Kinetic study in an automatic continuous‐flow photochemical platform with machine learning

In this work, we first solved the partial differential equation of the one‐dimensional axial diffusion model in an open‐source platform, that is, FEniCS, to explore the influence of the axial dispersion on the reaction yield‐to‐time profile. Then, we built an automatic platform, which included a pho...

Full description

Saved in:
Bibliographic Details
Published in:AIChE journal Vol. 69; no. 9
Main Authors: Wang, Yuhan, Shen, Chong, Qiu, Min, Shang, Minjing, Su, Yuanhai
Format: Journal Article
Language:English
Published: Hoboken, USA John Wiley & Sons, Inc 01.09.2023
American Institute of Chemical Engineers
Subjects:
automation
Axial diffusion
Genetic algorithms
kinetics
Light sources
Machine learning
Methylene blue
microreactors
Optimization
Partial differential equations
Photochemical machining
Photochemicals
photochemistry
Photolysis
Reaction mechanisms
ISSN:0001-1541, 1547-5905
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:In this work, we first solved the partial differential equation of the one‐dimensional axial diffusion model in an open‐source platform, that is, FEniCS, to explore the influence of the axial dispersion on the reaction yield‐to‐time profile. Then, we built an automatic platform, which included a photomicroreactor, a continuously controlled pump, a high‐power UV‐LED light source, an in‐line visible‐light absorbance analytical unit, and a Raspberry Pi based controlling unit. Moreover, steady‐state feeding and sampling functions could be realized in this continuous‐flow photochemical platform. The homogeneous photolysis of methylene blue and the photo‐Favorskii rearrangement synthesis of ibuprofen as the model reactions were used to validate the robustness of this automatic platform with unsteady‐state and steady‐state operations, through which kinetic study was carried out using genetic algorithm based symbolic regression, leading to deep understanding on reaction mechanisms and benefits for process optimization.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0001-1541
1547-5905
DOI:10.1002/aic.18102