Artificial neural networks and data fusion enable concentration predictions for inline process analytics-Reference-Cited by-同舟云学术

Artificial neural networks and data fusion enable concentration predictions for inline process analytics

Published:2022 Issue:4 Volume:1 Page:405-412
ISSN:2635-098X
Container-title:Digital Discovery
language:en
Short-container-title:Digital Discovery

Author:

Sagmeister Peter¹²^ORCID,Hierzegger Robin¹²^ORCID,Williams Jason D.¹²^ORCID,Kappe C. Oliver¹²^ORCID,Kowarik Stefan²^ORCID

Affiliation:

1. Center for Continuous Flow Synthesis and Processing (CCFLOW), Research Center Pharmaceutical Engineering (RCPE), Inffeldgasse 13, 8010 Graz, Austria

2. Institute of Chemistry, University of Graz, NAWI Graz, Heinrichstrasse 28, A-8010 Graz, Austria

Abstract

Artificial neural networks (ANNs) facilitate rapid quantification of process spectra from inline process analytical technologies. Data fusion also enables combination of multiple data sources, resulting in better quality concentration measurements.

Funder

Steirische Wirtschaftsförderungsgesellschaft

Österreichische Forschungsförderungsgesellschaft

Publisher

Royal Society of Chemistry (RSC)

Link

http://pubs.rsc.org/en/content/articlepdf/2022/DD/D2DD00006G

Reference70 articles.

1. On-demand continuous-flow production of pharmaceuticals in a compact, reconfigurable system

2. A robotic platform for flow synthesis of organic compounds informed by AI planning

3. Reconfigurable system for automated optimization of diverse chemical reactions

4. Automated radial synthesis of organic molecules

5. Organic synthesis in a modular robotic system driven by a chemical programming language

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