Issue
Matériaux & Techniques
Volume 108, Number 5-6, 2020
Materials and Society: transitions in society, materials and energy
Article Number 507
Number of page(s) 11
Section Environnement - recyclage / Environment - recycling
DOI https://doi.org/10.1051/mattech/2021007
Published online 26 April 2021
  1. United Nations, Transforming our world: the 2030 Agenda for Sustainable Development, 2015, https://sdgs.un.org/2030agenda (last access January 23, 2021) [Google Scholar]
  2. J.-P. Birat, E. Malfa, V. Colla, J.S. Thomas, SUSTAINABLE steel production for the 2030s: The vision of the European Steel Technology Platform’s Strategic Research Agenda (ESTEP’s SRA), in: Technical Proceedings of the2014 NSTI Nanotechnology Conference and Expo, NSTI-Nanotech 2014, Vol. 3, 2014, pp. 238–241 [Google Scholar]
  3. A.N. Conejo, J.-P. Birat, A. Dutta, A review of the current environmental challenges of the steel industry and its value chain, J. Environ. Manag. 259, article no. 109782 (2020) [Google Scholar]
  4. J.-P. Birat, Society, materials, and the environment: The case of steel, Metals 10(3), art. no. 331 (2020) [Google Scholar]
  5. J.-P. Birat, The environment and materials, from the standpoints of ethics, social sciences, law and politics, Materiaux & Techniques 107(1), art. no. 102 (2019) [Google Scholar]
  6. https://www.estep.eu/assets/Uploads/ec-rtd-he-partnerships-for-clean-steel-low-carbon-steelmaking.pdf (last access January 17, 2021) [Google Scholar]
  7. FOCUS Roland Berger, “ The future of steelmaking – How the European steel industry can achieve carbon neutrality”, in: Roland Berger GMBH, May 2020, 2020 [Google Scholar]
  8. M. Arens, Policy support for and R&D activities on digitising the European steel industry, Resour. Conserv. Recycl. 143, 244–250 (2019) [Google Scholar]
  9. T.A. Branca, B. Fornai, V. Colla, M.M. Murri, E. Streppa, A.J. Schröder, The challenge of digitalization in the steel sector, Metals 10(2), art. no. 288 (2020) [Google Scholar]
  10. T. Akyazi, A. Oyarbide, A. Goti, J. Gaviria, F. Bayon, Creating a roadmap for professional skills in industry 4.0, Hydrocarb. Process. 99(11), (2020) [Google Scholar]
  11. A.B.L. de Sousa Jabbour, C.J.C. Jabbour, C. Foropon, M.G. Filho, When titans meet – Can industry 4.0 revolutionise the environmentally-sustainable manufacturing wave? The role of critical success factors, Technol. Forecast. Social Change 132, 18–25 (2018) [Google Scholar]
  12. Y. Li, J. Dai, L. Cui, The impact of digital technologies on economic and environmental performance in the context of industry 4.0: A moderated mediation model, Int. J. Prod. Econ. 229, article no. 107777 (2020) [Google Scholar]
  13. K. Tiwari, M. Khan S., Sustainability accounting and reporting in the industry 4.0, J. Clean. Prod. 258, (2020) [Google Scholar]
  14. V. Colla, Empowering steel manufacturing through Artificial Intelligence and Machine Learning, in: AI & Big Data for Innovation Summit, December 2–5 2019, organised by the K4I Forum in the European Parliament, Brussels (Belgium), 2019 [Google Scholar]
  15. G.F. Porzio, V. Colla, B. Fornai, M. Vannucci, M. Larsson, H. Stripple, Process integration analysis and some economic-environmental implications for an innovative environmentally friendly recovery and pre-treatment of steel scrap, Appl. Energy 161, 656–672 (2016) [Google Scholar]
  16. I. Matino, T.A. Branca, B. Fornai, V. Colla, L. Romaniello, Scenario analyses for by-products reuse in integrated steelmaking plants by combining process modeling, simulation, and optimization techniques, Steel Res. Int. 90(10), art. no. 1900150 (2019) [Google Scholar]
