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Exploring the System Dynamics of Industrial Symbiosis (IS) with Machine Learning (ML) Techniques—A Framework for a Hybrid-Approach

In: Advances and New Trends in Environmental Informatics

Author

Listed:
  • Anna Lütje

    (Leuphana University Lüneburg
    HTW Berlin, University of Applied Sciences)

  • Martina Willenbacher

    (Leuphana University Lüneburg
    HTW Berlin, University of Applied Sciences)

  • Martin Engelmann

    (HTW Berlin, University of Applied Sciences)

  • Christian Kunisch

    (HTW Berlin, University of Applied Sciences)

  • Volker Wohlgemuth

    (HTW Berlin, University of Applied Sciences)

Abstract

Artificial Intelligence (AI) is one of the driving forces of the digital revolution in terms of the areas of application that already exist and those that are emerging as potential. One can envision the application field of Industrial Symbiosis (IS), so which potential role can AI play in the context of IS systems and how can AI support/contribute to the facilitation of IS systems, which is explored in more detail in this paper. A systematic literature review was conducted to identify problem-/improvement driven fields of action in the context of IS and to present the current state of ICT tools for IS systems with corresponding implications. This led to the selection of suitable Machine Learning (ML) techniques, proposing a general framework of a combinatorial approach of Agent-Based Modelling (ABM) and ML for exploring the system dynamics of IS. This hybrid-approach opens up the simulation of scenarios with optimally utilized IS systems in terms of system adaptability and resilience.

Suggested Citation

  • Anna Lütje & Martina Willenbacher & Martin Engelmann & Christian Kunisch & Volker Wohlgemuth, 2020. "Exploring the System Dynamics of Industrial Symbiosis (IS) with Machine Learning (ML) Techniques—A Framework for a Hybrid-Approach," Progress in IS, in: Rüdiger Schaldach & Karl-Heinz Simon & Jens Weismüller & Volker Wohlgemuth (ed.), Advances and New Trends in Environmental Informatics, pages 117-130, Springer.
    • Handle: RePEc:spr:prochp:978-3-030-30862-9_9
    DOI: 10.1007/978-3-030-30862-9_9
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    Cited by:

    1. Qinglan Liu & Adriana Hofmann Trevisan & Miying Yang & Janaina Mascarenhas, 2022. "A framework of digital technologies for the circular economy: Digital functions and mechanisms," Business Strategy and the Environment, Wiley Blackwell, vol. 31(5), pages 2171-2192, July.
    2. Lara Waltersmann & Steffen Kiemel & Julian Stuhlsatz & Alexander Sauer & Robert Miehe, 2021. "Artificial Intelligence Applications for Increasing Resource Efficiency in Manufacturing Companies—A Comprehensive Review," Sustainability, MDPI, vol. 13(12), pages 1-26, June.
    3. Anna Lütje & Volker Wohlgemuth, 2020. "Requirements Engineering for an Industrial Symbiosis Tool for Industrial Parks Covering System Analysis, Transformation Simulation and Goal Setting," Administrative Sciences, MDPI, vol. 10(1), pages 1-24, February.

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