Repository logo
 

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

Accepted version
Peer-reviewed

Loading...
Thumbnail Image

Type

Article

Change log

Authors

Lapkin, AA 
Heer, PK 
Jacob, P-M 
Hutchby, M 
Cunningham, W 

Abstract

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.

Description

Keywords

0801 Artificial Intelligence and Image Processing, Generic Health Relevance

Journal Title

Faraday Discuss

Conference Name

Journal ISSN

1359-6640
1364-5498

Volume Title

202

Publisher

Royal Society of Chemistry (RSC)
Sponsorship
Engineering and Physical Sciences Research Council (EP/K014889/1)
This work was funded in part by the EPSRC project “Terpenebased Manufacturing for Sustainable Chemical Feedstocks” EP/K014889. The PhD scholarship of WC is funded by the EPSRC Doctoral Training Centre in Sustainable Chemical Technologies (EP/G03768X/1). We gratefully acknowledge collaboration with RELX Intellectual Properties SA and their technical support, which enabled us to mine REAXYS. PMJ is grateful to Peterhouse and the Cambridge Trust for PhD scholarships.