Industrial symbiosis involves creating integrated cycles of by-products and waste between networks of industrial actors in order to maximize economic value, while at the same time minimizing environmental strain. In such a network, the global environmental strain is no longer equal to the sum of the environmental strain of the individual actors, but it is dependent on how well the network performs as a whole. The development of methods to understand, manage or optimize such networks remains an open issue. In this paper we put forward a simulation model of by-product flow between industrial actors. The goal is to introduce a method for modelling symbiotic exchanges from a macro perspective. The model takes into account the effect of two main mechanisms on a multi-objective optimization of symbiotic processes. First it allows us to study the effect of geographical properties of the economic system, said differently, where actors are divided in space. Second, it allows us to study the effect of clustering complementary actors together as a function of distance, by means of a spatial correlation between the actors’ by-products. Our simulations unveil patterns that are relevant for macro-level policy. First, our results show that the geographical properties are an important factor for the macro performance of symbiotic processes. Second, spatial correlations, which can be interpreted as planned clusters such as Eco-industrial parks, can lead to a very effective macro performance, but only if these are strictly implemented. Finally, we provide a proof of concept by comparing the model to real world data from the European Pollutant Release and Transfer Register database using georeferencing of the companies in the dataset. This work opens up research opportunities in interactive data-driven models and platforms to support real-world implementation of industrial symbiosis.

Research on value pathways for organic wastes has been steadily increasing in recent decades. There have been few considerably broad overview studies of such materials and their valuation potential in the bio-based economy in part because of the vast multitude of materials and processes that can be used to produce energy carriers, chemicals, and materials of value. This article explores how automated data analysis approaches can help in analyzing large bodies of text to distill and present potential value pathways for secondary (waste) bio-based materials. The study employed multiple methods (literature collection, topic modelling, and co-occurrence analysis) on a collection of abstracts from 53,292 academic articles covering technologies, applications, and products (TAPs) for bio-based wastes. The results of both the topic modelling and co-occurrence analysis are presented as online interactive web pages. The topic modelling presented an overview of research clusters related to secondary organic resources, processes, and disciplines. The co-occurrence analysis helped to understand which TAPs are researched in relation to a broad spectrum of organic wastes. Co-occurrences were evaluated using the Normalized Pointwise Mutual Information measure to locate terms which co-occur more frequently than would be expected by chance. Through the use of detailed lists of organic wastes and TAPs, the co-occurrence method mapped out 7118 unique intersections between 473 specific wastes and 228 TAPs. This technique enables us to find seemingly non-obvious valorization pathways such as the re-use of oyster shells as catalysts for bio-diesel production and bioplastic production from brewery waste. While a proof-of-concept, this work points the way for using Big Data to suggest novel pathways for implementing the Circular Economy.