(624d) A Graph-Analytical Approach for the Development of Circular Economy Networks

Industrial Symbiosis (IS) has proven to be an important tool for improving business sustainability with numerous environmental and economic benefits for all members involved. The breadth of successful examples all over the world and the huge amounts of unexploited waste indicates huge potential. However, many attempts to implement innovative IS practices have failed due to lack of information, knowledge or understanding of the system’s complexity. The large amount of unstructured data from different sources renders the analysis a challenging process and the different background knowledge of associated partners makes it difficult to identify and evaluate potential value chains. IS networks are dynamic and require partners who can adapt to the constantly changing environment. In order to capture the complexity of these networks and make fast, accurate and data-driven decisions, companies must implement innovative technologies leveraging the abundance of data available.

This paper proposes graph databases, as the appropriate tools for modeling IS networks and automate decision making. Graph databases have flexible schema, can handle very large sets of semi-structured data, which can describe effectively the varied IS networks and at the same time can perform efficiently complex queries. The proposed model is composed of nodes with 3 labels: Companies, Processes and Materials, which connect to each other with relationships (edges) of different labels. Each node and each edge contains different properties depending on its label. Properties regarding materials can be physical, chemical, biological etc. whereas properties regarding processes can be temperature, pressure, CAPEX, OPEX etc. Properties are powerful tools that allow the user to query, having incomplete knowledge.

With this representation, the user can model large and complex IS networks involving heterogeneous industries and a wide range of by-products exchanged. The graph can be used for designing zero waste Eco-industrial parks, or for identifying potential networks between industrial units at existing industrial parks. The graph can be enriched as more technologies are developed or more practices are applied. Connections can be either implemented or potential, depending on the development stage. Furthermore quantitative data can be used for evaluating possible synergies with mathematical optimization techniques.

The graph can be used for matching waste to process, waste to company, company to company or querying using only desired properties. We have demonstrated examples in agricultural activities, rural and municipal communities and ports, with reliable results. In conclusion, the developed model allows real time analysis about potential IS networks which include several different partners and synergies that would otherwise be extremely difficult to identify.