  17. V. Colla, I. Matino, F. Cirilli, et al., Improving energy and resource efficiency of electric steelmaking through simulation tools and process data analyses, Materiaux & Techniques 104(6-7), article no. 602 (2016) [Google Scholar]
  18. C. Schneider, S. Lechtenböhmer, Industrial site energy integration – The sleeping giant of energy efficiency? Identifying site specific potentials for vertical integrated production at the example of German steel production, in: Eceee Industrial Summer Study Proceedings, 2016, pp. 587–598 [Google Scholar]
  19. V. Colla, F. Cirilli, B. Kleimt, et al., Monitoring the environmental and energy impacts of electric arc furnace steelmaking, Materiaux & Techniques 104(1), art. no. 104 (2016) [Google Scholar]
  20. I. Matino, E. Alcamisi, V. Colla, S. Baragiola, P. Moni, Process modelling and simulation of electric arc furnace steelmaking to allow prognostic evaluations of process environmental and energy impacts, Materiaux & Techniques 104(1), art. no. 10 (2016) [Google Scholar]
  21. V. Colla, I. Matino, S. Dettori, S. Cateni, R. Matino, Reservoir computing approaches applied to energy management in industry, Commun. Comput. Inform. Sci. 1000, 66–79 (2019) [Google Scholar]
  22. X. Chen, J. She, X. Chen, Wu M., Discrete wavelet transfer based BPNN for calculating carbon efficiency of sintering process, J. Adv. Comput. Intell. Intell. Inform. 20(7), 1070–1076 (2016) [Google Scholar]
  23. M. Lieder, F.M.A. Asif, A. Rashid, A choice behavior experiment with circular business models using machine learning and simulation modeling, J. Clean. Prod. 258, article no. 120894 (2020) [Google Scholar]
  24. V.J.L. Gan, I.M.C. Lo, J. Ma, K.T. Tse, J.C.P. Cheng, C. Chan M., Simulation optimisation towards energy efficient green buildings: Current status and future trends, J. Clean. Prod. 254, article no. 120012 (2020) [Google Scholar]
  25. J. Varghese J., Computational design of catalysts for bio-waste upgrading, Curr. Opin. Chem. Eng. 26, 20–27 (2019) [Google Scholar]
  26. M. Vondra, M. Touš, S.Y. Teng, Digestate evaporation treatment in biogas plants: A techno-economic assessment by Monte Carlo, neural networks and decision trees, J. Clean. Prod. 238, article. no. 117870 (2019) [Google Scholar]
  27. S. Cateni, V. Colla, G. Nastasi, A multivariate fuzzy system applied for outliers detection, J. Intell. Fuzzy Syst. 24(4), 889–903 (2013) [Google Scholar]
  28. S. Cateni, V. Colla, M. Vannucci, A fuzzy logic-based method for outliers detection, in: Proceedings of the IASTED International Conference on Artificial Intelligence and Applications, AIA 2007, 2007, pp. 561–566 [Google Scholar]
  29. M. Sakurada, T. Yairi, Anomaly detection using autoencoders with nonlinear dimensionality reduction, in: Proceedings of the MLSDA 2014, 2nd Workshop on Machine Learning for Sensory Data Analysis, 2014, pp. 4–11 [Google Scholar]
  30. C. Zhou, R.C. Paffenroth, Anomaly detection with robust deep autoencoders, in: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2017, pp. 665–674 [Google Scholar]
  31. I.-C. Wu, T.-L. Chen, Y.-M. Chen, T.-C. Liu, Y.-A. Chen, Analyzing load profiles of electricity consumption by a time series data mining framework, in: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10294 LNCS, 2017, pp. 443–454 [Google Scholar]
  32. X.-L. Li, D.-X. Liu, C. Jia, X.-Z. Chen, Multi-model control of blast furnace burden surface based on fuzzy SVM, Neurocomputing 148, 209–215 (2015) [Google Scholar]
  33. S. Cateni, V. Colla, M. Vannucci, General purpose input variables extraction: A genetic algorithm based procedure GIVE a GAP, in: ISDA 2009, 9th International Conference on Intelligent Systems Design and Applications, 2009, pp. 1278–1283 [Google Scholar]
