Keeping an "eye" on the experiment: computer vision for real-time monitoring and control.

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 (vide

SUBMITTER: El-Khawaldeh R 

PROVIDER: S-EPMC10806693 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature