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Metallurgist
Published in: Metallurgist 9-10/2023

25-02-2023

Automatic System for Detecting Visible Emissions in a Potroom of Aluminum Plant Based on Technical Vision and a Neural Network

Authors: A. K. Shestakov, P. A. Petrov, M. Yu. Nikolaev

Published in: Metallurgist | Issue 9-10/2023

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Abstract

Electrolysis of a cryolite-alumina melt is accompanied by emission of harmful pollutants into the atmosphere: hydrogen fluorides, perfluorocarbons, fluorine (gaseous and solid), tarry matters, benzopyrene. Workers of the potroom receive the highest dose of harmful emissions, which leads to a multiple risk of developing serious occupational diseases, including lung cancer. For these reasons, ensuring the environmental safety of production is an important and relevant task, which also corresponds to the goals of sustainable development of the aluminium production industry.
The appearance of smoke in the potroom signals a malfunction of the gas treatment system, depressurization of aluminum reduction cell, disruptions of the anode carbon or occurrence of the anode effect. Such situations require immediate actions of the staff. The objective of the research is to improve the monitoring quality and reduce the emissions of pollutants into the environment, which are generated as a result of the cryolite-alumina melt electrolysis. To improve the emissions detection rate, an automatic system for monitoring the visible emissions of pollutants in the atmosphere of the potroom has been developed. The development is a vision system based on a convolutional neural network. The neural network was trained using the TensorFlow machine learning software and a dataset of labeled photographs of the model of the aluminum reduction cell with different smoke variations. The results of neural network testing show that the overall accuracy of the trained model was 94.75%. It proves that the model can be used to detect smoke and visible emissions of pollutants. The developed automatic monitoring system allows increasing the detection rate of an emergency situation by 2 times compared to traditional systems based on gas sensors, reducing the time for aluminium reduction cells depressurization and, consequently, reducing emissions of pollutants into the environment.
previous article Simulation of the Thermal Regime of Semi-Continuous Casting of Aluminum Alloy Ingots
next article Increasing the Service Life of Large-Diameter Shut-Off Valves in the Conditions of Slurry Systems of Metallurgical Enterprises

