Keeping an "eye" on the experiment: computer vision for real-time monitoring and control.
Chem Sci
; 15(4): 1271-1282, 2024 Jan 24.
Article
en En
| MEDLINE
| ID: mdl-38274057
ABSTRACT
This work presents a generalizable computer vision (CV) and machine learning model that is used for automated real-time monitoring and control of a diverse array of workup processes. Our system simultaneously monitors multiple physical outputs (e.g., liquid level, homogeneity, turbidity, solid, residue, and color), offering a method for rapid data acquisition and deeper analysis from multiple visual cues. We demonstrate a single platform (consisting of CV, machine learning, real-time monitoring techniques, and flexible hardware) to monitor and control vision-based experimental techniques, including solvent exchange distillation, antisolvent crystallization, evaporative crystallization, cooling crystallization, solid-liquid mixing, and liquid-liquid extraction. Both qualitative (video capturing) and quantitative data (visual outputs measurement) were obtained which provided a method for data cross-validation. Our CV model's ease of use, generalizability, and non-invasiveness make it an appealing complementary option to in situ and real-time analytical monitoring tools and mathematical modeling. Additionally, our platform is integrated with Mettler-Toledo's iControl software, which acts as a centralized system for real-time data collection, visualization, and storage. With consistent data representation and infrastructure, we were able to efficiently transfer the technology and reproduce results between different labs. This ability to easily monitor and respond to the dynamic situational changes of the experiments is pivotal to enabling future flexible automation workflows.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Tipo de estudio:
Qualitative_research
Idioma:
En
Revista:
Chem Sci
Año:
2024
Tipo del documento:
Article
Pais de publicación:
Reino Unido