Automation of route identification and optimisation based on data-mining and chemical intuition

A. A. Lapkin, P. K. Heer, P. M. Jacob, M. Hutchby, W. Cunningham, S. D. Bull, M. G. Davidson

Research output: Contribution to journalArticlepeer-review

20 Citations (SciVal)


Data-mining of Reaxys and network analysis of the combined literature and in-house reactions set were used to generate multiple possible reaction routes to convert a bio-waste feedstock, limonene, into a pharmaceutical API, paracetamol. The network analysis of data provides a rich knowledge-base for generation of the initial reaction screening and development programme. Based on the literature and the in-house data, an overall flowsheet for the conversion of limonene to paracetamol was proposed. Each individual reaction-separation step in the sequence was simulated as a combination of the continuous flow and batch steps. The linear model generation methodology allowed us to identify the reaction steps requiring further chemical optimisation. The generated model can be used for global optimisation and generation of environmental and other performance indicators, such as cost indicators. However, the identified further challenge is to automate model generation to evolve optimal multi-step chemical routes and optimal process configurations.

Original languageEnglish
Pages (from-to)483-496
Number of pages14
JournalFaraday Discussions
Publication statusPublished - 1 Sept 2017

ASJC Scopus subject areas

  • Physical and Theoretical Chemistry


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