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Literature
1.
V. Mann, V. Buzunov, V. Pingin, et al., “Environmental aspects of UC RUSAL’s aluminum smelters sustainable development,” in: Light Metals, Springer, Cham (2019).
2.
3.
Yu. A. Sychev, M. E. Aladin, and V. A. Serikov, “Developing a hybrid filter structure and a control algorithm for hybrid power supply,” Int. J. Power Elec. Dr., 13, No. 3, 1625–1634 (2022).
4.
5.
Program for Improving the Environmental Efficiency of the Branch Office RUSAL Bratsk PJSC in Shelekhov (2019).
6.
T. A. Tarabarinova and E. I. Golovina, “Capitalization of mineral resources as an innovation ecological strategy,” Geol. Miner. Res. Sib., 4, 86–96 (2021).
7.
R. Y. Feshchenko, E. A. Feschenko, R. N. Eremin, et al., “Analysis of the anode paste charge composition,” Metallurgist, 64, No. 7, 615–622 (2020).CrossRef
8.
F. Y. Sharikov, Y. V. Sharikov, and K. A. Krylov, “Selection of key parameters for green coke calcination in a tubular rotary kiln to produce anode petcoke,” ARPN J. Eng. Appl. Sci., 15, 2904–2912 (2020).
9.
Y. V. Sharikov and V. E. Quiroz Cabascango, “Mathematical modeling of mass, heat and fluid flow in a reverberatory furnace for melting nickel-containing raw materials,” J. Phys. Conf. Ser., 1753, No. 1, 012064 (2021).
10.
E. S. Gorlanov, R. Kawalla, and A. A. Polyakov, “Electrolytic production of aluminum. Review. Part 2. Development prospects,” Tsvetn. Metall., No. 10, 42–49 (2020).
11.
A. Zherdev, A. Svoevskiy, V. Pingin, et al., “Environmental enhancement of potroom processes by using a machine vision system,” in: Light Metals, Springer, Cham (2022).
12.
S. A. Martynov, O. N. Masko, and S. N. Fedorov, “Innovative ore-thermal furnace control systems,” Tsvetn. Metall., No. 4, 87–94 (2022).
13.
V. E. Q. Cabascango, V. Y. Bazhin, S. A. Martynov, and F. R. O. Pardo, “Automatic control system for thermal state of reverberatory furnaces in production of nickel alloys,” Metallurgist, 66, No. 1, 104–116 (2022).CrossRef
14.
V. Bazhin and O. Masko, “Monitoring of the behavior and state of nanoscale particles in a gas cleaning system of an ore-thermal furnace,” Symmetry, 14, No. 5, 923–936 (2022).CrossRef
15.
D. A. Kashin and A. A. Kulchitskiy, “Image-based quality monitoring of metallurgical briquettes,” Tsvetn. Metall., No. 9, 92–98 (2022).
16.
A. A. Kul’chitskii and D. A. Kashin, “The choice of a method for non-contact assessment of the composition of briquetted charge materials,” J. Phys. Conf. Ser., 1399, No. 4, 044108 (2019).
17.
V. I. Bolobov, S. A. Chupin, V. S. Bochkov, et al., “The effect of finely divided martensite of austenitic high manganese steel on the wear resistance of the excavator buckets teeth,” Key Eng. Mat., 854, 3–9 (2020).CrossRef
18.
V. Y. Bazhin, A. A. Kulchitskiy, and D. N. Kadrov, “Complex control of the state of steel pins in soderberg electrolytic cells by using computer vision systems,” Tsvetn. Metall., No. 3, 27–32 (2018).
19.
N. V. Vasilyeva, A. V. Boikov, O. O. Erokhina, and A. Y. Trifonov, “Automated digitization of radial charts,” J. Min. Inst., 247, 82–87 (2021).CrossRef
20.
L. C. Chen, G. Papandreou, I. Kokkinos, et al., “DeepLab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs,” IEEE T. Pattern Anal., 40, No. 4, 834–848 (2018).CrossRef
21.
A. Boikov, V. Payor, R. Savelev, and A. Kolesnikov, “Synthetic data generation for steel defect detection and classification using deep learning,” Symmetry, 13, No. 7, 1176–1186 (2021).CrossRef
22.
C. Chen, Q. Chen, J. Xu, and V. Koltun, “Learning to see in the dark,” Proc. CVPR IEEE, 3291–3300 (2018).
23.
D. R. Yakupov, P. V. Ivanova, and S. L. Ivanov, “Physical simulation of load displacement resistance of peat land surface on test bench,” Min. Inform. Anal. B., No. 5-1, 117–129 (2021).
24.
D. R. Iakupov, P. V. Ivanova, and S. L. Ivanov, “Estimate of parameters of retreat cutting equipment components for peatlands,” Min. Inform. Anal. B., No. 10, 88–103 (2022).
25.
A. V. Bolshunov, D. A. Vasilev, S. A. Ignatiev, et al., “Mechanical drilling of glaciers with bottom-hole scavenging with compressed air,” Ice Snow, 62, No. 1, 35–46 (2022).
26.
L. Q. Phuc, P. N. Dmitriev, T. V. Duy, and L. Yunpeng, “Influence of the main roof on the parameters of the abutment pressure zone in the selvedge of the seam,” Min. Inform. Anal. B., No. 6-1, 68–82 (2022).
27.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” Proc. CVPR IEEE, 770–778 (2016).
28.
S. A. Lavrenko and D. I. Shishlyannikov, “Performance evaluation of heading-and-winning machines in the conditions of potash mines,” Appl. Sci., 11, No. 8, 3444–3454 (2021).CrossRef
29.
S. Zhao, Y. Xie, W. Yue, and X. Chen, “A machine learning method for state identification of superheat degree with flame interference,” in: 10th International Symposium on High-Temperature Metallurgical Processing, Springer, Cham (2019).
30.
B. U. Toreyin and A. E. Cetin, “Online detection of fire in video,” Proc. CVPR IEEE, 1–5 (2007).
31.
A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” Commun. ACM, 60, No. 6, 84–90 (2017).CrossRef
32.
A. Paszke, S. Gross, F. Massa, et al., “PyTorch: An imperative style, high-performance deep learning library,” Adv. Neur. In., 32, 8024–8035 (2019).
33.
34.
N. Vasilyeva, E. Fedorova, and A. Kolesnikov, “Big data as a tool for building a predictive model of mill roll wear,” Symmetry, 13, No. 5, 859–869 (2021).CrossRef
35.
I. Beloglazov and K. Krylov, “An interval-simplex approach to determine technological parameters from experimental data,” Mathematics, 10, No. 16, 2959 (2022).
Metadata
Title
Automatic System for Detecting Visible Emissions in a Potroom of Aluminum Plant Based on Technical Vision and a Neural Network
Authors
A. K. Shestakov
P. A. Petrov
M. Yu. Nikolaev
Publication date
25-02-2023
Publisher
Springer US
Published in
Metallurgist / Issue 9-10/2023
Print ISSN: 0026-0894
Electronic ISSN: 1573-8892
DOI
https://doi.org/10.1007/s11015-023-01445-z

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