  34. V. Bolón-Canedo, B. Remeseiro, B. Cancela, Feature Selection for Big Visual Data: Overview and Challenges, in: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10882 LNCS, 2018, pp. 136–143 [Google Scholar]
  35. S.K. Phan, C. Chen, Big Data and Monitoring the Grid, The Power Grid: Smart, Secure, Green and Reliable, 2017, pp. 253–285 [Google Scholar]
  36. J. Pence, Y. Sun, X. Zhu, Z. Mohaghegh, C. Ostroff, E. Kee, Data-theoretic methodology and computational platform for the quantification of organizational mechanisms in probabilistic risk assessment, in: International Topical Meeting on Probabilistic Safety Assessment and Analysis, PSA 2017, Vol. 2, 2017, pp. 1294–1300 [Google Scholar]
  37. S. Cateni, V. Colla, M. Vannucci, A genetic algorithm-based approach for selecting input variables and setting relevant network parameters of a SOM-based classifier, Int. J. Simul.: Syst. Sci. Technol. 12(2), 30–37 (2011) [Google Scholar]
  38. S. Cateni, V. Colla, M. Vannucci, A hybrid feature selection method for classification purposes, in: Proceedings – UKSim-AMSS 8th European Modelling Symposium on Computer Modelling and Simulation, EMS 2014, 2014, pp. 39–44 [Google Scholar]
  39. I. Matino, S. Dettori, V. Colla, V. Weber, S. Salame, 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, article no. 113578 (2019) [Google Scholar]
  40. I. Matino, V. Colla, T.A. Branca, L. Romaniello, Optimization of By-Products Reuse in the Steel Industry: Valorization of Secondary Resources with a Particular Attention on Their Pelletization, Waste Biomass Valoriz. 8(8), 2569–2581 (2017) [Google Scholar]
  41. Z. Yeo, J.S.C. Low, D.Z.L. Tan, S.Y. Chung, T.B. Tjandra, J. Ignatius, A collaboration platform for enabling industrial symbiosis: Towards creating a self-learning waste-to-resource database for recommending industrial symbiosis transactions using text analytics, Proc. CIRP 80, 643–648 (2019) [Google Scholar]
  42. G.F. Porzio, B. Fornai, A. Amato, et al., Reducing the energy consumption and CO2 emissions of energy intensive industries through decision support systems – An example of application to the steel industry, Appl. Energy 112, 818–833 (2013) [Google Scholar]
  43. E. Arica, M. Oliveira, C. Emmanouilidis, Performance measurement in sensorized sociotechnical manufacturing environments, in: IFIP Advances in Information and Communication Technology, Vol. 536, 2018, pp. 263–268 [Google Scholar]
  44. M. Oliveira, E. Arica, M. Pinzone, P. Fantini, M. Taisch, Human-Centered Manufacturing Challenges Affecting European Industry 4.0 Enabling Technologies, in: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11786 LNCS, 2019, pp. 507–517 [Google Scholar]
  45. E.G. Almquist, Implementation of MANIFEST augmented reality system at tata steel Europe, in: AISTech − Iron and Steel Technology Conference Proceedings, Vol. 3, 2020, pp. 1734–1744 [Google Scholar]
  46. M. Kerin, D.T. Pham, A review of emerging industry 4.0 technologies in remanufacturing, J. Clean. Prod. 237, (2019) [Google Scholar]
  47. I. Matino, V. Colla, L. Romaniello, F. Rosito, L. Portulano, Simulation techniques for an efficient use of resources: an overview for the steelmaking field, in: 2015 World Congress on Sustainable Technologies, WCST 2015, 2016, pp. 48–54 [Google Scholar]
  48. V. Colla, I. Matino, T.A. Branca, B. Fornai, L. Romaniello, F. Rosito, Efficient use of water resources in the steel industry, Water 9(11), article no. 874 (2017) [Google Scholar]
  49. I. Matino, V. Colla, T.A. Branca, Extension of pilot tests of cyanide elimination by ozone from blast furnace gas washing water through Aspen Plus® based model, Front. Chem. Sci. Eng. 12(4), 718–730 (2018) [Google Scholar]
