Computer vision for kinetic analysis of lab- and process-scale mixing phenomena

Henry Barrington; Alan  Dickinson; Jake McGuire; Chunhui Yan; Marc Reid

doi:10.1021/acs.oprd.2c00216

Computer vision for kinetic analysis of lab- and process-scale mixing phenomena

Henry Barrington, Alan Dickinson, Jake McGuire, Chunhui Yan, Marc Reid

Pure And Applied Chemistry

Research output: Contribution to journal › Article › peer-review

12 Citations (Scopus)

96 Downloads (Pure)

Abstract

A software platform for the computer vision-enabled analysis of mixing phenomena of relevance to process scale-up is described. By bringing new and known time-resolved mixing metrics under one platform, hitherto unavailable comparisons of pixel-derived mixing metrics are exemplified across non-chemical and chemical processes. The analytical methods described are applicable using any camera and across an appreciable range of reactor scales, from development through to process scale-up. A case study in nucleophilic aromatic substitution run on a 5 L scale in a stirred tank reactor shows how camera and offline concentration analyses can be correlated. In some cases, it can be shown that camera data hold the power to predict reaction progress.

Original language	English
Pages (from-to)	3073-3088
Number of pages	16
Journal	Organic Process Research and Development
Volume	26
Issue number	11
Early online date	4 Nov 2022
DOIs	https://doi.org/10.1021/acs.oprd.2c00216
Publication status	Published - 18 Nov 2022

Keywords

mixing
computer vision
kinetics
imaging

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10.1021/acs.oprd.2c00216Licence: CC BY 4.0

Barrington-etal-OPRD-2022-Computer-vision-for-kinetic-analysis-of-labFinal published version, 11.4 MBLicence: CC BY 4.0

Computer Vision for Analytical Chemistry (CVAC): Scalable Productivity for Chemical Manufacturing- UKRI Future Leaders Fellowship
Reid, M. (Fellow)
MRC (Medical Research Council)
1/02/21 → 31/01/28
Project: Research Fellowship

Cite this

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abstract = "A software platform for the computer vision-enabled analysis of mixing phenomena of relevance to process scale-up is described. By bringing new and known time-resolved mixing metrics under one platform, hitherto unavailable comparisons of pixel-derived mixing metrics are exemplified across non-chemical and chemical processes. The analytical methods described are applicable using any camera and across an appreciable range of reactor scales, from development through to process scale-up. A case study in nucleophilic aromatic substitution run on a 5 L scale in a stirred tank reactor shows how camera and offline concentration analyses can be correlated. In some cases, it can be shown that camera data hold the power to predict reaction progress.",

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AU - Barrington, Henry

AU - Dickinson, Alan

AU - McGuire, Jake

AU - Yan, Chunhui

AU - Reid, Marc

PY - 2022/11/18

Y1 - 2022/11/18

N2 - A software platform for the computer vision-enabled analysis of mixing phenomena of relevance to process scale-up is described. By bringing new and known time-resolved mixing metrics under one platform, hitherto unavailable comparisons of pixel-derived mixing metrics are exemplified across non-chemical and chemical processes. The analytical methods described are applicable using any camera and across an appreciable range of reactor scales, from development through to process scale-up. A case study in nucleophilic aromatic substitution run on a 5 L scale in a stirred tank reactor shows how camera and offline concentration analyses can be correlated. In some cases, it can be shown that camera data hold the power to predict reaction progress.

AB - A software platform for the computer vision-enabled analysis of mixing phenomena of relevance to process scale-up is described. By bringing new and known time-resolved mixing metrics under one platform, hitherto unavailable comparisons of pixel-derived mixing metrics are exemplified across non-chemical and chemical processes. The analytical methods described are applicable using any camera and across an appreciable range of reactor scales, from development through to process scale-up. A case study in nucleophilic aromatic substitution run on a 5 L scale in a stirred tank reactor shows how camera and offline concentration analyses can be correlated. In some cases, it can be shown that camera data hold the power to predict reaction progress.

KW - mixing

KW - computer vision

KW - kinetics

KW - imaging

U2 - 10.1021/acs.oprd.2c00216

DO - 10.1021/acs.oprd.2c00216

M3 - Article

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SP - 3073

EP - 3088

JO - Organic Process Research and Development

JF - Organic Process Research and Development

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ER -

Computer vision for kinetic analysis of lab- and process-scale mixing phenomena

Abstract

Keywords

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Projects

Computer Vision for Analytical Chemistry (CVAC): Scalable Productivity for Chemical Manufacturing- UKRI Future Leaders Fellowship

Cite this