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Edge Deep Learning Towards the Metallurgical Industry: Improving the Hybrid Pelletized Sinter (HPS) Process

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Enterprise Information Systems (ICEIS 2021)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 455))

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Abstract

The implementation of intelligent systems in the processes brings the industries of the mining and metallurgy sectors closer to the context of Industry 4.0 and provides significant improvements, especially in the production and consumption of raw materials and internal products. In this work, we propose an Artificial Intelligence System in Deep Learning with Edge Computing to recognize the quasi-particles of the Hybrid Pelletized Sinter (HPS) process in the steel industry. We train our model with the aXeleRate tool using the Keras-Tensorflow framework and MobileNet architecture. We then tested the model in an embedded system using the SiPEED MaiX Dock board. The model validation results were 98.60% precision and 100.00% recall. Bench-scale test results were 100.00% precision and 70.00% recall. The results were promising and indicate the feasibility of the proposal.

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Notes

  1. 1.

    https://github.com/AIWintermuteAI/aXeleRate.

References

  1. Campolo, C., Genovese, G., Iera, A., Molinaro, A.: Virtualizing AI at the distributed edge towards intelligent IoT applications. J. Sens. Actuator Netw. 10(1), 13 (2021)

    Article  Google Scholar 

  2. Chen, Z., Liu, L., Qi, X., Geng, J.: Digital mining technology-based teaching mode for mining engineering. iJET 11(10), 47–52 (2016)

    Google Scholar 

  3. Cob-Parro, A.C., Losada-Gutiérrez, C., Marrón-Romera, M., Gardel-Vicente, A., Bravo-Muñoz, I.: Smart video surveillance system based on edge computing. Sensors 21(9), 2958 (2021)

    Article  Google Scholar 

  4. Cornetta, G., Touhafi, A.: Design and evaluation of a new machine learning framework for IoT and embedded devices. Electronics 10(5), 600 (2021)

    Article  Google Scholar 

  5. Deng, S., Zhao, H., Fang, W., Yin, J., Dustdar, S., Zomaya, A.Y.: Edge intelligence: the confluence of edge computing and artificial intelligence. IEEE Internet Things J. 7, 7457–7469 (2020)

    Article  Google Scholar 

  6. Dias, Í.D.S.M.: Sistema de controle granulométrico de pelotas de minério de ferro (2018)

    Google Scholar 

  7. Gao, G., Gao, J., Liu, Q., Wang, Q., Wang, Y.: CNN-based density estimation and crowd counting: a survey. arXiv preprint arXiv:2003.12783 (2020)

  8. Gontijo, M.D.: Análise granulométrica por imagem de amostras ultrafinas. Rev. Engenharia Interesse Soc. 1(3) (2018)

    Google Scholar 

  9. Januzzi, A.: Análise da aglomeração a frio no processo hps (hybrid pelletized sinter) com ênfase nas matérias-primas envolvidas (2008)

    Google Scholar 

  10. Karras, K., et al.: A hardware acceleration platform for AI-based inference at the edge. Circ. Syst. Signal Process. 39(2), 1059–1070 (2020)

    Article  Google Scholar 

  11. Keresztes, B., Abdelghafour, F., Randriamanga, D., da Costa, J.P., Germain, C.: Real-time fruit detection using deep neural networks. In: 14th International Conference on Precision Agriculture (2018)

    Google Scholar 

  12. Kinnunen, P.H.M., Kaksonen, A.H.: Towards circular economy in mining: opportunities and bottlenecks for tailings valorization. J. Clean. Prod. 228, 153–160 (2019)

    Article  Google Scholar 

  13. Klippel, E., Oliveira, R., Maslov, D., Bianchi, A., Silva, S.E., Garrocho, C.: Towards to an embedded edge AI implementation for longitudinal rip detection in conveyor belt. In: Anais Estendidos do X Simpósio Brasileiro de Engenharia de Sistemas Computacionais, pp. 97–102. SBC, Porto Alegre (2020). https://doi.org/10.5753/sbesc_estendido.2020.13096, https://sol.sbc.org.br/index.php/sbesc_estendido/article/view/13096

  14. LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)

    Article  Google Scholar 

  15. Li, E., Zeng, L., Zhou, Z., Chen, X.: Edge AI: on-demand accelerating deep neural network inference via edge computing. IEEE Trans. Wirel. Commun. 19(1), 447–457 (2019)

    Article  Google Scholar 

  16. Lima, A.J.D.A.S.: Caracterização tecnológica de uma mistura de sinter feed e pellet feed para uso em processo hps (2019)

    Google Scholar 

  17. Lin, X., Li, J., Wu, J., Liang, H., Yang, W.: Making knowledge tradable in edge-AI enabled IoT: a consortium blockchain-based efficient and incentive approach. IEEE Trans. Industr. Inf. 15(12), 6367–6378 (2019)

    Article  Google Scholar 

  18. Liu, C., Su, X., Li, C.: Edge computing for data anomaly detection of multi-sensors in underground mining. Electronics 10(3), 302 (2021)

    Article  MathSciNet  Google Scholar 

  19. Mardonova, M., Choi, Y.: Review of wearable device technology and its applications to the mining industry. Energies 11(3), 547 (2018)

    Article  Google Scholar 

  20. Mazzia, V., Khaliq, A., Salvetti, F., Chiaberge, M.: Real-time apple detection system using embedded systems with hardware accelerators: an edge AI application. IEEE Access 8, 9102–9114 (2020)

    Article  Google Scholar 

  21. Meira, N., Silva, M., Oliveira, R., Souza, A., D’Angelo, T., Vieira, C.: Edge deep learning applied to granulometric analysis on quasi-particles from the hybrid pelletized sinter (HPS) process. In: Proceedings of the 23rd International Conference on Enterprise Information Systems - Volume 1: ICEIS, pp. 527–535. INSTICC, SciTePress (2021). https://doi.org/10.5220/0010458805270535

