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Accueil Tous les numéros Volume 109 / Numéro 5-6 (2021) Matériaux & Techniques, 109 5-6 (2021) 502 Références
Numéro
Matériaux & Techniques
Volume 109, Numéro 5-6, 2021
Special Issue on ‘Materials and Society: the circular economy, design for circularity and industrial symbiosis’, edited by Jean-Pierre Birat, Gaël Fick, Nicolas Perry, Andrea Declich, Leiv Kolbensein, Dominique Millet and Thecle Alix
Numéro d'article 502
Nombre de pages 13
Section Environnement − recyclage / Environment − recycling
DOI https://doi.org/10.1051/mattech/2022010
Publié en ligne 28 février 2022
  1. European Steel Association, European Steel in Figures 2020, 2020, Available from https://www.eurofer.eu/assets/Uploads/European-Steel-in-Figures-2020.pdf [Online accessed: 6/10/2021] [Google Scholar]
  2. D. Ibarra, J. Ganzarain, I.J. Igartua, Business model innovation through Industry 4.0: A review, Proc. Manuf. 22, 4–10 (2018), https://doi.org/10.1016/j.promfg.2018.03.002 [Google Scholar]
  3. T.A. Branca, B. Fornai, V. Colla, et al., The challenge of digitalization in the steel sector, Metals 10(2), 1–23 (2020), https://doi.org/10.3390/met10020288 [Google Scholar]
  4. International Society of Automation, ANSI/ISA-95.00.01 Enterprise-Control System Integration – Part I: Models and Terminology, 2010 [Google Scholar]
  5. K. Schweichhart, Reference Architectural Model Industry 4.0 (RAMI 4.0), 2016, Available from https://ec.europa.eu/futurium/en/system/files/ged/a2-schweichhart-reference_architectural_model_industrie_4.0_rami_4.0.pdf [Google Scholar]
  6. V. Colla, C. Pietrosanti, E. Malfa, et al., Environment 4.0: How digitalization and machine learning can improve the environmental footprint of the steel production processes, Materiaux & Techniques 108(5-6), (2020), https://doi.org/10.1051/mattech/2021007 [Google Scholar]
  7. T.A. Branca, V. Colla, D. Algermissen, et al., Reuse and recycling of by-products in the steel sector: Recent achievements paving the way to circular economy and industrial symbiosis in Europe, Metals 10(3), (2020), https://doi.org/10.3390/met10030345 [Google Scholar]
  8. J. Feliks, K. Majewska, Agent-based modeling of steel production processes under uncertainty, in: Proceedings 24th International Conference on Metallurgy and Materials, METAL 2015, 2015, pp. 1739–1744 [Google Scholar]
  9. W. Shen, Q. Hao, H.J. Yoon, et al., Applications of agent-based systems in intelligent manufacturing: An updated review, Adv. Eng. Inform. 20, 415–431 (2006), https://doi.org/10.1016/j.aei.2006.05.004 [CrossRef] [Google Scholar]
  10. S. Karnouskos, P. Leitao, L. Ribeiro, et al., Industrial agents as a key enabler for realizing industrial cyber-physical systems: Multiagent systems entering industry 4.0, IEEE Ind. Electron. Mag. 14(3), 18–32 (2020), https://doi.org/10.1109/MIE.2019.2962225 [CrossRef] [Google Scholar]
  11. V. Gorodetsky, V. Larukchin, P. Skobelev, Conceptual model of digital platform for enterprises of industry 5.0, in: Studies in Computational Intelligence, 2020, pp. 35–40, https://doi.org/10.1007/978-3-030-32258-8_4 [Google Scholar]
  12. L. Monostori, J. Váncza, S.R.T. Kumara, Agent-based systems for manufacturing, CIRP Ann. Manuf. Technol. 55(2), 697–720 (2006), https://doi.org/10.1016/j.cirp.2006.10.004 [CrossRef] [Google Scholar]
  13. S. Jacobi, C. Madrigal-Mora, E. León-Soto, et al., AgentSteel: An agent-based online system for the planning and observation of steel production, in: Proceedings of the International Conference on Autonomous Agents, 2005, pp. 155–160 [Google Scholar]
