Abstract:
Recently, continuous production of perovskite quantum dots (QDs) has received substantial attention, and has also posed certain engineering challenges. Specifically, an a...Show MoreMetadata
Abstract:
Recently, continuous production of perovskite quantum dots (QDs) has received substantial attention, and has also posed certain engineering challenges. Specifically, an absence of well established crystallization models, lack of batch-to-continuous scale up studies, and unavailability of set-point tracking platforms are the major roadblocks. In this work, we tackle these challenges by (a) proposing a first-principled kinetic Monte Carlo (kMC) model to describe crystallization kinetics, (b) constructing a multiscale model to design a slug flow crystallizer (SFC) for mass production of QDs, and (c) formulating an optimization problem for optimal operation of the SFC. Furthermore, the complex fluid dynamics in a SFC was modeled using ANSYS Fluent and was integrated with a continuum crystallization model, which has not been addressed by the previous studies. In the optimal operation problem, an artificial neural network (ANN) based surrogate model was coupled with the multiscale model for better computational efficiency, and to ensure a good set-point tracking performance.
Published in: 2021 American Control Conference (ACC)
Date of Conference: 25-28 May 2021
Date Added to IEEE Xplore: 28 July 2021
ISBN Information: