Prediction and Online Control for Process Parameters of Vanadium Nitrogen Alloys Production Based on Digital Twin

Zhe Wang, Zifeng Xu, Zenggui Gao, Keqi Zhang and Lilan Liu ()
Additional contact information
Zhe Wang: Shanghai Key Laboratory of Intelligent Manufacturing and Robotics, School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China
Zifeng Xu: Shanghai Key Laboratory of Intelligent Manufacturing and Robotics, School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China
Zenggui Gao: Shanghai Key Laboratory of Intelligent Manufacturing and Robotics, School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China
Keqi Zhang: Shanghai Key Laboratory of Intelligent Manufacturing and Robotics, School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China
Lilan Liu: Shanghai Key Laboratory of Intelligent Manufacturing and Robotics, School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China

Sustainability, 2024, vol. 16, issue 17, 1-21

Abstract: The production of vanadium nitrogen alloys (VNs) is a chemical reaction process carried out in a closed pusher plate kiln, making real-time monitoring of key parameters challenging. Traditional methods for controlling process parameters are insufficient to meet the demands of production control. And the current production line heavily depends on workers’ experience and operates with a relatively low level of automation. In order to solve the above problems, this paper proposes a method for monitoring, predicting, and online controlling the production process parameters of VNs based on digital twins. Firstly, the process parameter affecting quality in the production process is experimentally selected as the target for prediction and control. Then, the ISSA-GRNN (Improved Sparrow Search Algorithm-Generalized Regression Neural Networks) fusion prediction model is constructed to predict the optimal values and intervals for the process parameter of movement interval. Finally, a digital twin system is developed to integrate the fusion prediction model to achieve real-time monitoring and online control of the production line. And the superiority of the algorithm and the feasibility of online control are verified through experiments. This paper achieves accurate prediction and online control of parameters in the VNs production process and has reduced reliance on workers’ production experience. Additionally, it has effectively lowered energy consumption and failure rates, facilitated the transition from traditional kiln production to intelligent production, and thereby supported sustainable development.

Keywords: process parameters prediction; digital twin; VNs production line; online control system; machine learning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/16/17/7545/pdf (application/pdf)
https://www.mdpi.com/2071-1050/16/17/7545/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:16:y:2024:i:17:p:7545-:d:1468121

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().