  14. L. Sun, F. Luan, A multi-agent framework for the scheduling of steel-making and continuous casting process with lagrangian relaxation neural networks, IFAC-PapersOnLine 28(25), 108–113 (2015), https://doi.org/10.1016/j.ifacol.2015.11.068 [CrossRef] [Google Scholar]
  15. M.J. Neuer, F. Marchiori, A. Ebel, et al., Dynamic reallocation and rescheduling of steel products using agents with strategical anticipation and virtual market structures, IFAC-PapersOnLine 49(20), 232–237 (2016), https://doi.org/10.1016/j.ifacol.2016.10.126 [CrossRef] [Google Scholar]
  16. F. Marchiori, A. Belloni, M. Benini, et al., Integrated dynamic energy management for steel production, Energy Proc. 105, 2772–2777 (2017), https://doi.org/10.1016/j.egypro.2017.03.597 [CrossRef] [Google Scholar]
  17. F. Marchiori, M. Benini, S. Cateni, et al., Agent-based approach for energy demand-side management, Stahl und Eisen 138(2), 25–29 (2018) [Google Scholar]
  18. S. Franklin, A. Graesser, Is it an agent, or just a program? A taxomony of autonomous agents, in: J.P. Müller, M.J. Wooldridge, N.R. Jennings, eds., Intelligent Agents III Agent Theories, Architectures, and Languages, Springer Berlin Heidelberg, 1996, pp. 21–35 [Google Scholar]
  19. Y. Labrou, T. Finin, Y. Peng, Agent communication languages: The current landscape, IEEE Intell. Syst. Their Appl. 14(2), 45–52 (1999), https://doi.org/10.1109/5254.757631 [CrossRef] [Google Scholar]
  20. H.S. Nwana, L. Lee, N.R. Jennings, Co-ordination in software agent systems, BT Technol. J., 42–58 (1996) [Google Scholar]
  21. J.E. Doran, S. Franklin, N.R. Jennings, et al., On cooperation in multi-agent systems, Knowl. Eng. Rev., 1–7 (1997), https://doi.org/10.1017/S0269888997003111 [Google Scholar]
  22. T. Labeodan, K. Aduda, G. Boxem, et al., On the application of multi-agent systems in buildings for improved building operations, performance and smart grid interaction – A survey, Renew. Sustain. Energy Rev. 50, 1405–1414 (2015), Elsevier Ltd, https://doi.org/10.1016/j.rser.2015.05.081 [CrossRef] [Google Scholar]
  23. G.H. Merabet, M. Essaaidi, H. Talei, et al., Applications of multi-agent systems in smart grids: A survey, in: Proceedings on International Conference on Multimedia Computing and Systems, Sep. 2014, pp. 1088–1094, https://doi.org/10.1109/ICMCS.2014.6911384 [Google Scholar]
  24. B. Chen, H.H. Cheng, A review of the applications of agent technology in traffic and transportation systems, IEEE Trans. Intell. Transp. Syst. 11, 485–497 (2010), https://doi.org/10.1109/TITS.2010.2048313 [CrossRef] [Google Scholar]
  25. E. Shakshuki, M. Reid, Multi-agent system applications in healthcare: Current technology and future roadmap, Proc. Comput. Sci. 52(1), 252–261 (2015), https://doi.org/10.1016/j.procs.2015.05.071 [CrossRef] [Google Scholar]
  26. V. Iannino, V. Colla, J. Denker, et al., A CPS-based simulation platform for long production factories, Metals 9(10), 1–20 (2019), https://doi.org/10.3390/met9101025 [CrossRef] [Google Scholar]
  27. V. Iannino, C. Mocci, M. Vannocci, et al., An event-driven agent-based simulation model for industrial processes, Appl. Sci. 10(12), 1–22 (2020), https://doi.org/10.3390/app10124343 [CrossRef] [Google Scholar]
  28. L. Tang, J. Liu, A. Rong, et al., A review of planning and scheduling systems and methods for integrated steel production, Eur. J. Oper. Res. 133(1), 1–20 (2001), https://doi.org/10.1016/S0377-2217(00)00240-X [CrossRef] [Google Scholar]
  29. M. Iglesias-Escudero, J. Villanueva-Balsera, F. Ortega-Fernandez, et al., Planning and scheduling with uncertainty in the steel sector: A review, Appl. Sci. (2019), https://doi.org/10.3390/app9132692 [Google Scholar]
