(219a) Finding Optimal Reaction Routes in Chemical Reaction Networks

Large chemical databases, such as Reaxys®[1], containing more than 119 Million compounds and more than 46 Million reactions, have previously been used to investigate the connectedness of chemical space.¹ Big data sets of known chemical reactions have been explored as the Network of Organic Chemistry by representing molecules as nodes and reactions as edges.² Amongst others, the networks statistics,¹ the networks historic evolution,³ and its usefulness for reaction development⁴ and retrosynthesis⁵ have recently been recognised.

Many questions about the chemical space and its applications, however, remain unsolved, e.g. the automated reaction route selection. That is choosing the best route from certain feedstock via multiple intermediates to desired end-products, whereby beststill has to be defined. The problem has received much attention at present with regard to the growing need for more sustainable reaction routes and routes developed on novel feedstocks, i.e. renewable feedstocks and waste streams.⁴ Optimisation based methods, e.g. the Reaction Network Flux Analysis (RNFA), have been recognised to enable route selection with manually mined and relatively small data sets (e.g. 80 substances and 116 reactions)⁶. It was firstly introduced by Voll and Marquardt for the evaluation of reaction routes for biofuels⁶ and has been further developed for instance to extend the route evaluation criteria,⁷ and to account for geographic locations and supply chain considerations.⁸ Furthermore, Zhang et al. had implemented the RNFA in order to evaluate the reaction pathways for the production of biopolymers.⁹

Herein, automatable methods to detect optimal reaction routes from very large sets of recorded reactions are discussed. Firstly, we investigate two problem formulations for automatically mined chemical data sets.¹⁰ In addition to the RNFA, we introduce a Petri Net Optimisation (PNO) formalism which explicitly takes the order in which reactions occur into account. In network optimisation this can enable constraints on reaction cycles and intrinsically enables us to regard non-flux related reaction costs. Secondly, we explore chemical heuristics and sustainability metrics derivable from digital data recording. We trim down data recordings using heuristics, e.g. variation of carbon atoms and exclusion of pre-defined chemical structures. Balance equations are formulated as constraints and a variety of sustainability metrics is explored as objective functions. We demonstrated the potential of the proposed method on an illustrative working example and a relevant chemical case study for sustainable production of citral from -pinene. Citral is an important compound in the fragrance and flavour industry and also an intermediate for pharmaceutical processes;¹¹ -pinene is has been shown as a valid starting material due to its structure, its occurrence in non-edible plants (pine trees)¹² and as co-product from the paper industry.¹³

This work bundles present knowledge and elucidates a roadmap for chemical process route selection as a driver for the transition towards a sustainable chemical industry.

Acknowledgments: 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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