  22. Ohbuchi, E.: Low power AI hardware platform for deep learning in edge computing. In: 2018 IEEE CPMT Symposium Japan (ICSJ), pp. 89–90. IEEE (2018)

    Google Scholar 

  23. Passos, L.A.S., Moreira, J.L., Jorge, A., Cavalcante, M.V.S.: Melhoria no desempenho do processo de produção de pelotas de minério de ferro em discos de pelotização pela utilização de sistemas otimizantes com lógica nebulosa. In: ABM Proceedings. Editora Blucher, September 2014. https://doi.org/10.5151/2594-357x-25340

  24. Robben, C., Wotruba, H.: Sensor-based ore sorting technology in mining-past, present and future. Minerals 9(9), 523 (2019)

    Article  Google Scholar 

  25. Saleem, M.H., Potgieter, J., Arif, K.M.: Plant disease detection and classification by deep learning. Plants 8(11), 468 (2019)

    Article  Google Scholar 

  26. Santos, R.B.M., Augusto, K.S., Paciornik, S., Alcantara Domingues, A.L.: An image analysis system for automatic characterisation of iron ore sintering quasiparticles. Mineral Process. Extract. Metallurgy 1–9 (2019)

    Google Scholar 

  27. Shibuta, Y., Ohno, M., Takaki, T.: Computational metallurgy: advent of cross-scale modeling: high-performance computing of solidification and grain growth. Adv. Theory Simul. 1(9), 1870020 (2018)

    Article  Google Scholar 

  28. Sinoviev, V.V., Okolnishnikov, V.V., Starodubov, A.N., Dorofeev, M.U.: Approach to effectiveness evaluation of robotics technology in mining using discrete event simulation. Int. J. Math. Comput. Simul. 10, 123–128 (2016)

    Google Scholar 

  29. Souza, L.E.R.: Medição de granulometria de minério de ferro através de imagens em circuito de britagem primária (2020)

    Google Scholar 

  30. Wang, X., Han, Y., Leung, V.C., Niyato, D., Yan, X., Chen, X.: Convergence of edge computing and deep learning: a comprehensive survey. IEEE Commun. Surv. Tutor. 22(2), 869–904 (2020)

    Article  Google Scholar 

  31. Wang, X., Han, Y., Wang, C., Zhao, Q., Chen, X., Chen, M.: In-edge AI: intelligentizing mobile edge computing, caching and communication by federated learning. IEEE Netw. 33(5), 156–165 (2019)

    Article  Google Scholar 

  32. Zhang, Q., Liu, Y., Gong, C., Chen, Y., Yu, H.: Applications of deep learning for dense scenes analysis in agriculture: a review. Sensors 20(5), 1520 (2020)

    Article  Google Scholar 

  33. Zhou, X., Fang, B., Qian, J., Xie, G., Deng, B., Qian, J.: Data driven faster R-CNN for transmission line object detection. In: Ning, H. (ed.) CyberDI/CyberLife -2019. CCIS, vol. 1137, pp. 379–389. Springer, Singapore (2019). https://doi.org/10.1007/978-981-15-1922-2_27

    Chapter  Google Scholar 

  34. Zhou, Z., Chen, X., Li, E., Zeng, L., Luo, K., Zhang, J.: Edge intelligence: paving the last mile of artificial intelligence with edge computing. Proc. IEEE 107(8), 1738–1762 (2019)

    Article  Google Scholar 

  35. Zobnin, N.N., Torgovets, A.K., Pikalova, I.A., Yussupova, Y.S., Atakishiyev, S.A.: Influence of thermal stability of quartz and the particle size distribution of burden materials on the process of electrothermal smelting of metallurgical silicon. Orient. J. Chem. 34(2), 1120–1125 (2018)

    Article  Google Scholar 

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Acknowledgements

The authors would like to thank CAPES, CNPq and the Federal University of Ouro Preto for supporting this work. Also, the authors would like to thank ArcelorMittal Monlevade for enabling the creation of a dataset with real images. This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001, and by Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) - Finance code 308219/2020-1.

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Authors and Affiliations

  1. Federal University of Ouro Preto, Ouro Preto, Brazil

    Natália F. de C. Meira, Mateus C. Silva, Cláudio B. Vieira & Ricardo A. R. Oliveira

  2. ArcelorMittal, João Monlevade, Brazil

    Alinne Souza

Authors
  1. Natália F. de C. Meira
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  2. Mateus C. Silva
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  3. Cláudio B. Vieira
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  4. Alinne Souza
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  5. Ricardo A. R. Oliveira
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Corresponding author

Correspondence to Mateus C. Silva .

Editor information

Editors and Affiliations

  1. Polytechnic Institute of Setúbal/INSTICC, Setúbal, Portugal

    Joaquim Filipe

  2. Warsaw University of Technology, Warsaw, Poland

    Michał Śmiałek

  3. George Mason University, Fairfax, VA, USA

    Alexander Brodsky

  4. MODESTE/ESEO, Angers, France

    Slimane Hammoudi

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de C. Meira, N.F., Silva, M.C., Vieira, C.B., Souza, A., Oliveira, R.A.R. (2022). Edge Deep Learning Towards the Metallurgical Industry: Improving the Hybrid Pelletized Sinter (HPS) Process. In: Filipe, J., Śmiałek, M., Brodsky, A., Hammoudi, S. (eds) Enterprise Information Systems. ICEIS 2021. Lecture Notes in Business Information Processing, vol 455. Springer, Cham. https://doi.org/10.1007/978-3-031-08965-7_8

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  • DOI: https://doi.org/10.1007/978-3-031-08965-7_8

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