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- Title
Monitoring Rheological Changes Using Acoustic Emissions for Complex Formulated Fluids Manufacturing.
- Authors
Hefft, Daniel Ingo; Blake, Natasha Rosanne; Farhoud, Aziza; Farrar, Ellie; O'Sullivan, Jonathan James; Alberini, Federico
- Abstract
The measurement capabilities of a newly developed in‐situ rheometric device based on a single passive acoustic emission sensor and machine learning algorithms were investigated. Two surfactant structured fluids demonstrating complex non‐Newtonian rheology (Power‐law and Herschel‐Bulkley models) were examined. Furthermore, a static evaluation on the laboratory scale in comparison to dynamic processing on the pilot scale was conducted. The results indicate that the machine learning algorithms of this technology can identify, in > 90 % of scenarios, the correct type of rheology or the manufacturing process step across both scales. This identification is based on solving a classification problem using quadratic support vector machine learning algorithms, which have proven to deliver the most robust predictions across a choice of 24 different algorithms tested. Additionally, a new format of in situ rheology display was introduced, referred to as RRF™ factor.
- Subjects
ACOUSTIC emission; NON-Newtonian fluids; MACHINE learning; COMPLEX fluids; PSEUDOPLASTIC fluids; HERSCHEL-Bulkley model; SUPPORT vector machines
- Publication
Chemical Engineering & Technology, 2023, Vol 46, Issue 10, p2245
- ISSN
0930-7516
- Publication type
Article
- DOI
10.1002/ceat.202300191