(579e) Strategic Molecules in Large Reaction Networks for Circular Chemical Economy

Chemical supply chains are currently based on linear valorisation methods: we extract raw materials – we process a variety of intermediates – we use products – and we dispose of waste. The depletion of fossil resources,¹ the generation of large amounts of waste,² and overconsumption or other societal implications ³ are only a few of the many disadvantages of linear economic models. A key concept of the future sustainable society is the transition to a circular industrial structure, requiring the reintegration of waste process streams as feedstock.

One of the fundamental challenges for building circular process structures is the question where to integrate the often highly functionalised waste as a novel feedstock. Many works focus on specific direct chemical transformation, e.g. the valorisation of crude-glycerol,⁴ the hydration reactions on crude sulphate turpentine (CST) components,⁵ or the conversion of lignocellulosic materials.⁶ There is also a trend within the literature which considers wider applicability of newly introduced streams and functionalities, e.g. by investigating possible bio-based building blocks for industries,⁷ or by highlighting the molecular geometries (privileged scaffolds) that lead to a wider range of potential end products.⁸

Due to the increase in available data in chemistry/chemical engineering, we can now intensively study the connectedness of chemical reactions and identify strategic integration points, i.e. strategic molecules, which are most relevant for overall chemical supply chains.⁹ Large sets of known chemical reactions have been investigated as the Network of Organic Chemistry by representing molecules as nodes and reactions as edges connecting the nodes.¹⁰ Inter alia, the networks historic evolution,¹¹ the statistics of the network,¹² and its usefulness for reaction development¹³ and retrosynthesis¹⁴ have recently been recognised. Thus, the data-driven networks are promising to also contribute to the understanding of integration points for novel feedstocks.

Herein, we introduce the concept of strategic molecules and present a method to identify those in chemical reaction networks. In particular, we mine Reaxys®[1] database for network assembly and describe molecules through a portfolio of graph theoretical features. We mined a network of more than 500,000 molecules connected through 955,905 edges and used graph centrality measures, e.g. betweenness centrality and pagerank, to describe the location of each molecule in the network. We then selected the strategic molecules based on an isolation forest detection algorithm with regard to the graph centrality values. The results reconcile a variety of chemical structures, main intermediates from the chemical industries as well as parts of the aforementioned bio-based building blocks and privileged scaffolds. The algorithm detects relevant chemical structures without any prior knowledge about the structures. In a case study on the use of CST we illustrate its potential for the valorisation of the waste stream. This indicates a generic solution of finding key intermediates in chemical supply chains, i.e. strategic molecules, which we hypothesise will play a major role for the transition towards circular economy.

Acknowledgment: J. M. Weber would like to thank the Department of Chemical Engineering for funding in the form of a PhD studentship and Philipp-M. Jacobs for his support and expertise. We gratefully acknowledge collaboration with RELX Intellectual Properties SA and their technical support, which enabled us to mine Reaxys. Reaxys data were made accessible to our research project via the Elsevier R&D Collaboration Network. This work was funded, in part, by the EPSRC project “Terpene-based manufacturing for sustainable chemical feedstocks” EP/K014889. This project was funded, in part, by the National Research Foundation (NRF), Prime Minister’s Office, Singapore under its Campus for Research Excellence and Technological Enterprise (CREATE) program as a part of the Cambridge Centre for Advanced Research and Education in Singapore Ltd (CARES).

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