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

Anna Lütje (), Martina Willenbacher (), Martin Engelmann (), Christian Kunisch () and Volker Wohlgemuth ()
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Anna Lütje: Leuphana University Lüneburg
Martina Willenbacher: Leuphana University Lüneburg
Martin Engelmann: HTW Berlin, University of Applied Sciences
Christian Kunisch: HTW Berlin, University of Applied Sciences
Volker Wohlgemuth: HTW Berlin, University of Applied Sciences

A chapter in Advances and New Trends in Environmental Informatics, 2020, pp 117-130 from Springer

Abstract: 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.

Keywords: Artificial intelligence; Machine learning; Industrial symbiosis; Industrial ecology; Circular economy; Resource efficiency (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1007/978-3-030-30862-9_9

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