和海量数据来模拟和预测设备或系统的状态。因此，数字孪生正在实时应用于智能制造系统中，用于预测系统故障，并且已经被引入到建筑、农业等各种传统行业。稀土生产是典型的流程工业，其提取过程在该行业中至关重要。然而，提取过程通常具有非线性行为、大时间延迟以及各种过程变量的强耦合特性。一旦过程中发生故障，整条生产线将被关闭。因此，将数字孪生引入到过程模拟设计中，以提高提取过程的效率和智能化水平。本文提出了构建稀土数字孪生的技术，如成分含量的软测量、成分含量过程模拟、控制优化策略和虚拟车间等。最后，验证了模型的有效性，并通过案例研究验证了整个数字孪生框架的可行性。
