| Title |
Discovering Circular Process Solutions through Automated Reaction Network Optimization |
| ID_Doc |
6406 |
| Authors |
Weber, JM; Guo, Z; Lapkin, AA |
| Title |
Discovering Circular Process Solutions through Automated Reaction Network Optimization |
| Year |
2022 |
| Published |
Acs Engineering Au, 2, 4 |
| Abstract |
The transition toward a circular and biobased chemical industry is needed to cut global CO2 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 ss pinene to citral. Our results show that the tools are well suited to model circular process interactions within different environment scenarios. |
| PDF |
https://pubs.acs.org/doi/pdf/10.1021/acsengineeringau.2c00002
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