With the continuous development of aluminum electrolysis production processes, traditional manual measurement methods can no longer meet the requirements for efficient and precise production. This study developed an industrial embedded measurement system based on an ARM processor to automatically measure the height and temperature of molten aluminum and electrolyte in aluminum electrolysis cells in real time while providing high-precision data feedback. The system integrates temperature, impedance, and displacement sensors, ensuring measurement stability and accuracy in complex electrolysis environments through multisensor fusion and optimized signal processing algorithms. To address interference factors such as strong magnetic fields and high temperatures present in aluminum electrolysis cells, the system employs electromagnetic compatibility techniques to ensure long-term stable operation under harsh conditions. Moreover, the embedded system features high scalability and flexibility, enabling customization for different electrolysis cell configurations and diverse application requirements. Experimental results demonstrate that the system achieves high accuracy and stability, effectively supporting real-time monitoring and precise control in the aluminum electrolysis production. This work provides a novel approach for intelligent aluminum production.

1. [1] G. Saevarsdottir, H. Kvande, and B. J. Welch, “Aluminum production in the times of climate change: The global challenge to reduce the carbon footprint and prevent carbon leakage,” JOM The J. of The Minerals, Metals and Materials Society, Vol.72, pp. 296-308, 2020. https://doi.org/10.1007/s11837-019-03918-6
2. [2] A. T. Tabereaux and R. D. Peterson, “Aluminum production,” Se. Seetharaman, R. Guthrie, A. McLean, Sri. Seetharaman, and H. Y. Sohn (Eds.), “Treatise on Process Metallurgy (Second Edition),” pp. 625-676, Elsevier, 2024. https://doi.org/10.1016/B978-0-323-85373-6.00004-1
3. [3] W. E. Haupin “Principles of aluminum electrolysis,” G. Bearne, M. Dupuis, and G. Tarcy (Eds.), “Essential Readings in Light Metals, Volume 2 Aluminum Reduction Technology,” Springer, pp. 3-11, 2016. http://doi.org/10.1007/978-3-319-48156-2_1
4. [4] Z. Zhang, X. Zhao, X. Bian et al., “Impedance measurement method for aluminum electrolysis under strong magnetic field interference,” Metallurgical Automation, Vol.43, No.4, pp. 69-72, 2019 (in Chinese).
5. [5] E. Balomenos, D. Panias, and I. Paspaliaris, “Energy and exergy analysis of the primary aluminum production processes: A review on current and future sustainability,” Mineral Processing and Extractive Metallurgy Review, Vol.32, Issue 2, pp. 69-89, 2011. https://doi.org/10.1080/08827508.2010.530721
6. [6] Y. Shi, Z. Wu, and H. Lu, “Development of aluminum electrolysis inspection robot technology and equipment,” Nonferrous Metal Design, Vol.50, No.2, pp. 26-29, 2023 (in Chinese).
7. [7] S. Zeng, Q. Zhang, Y. Zhang et al., “A method for measuring temperature and aluminum level in aluminum electrolysis process,” Beijing: CN103954320B, 2016-05-18, 2016 (in Chinese).
8. [8] J. Wang, “Multi-parameter detection system for aluminum electrolysis cell,” North China University of Technology, 2005 (in Chinese).
9. [9] A. Saleem, P. R. Underhill, D. Chataway et al., “Electromagnetic measurement of molten metal level in pyrometallurgical furnaces,” IEEE Trans. on Instrumentation and Measurement, Vol.69, No.6, pp. 3118-3125, 2020. https://doi.org/10.1109/TIM.2019.2929613
10. [10] M. Gao, Y. Liu, J. Huang et al., “Design of the automatic jacquard control system based on STM32F407,” 2014 Int. Conf. on Information Science, Electronics and Electrical Engineering (ISEEE), pp. 1143-1146, 2014. http://doi.org/10.1109/InfoSEEE.2014.6947849
11. [11] T. Li, F. Luan, M. Wang et al., “Design of remote monitoring system based on STM32F407 microcontroller,” 2019 IEEE Int. Conf. on Power, Intelligent Computing and Systems (ICPICS), pp. 304-307, 2019. http://doi.org/10.1109/ICPICS47731.2019.8942548
12. [12] Z. Wei, “Research on STM32-Based signal conditioning system measurement system,” Highlights in Science, Engineering and Technology, Vol.42, pp. 9-17, 2023. https://doi.org/10.54097/hset.v42i.7050
13. [13] B. R. Archambeault and J. Drewniak, “PCB Design for Real-World EMI Control,” Springer Science and Business Media, 2013.
14. [14] E. Y. Pujianti and L. H. Pratomo, “Implementation of the STM32F407 microcontroller based 5-level inverter,” Indonesian J. of Electrical and Electronics Engineering, Vol.5, No.2, pp. 46-49, 2022.
15. [15] F. U. Ahmed, Z. T. Sandhie, L. Ali et al., “A brief overview of on-chip voltage regulation in high-performance and high-density integrated circuits,” IEEE Access, Vol.9, pp. 813-826, 2020. https://doi.org/10.1109/ACCESS.2020.3047347
16. [16] S. Q. Jiang and Z. Y. Zhu, “Ship insulation monitoring and fault location system based on STM32,” 2020 IEEE 18th Int. Conf. on Industrial Informatics (INDIN), Vol.1, pp. 851-856, 2020. https://doi.org/10.1109/INDIN45582.2020.9442153
17. [17] A. Mohammad, R. Das, M. A. Islam et al., “Real-time operating systems (RTOS) for embedded systems,” Asian J. of Mechatronics and Electrical Engineering, Vol.2, No.2, pp. 95-104, 2023. https://doi.org/10.55927/ajmee.v2i2.7761
18. [18] L. Shangyang, D. Zhekang, L. Huipin et al., “STM32 and UCOSIII based engraving machine motion control system,” 2020 Chinese Control and Decision Conf. (CCDC), pp. 403-408, 2020. https://doi.org/10.1109/CCDC49329.2020.9164537
19. [19] H. Kopetz and W. Steiner, “Real-time Systems: Design Principles for Distributed Embedded Applications,” Springer Nature, 2022. https://doi.org/10.1007/978-3-031-11992-7
20. [20] X. Wang, R. D. Peterson, and A. T. Tabereaux, “Electrical conductivity of cryolitic melts,” G. Bearne, M. Dupuis, and G. Tarcy (Eds.) “Essential Readings in Light Metals,” Springer, pp. 57-64, 2016. https://doi.org/10.1007/978-3-319-48156-2_8