  30. J. Zhao, Q.L. Liu, W. Wang, Models and algorithms of production scheduling in tandem cold rolling, Zidonghua Xuebao/Acta Autom. Sin. 34(5), 565–573 (2008), https://doi.org/10.3724/SP.J.1004.2008.00565 [Google Scholar]
  31. J. Wan, et al., Toward dynamic resources management for IoT-based manufacturing, IEEE Commun. Mag. 56(2), 52–59 (2018), https://doi.org/10.1109/MCOM.2018.1700629 [CrossRef] [Google Scholar]
  32. V. Iannino, C. Mocci, V. Colla, A brokering-based interaction protocol for dynamic resource allocation in steel production processes, in: Advances in Intelligent Systems and Computing, 1368 AISC, pp. 119–129, 2021, https://doi.org/10.1007/978-3-030-72654-6_12 [Google Scholar]
  33. V. Iannino, C. Mocci, V. Colla, A hybrid peer-to-peer architecture for agent-based steel manufacturing processes, IFAC-PapersOnLine 54(1), 528–33 (2021), https://doi.org/10.1016/j.ifacol.2021.08.167 [CrossRef] [Google Scholar]
  34. O. Taisir, Total productive maintenance review and overall equipment effectiveness measurement, Jordan J. Mech. Ind. Eng. 4(4), 517–522 (2010) [Google Scholar]
  35. S. Kumar, R. Bhushan, S. Swaroop, Study of total productive maintenance & its implementation approach in steel manufacturing industry: A case study of equipment wise breakdown analysis, Int. Res. J. Eng. Technol. 4(8), 608–613 (2017), Available from https://www.academia.edu/34509469/Study_of_total_productive_maintenance_and_its_implementation_approach_in_steel_manufacturing_industry_A_case_study_of_equipment_wise_breakdown_analysis [Online accessed: 6/16/2021] [Google Scholar]
  36. A. Hauser, M. Weitzer, S. Gunsch, et al., Dynamic hydrogen-intensified methanation of synthetic by-product gases from steelworks, Fuel Process. Technol. 217, 1–10 (2021), https://doi.org/10.1016/j.fuproc.2020.106701 [CrossRef] [Google Scholar]
  37. A. Zaccara, A. Petrucciani, I. Matino, et al., Renewable hydrogen production processes for the off-gas valorization in integrated steelworks through hydrogen intensified methane and methanol syntheses, Metals 10(11), 1–24 (2020), https://doi.org/10.3390/met10111535 [CrossRef] [Google Scholar]
  38. M. Bampaou, K. Panopoulos, P. Seferlis, et al., Integration of renewable hydrogen production in steelworks off-gases for the synthesis of methanol and methane, Energies 14(10), 2904 (2021), https://doi.org/10.3390/en14102904 [CrossRef] [Google Scholar]
  39. I. Matino, S. Dettori, V. Colla, et al., Forecasting blast furnace gas production and demand through echo state neural network-based models: Pave the way to off-gas optimized management, Appl. Energy 253, (2019), https://doi.org/10.1016/j.apenergy.2019.113578 [CrossRef] [Google Scholar]
  40. S. Dettori, I. Matino, V. Colla, et al., A deep learning-based approach for forecasting off-gas production and consumption in the blast furnace, Neutr. Comput. Appl., 1–14 (2021), https://doi.org/10.1007/s00521-021-05984-x [Google Scholar]
  41. S. Dettori, A. Maddaloni, F. Galli, et al., Steam turbine rotor stress control through nonlinear model predictive control, Energies 14(13), 1–30 (2021), https://doi.org/10.3390/en14133998 [Google Scholar]
  42. P. Leitão, V. Mařík, P. Vrba, Past, present, and future of industrial agent applications, IEEE Trans. Ind. Inform. 9(4), 2360–2372 (2013), https://doi.org/10.1109/TII.2012.2222034 [CrossRef] [Google Scholar]
  43. V. Marik, V. Gorodetsky, P. Skobelev, Multi-agent technology for industrial applications: Barriers and trends, IEEE Trans. Syst. Man, Cybern.: Syst. 1980–1987 (2020), https://doi.org/10.1109/SMC42975.2020.9283071 [Google Scholar]

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