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Digital Twins Based LCA and ISO 20140 for Smart and Sustainable Manufacturing Systems

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Sustainable Intelligent Systems

Part of the book series: Advances in Sustainability Science and Technology ((ASST))

Abstract

Currently, several facilities around the world, as part of government’s sustainable development strategies, are turning to the development of smart value chains that can leverage efficiently all of countries available and local resources. Smart factories vision embracing the fourth industrial revolution of manufacturing plants introduced a completely renewed industrial organization based on collaboration between human intelligence and capabilities and machines intelligence and computing capacities, manufacturing plants horizontal and vertical integration, and with particular interest for our paper end-to-end engineering. This collaborative endeavour has been translated in the field by a set of technologies for instance advanced simulation tools through digital twins. The use of these new resources and productivity enhancement has not been without consequences on natural ecosystems, which are increasingly subject to industrial competitiveness pressure. To counter the adverse environmental effects of industrial and technological growth, some manufacturers are developing simulation-based life cycle assessment approaches. Over the last few years, several research communities have explored the potential of simulation-based LCA method for the optimization of the environmental impact of production systems through the application of advanced artificial intelligence algorithms. However, only a limited number of these attempts have seen their practical implementation. Currently, digital twins’ technologies are rapidly expanding due to the advantages they offer for real-time simulation, multidimensional replication of industrial systems and end-to-end engineering. Through this work, we propose a generic solution based on digital twins’ technologies and ISO 20140 for real-time life cycle assessment and manufacturing systems sustainable optimization.

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Acknowledgements

Work carried out within the framework of the cooperation agreement for technological and scientific development concluded between the UM6P and the FRDISI.

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Authors and Affiliations

  1. National and High School of Electricity and Mechanic (ENSEM), HASSAN II University, Casablanca, Morocco

    Mezzour Ghita, Benhadou Siham & Medromi Hicham

  2. Research Foundation for Development and Innovation in Science and Engineering, Casablanca, Morocco

    Mezzour Ghita, Benhadou Siham & Medromi Hicham

  3. Innovation Lab for Operations (ILO), Mohammed VI Polytechnic University (UM6P), Ben Guerir, Morocco

    Mezzour Ghita & Hafid Griguer

Authors
  1. Mezzour Ghita
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  2. Benhadou Siham
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  3. Medromi Hicham
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  4. Hafid Griguer
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Correspondence to Mezzour Ghita .

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Editors and Affiliations

  1. Global Knowledge Research Foundation, Ahmedabad, Gujarat, India

    Amit Joshi

  2. School of Mathematics, Computer Science and Engineering, Liverpool Hope University, Liverpool, UK

    Atulya K. Nagar

  3. University of Costa Rica, San José, Costa Rica

    Gabriela Marín-Raventós

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Ghita, M., Siham, B., Hicham, M., Griguer, H. (2021). Digital Twins Based LCA and ISO 20140 for Smart and Sustainable Manufacturing Systems. In: Joshi, A., Nagar, A.K., Marín-Raventós, G. (eds) Sustainable Intelligent Systems. Advances in Sustainability Science and Technology. Springer, Singapore. https://doi.org/10.1007/978-981-33-4901-8_8

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