Discovering Circular Process Solutions through Automated Reaction Network Optimization

Abstract

The transition toward a circular and biobased chemical industry is needed to cut global CO 2 emissions and limit the chemical industry's overall impact on the environment. However, the development of circular chemical reaction systems is challenging as it requires symbiotic sets of novel chemical reaction pathways and involves unconventional processing steps. We present a methodological pipeline for automated reaction network optimization. The tools can guide the development of circular processes on the reaction pathway level. Chemical big data combined with energetic assessment metrics and state-of-the-art decision-making has the potential to efficiently identify the most promising reaction systems. We mine large-scale chemical reaction data from Reaxys database and automate the screening of pathways based on chemical rules. We then approximate thermodynamic properties for exergy calculations of the prescreened pathways and formulate the optimization problem as linear programming and mixed-integer linear programming problem. The methodological workflow is illustrated in a case study on the conversion of βpinene to citral. Our results show that the tools are well suited to model circular process interactions within different environment scenarios.

Synthesis of Pyrrole‐2‐Carboxylic Acid from Cellulose‐ and Chitin‐Based Feedstocks Discovered by the Automated Route Search

“…[18] A workflow consisting of mining reaction network and exergy optimization algorithms was developed to optimize synthetic design, and was exemplified on a case study of conversion of βpinene to citral. [19] To address the issue of sparse data, we performed deep mining from reaction network down to molecular substructure and atom-level analysis. This was achieved by developing similarity-based heuristics and in-depth molecular structure analysis.…”

“…Our own focus was on developing a synthetic planning tool for practical applications, especially development of sustainable synthetic routes [18] . A workflow consisting of mining reaction network and exergy optimization algorithms was developed to optimize synthetic design, and was exemplified on a case study of conversion of β‐pinene to citral [19] . To address the issue of sparse data, we performed deep mining from reaction network down to molecular substructure and atom‐level analysis.…”