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

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Advances and New Trends in Environmental Informatics

Part of the book series: Progress in IS ((PROIS))

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.

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Acknowledgements

This doctoral research work is co-financed by funds from the Berlin Equal Opportunities Programme.

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Authors and Affiliations

  1. Institute of Environmental Communication, Leuphana University Lüneburg, Universitätsallee 1, 21335, Lüneburg, Germany

    Anna Lütje & Martina Willenbacher

  2. School of Engineering—Technology and Life, HTW Berlin, University of Applied Sciences, Treskowallee 8, 10318, Berlin, Germany

    Anna Lütje, Martina Willenbacher, Martin Engelmann, Christian Kunisch & Volker Wohlgemuth

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  1. Anna Lütje
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  2. Martina Willenbacher
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  5. Volker Wohlgemuth
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Corresponding author

Correspondence to Anna Lütje .

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Editors and Affiliations

  1. CESR, University of Kassel, Kassel, Germany

    Rüdiger Schaldach

  2. CESR, University of Kassel, Kassel, Germany

    Karl-Heinz Simon

  3. Leibniz Supercomputing Centre, Bavarian Academy of Sciences and Humanities, Munich, Germany

    Jens Weismüller

  4. Department of Engineering - Technology and Life, HTW Berlin - University of Applied Sciences, Berlin, Germany

    Volker Wohlgemuth

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Lütje, A., Willenbacher, M., Engelmann, M., Kunisch, C., Wohlgemuth, V. (2020). Exploring the System Dynamics of Industrial Symbiosis (IS) with Machine Learning (ML) Techniques—A Framework for a Hybrid-Approach. In: Schaldach, R., Simon, KH., Weismüller, J., Wohlgemuth, V. (eds) Advances and New Trends in Environmental Informatics. Progress in IS. Springer, Cham. https://doi.org/10.1007/978-3-030-30862-9_9

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  • DOI: https://doi.org/10.1007/978-3-030-30862-9_9

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