  50. I. Matino, V. Colla, S. Baragiola, Internal Slags Reuse in an Electric Steelmaking Route and Process Sustainability: Simulation of Different Scenarios Through the EIRES Monitoring Tool, Waste Biomass Valoriz. 9(12), 2481–2491 (2018) [Google Scholar]
  51. I. Matino, V. Colla, S. Baragiola, Electric energy consumption and environmental impact in unconventional EAF steelmaking scenarios, Energy Proc. 105, 3636–3641 (2017) [Google Scholar]
  52. D.C. Mazur, J.A. Kay, K.D. Mazur, B.K. Venne, The value of integrating power and process for the metals industry, Iron Steel Technol. 15(5), 56–62 (2018) [Google Scholar]
  53. G.F. Porzio, G. Nastasi, V. Colla, M. Vannucci, T.A. Branca, Comparison of multi-objective optimization techniques applied to off-gas management within an integrated steelwork, Appl. Energy 136, 1085–1097 (2014) [Google Scholar]
  54. A. Maddaloni, G.F. Porzio, G. Nastasi, V. Colla, T.A. Branca, Multi-objective optimization applied to retrofit analysis: A case study for the iron and steel industry, Appl. Therm. Eng. 91, 638–646 (2015) [Google Scholar]
  55. V. Colla, I. Matino, S. Dettori, et al., Assessing the efficiency of the off-gas network management in integrated steelworks, Materiaux & Techniques 107(1), art. no. 104 (2019) [Google Scholar]
  56. A. Wolff, F. Mintus, S. Bialek, S. Dettori, V. Colla, “ Economical Mixed-Integer Model Predictive Controller for optimizing the sub-network of the BOF gas”, in: European Steel Days ESTAD 2019, June 24–28, 2019, Dusseldorf (Germany) [Google Scholar]
  57. E.F. Camacho, C. Bordons, Introduction to model predictive control, Adv. Textb. Control Signal Process. 1–11 (2007) [Google Scholar]
  58. S. Dettori, I. Matino, V. Colla, V. Weber, S. Salame, Neural network-based modeling methodologies for energy transformation equipment in integrated steelworks processes, Energy Proc. 158, 4061–4066 (2019) [Google Scholar]
  59. V. Colla, I. Matino, S. Dettori, S. Cateni, R. Matino, Reservoir computing approaches applied to energy management in industry, Commun. Comput. Inform. Sci. 1000, 66–79 (2019) [Google Scholar]
  60. I. Matino, S. Dettori, V. Colla, V. Weber, S. Salame, Two innovative modelling approaches in order to forecast consumption of blast furnace gas by hot blast stoves, Energy Proc. 158, 4043–4048 (2019) [Google Scholar]
  61. https://www.innovationpost.it/2019/04/01/lacciaieria-intelligente-che-diventa-un-impianto-faro/ [Google Scholar]
  62. G. Bavestrelli, Metal Scrap Classification and Tracking at Ori Martin, in: Proc. of the Workshop on Green Steel by the EAF route, November 13–14, 2019, Bergamo (Italy) [Google Scholar]
  63. A. Ballarino, C. Brondi, A. Brusaferri, G. Chizzoli, The CPS and LCA modelling: An integrated approach in the environmental sustainability perspective, IFIP Adv. Inform. Commun. Technol. 506, 543–552 (2017) [Google Scholar]
  64. “Circular Economy Action Plan: For a cleaner and more competitive Europe” within the framework of the “European Green Deal”, European Commission, 2020, https://ec.europa.eu/info/strategy/priorities-2019-2024/european-green-deal/actions-being-taken-eu_en (last access January 23, 2021) [Google Scholar]
  65. S. Popper, S. Bankes, R. Callaway, D. DeLaurentis, in: System-of-Systems Symposium: Report on a Summer Conversation, July 21–22, 2004, Potomac Institute for Policy Studies, Arlington, VA [Google Scholar]
  66. A. Moazed, Modern Monopolies, Macmillan, 2016, 30 p. [Google Scholar]
  67. https://www2.deloitte.com/us/en/insights/topics/strategy/as-a-service-business-model-flexible-consumption.html (last access January 27, 2021) [Google Scholar]

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