Exploring the potential of time-series transformers for process modeling and control in chemical systems: an inevitable paradigm shift?
N Sitapure, JSI Kwon - Chemical Engineering Research and Design, 2023 - Elsevier
The last two years have seen groundbreaking advances in natural language processing
(NLP) with the advent of applications like ChatGPT, Codex, and ChatSonic. This revolution …
(NLP) with the advent of applications like ChatGPT, Codex, and ChatSonic. This revolution …
Machine learning modeling and predictive control of the batch crystallization process
This work develops a framework for building machine learning models and machine-
learning-based predictive control schemes for batch crystallization processes. We consider …
learning-based predictive control schemes for batch crystallization processes. We consider …
Introducing hybrid modeling with time-series-transformers: A comparative study of series and parallel approach in batch crystallization
N Sitapure, J Sang-Il Kwon - Industrial & Engineering Chemistry …, 2023 - ACS Publications
Given the hesitance surrounding the direct implementation of black-box tools due to safety
and operational concerns, fully data-driven deep-neural-network (DNN)-based digital twins …
and operational concerns, fully data-driven deep-neural-network (DNN)-based digital twins …
[HTML][HTML] Physics-informed machine learning for MPC: Application to a batch crystallization process
This work presents a framework for develo** physics-informed recurrent neural network
(PIRNN) models and PIRNN-based predictive control schemes for batch crystallization …
(PIRNN) models and PIRNN-based predictive control schemes for batch crystallization …
Protein crystal based materials for nanoscale applications in medicine and biotechnology
LF Hartje, CD Snow - Wiley Interdisciplinary Reviews …, 2019 - Wiley Online Library
The porosity, order, biocompatibility, and chirality of protein crystals has motivated interest
from diverse research domains including materials science, biotechnology, and medicine …
from diverse research domains including materials science, biotechnology, and medicine …
Neural network-based model predictive control for thin-film chemical deposition of quantum dots using data from a multiscale simulation
N Sitapure, JSI Kwon - Chemical Engineering Research and Design, 2022 - Elsevier
Recently, thin-film deposition of quantum dot (QDs) to manufacture solar cells and displays
have received significant attention due to the lucrative optoelectronic properties of these …
have received significant attention due to the lucrative optoelectronic properties of these …
Measurement, modelling, and closed-loop control of crystal shape distribution: Literature review and future perspectives
CY Ma, JJ Liu, XZ Wang - Particuology, 2016 - Elsevier
Crystal morphology is known to be of great importance to the end-use properties of crystal
products, and to affect down-stream processing such as filtration and drying. However, it has …
products, and to affect down-stream processing such as filtration and drying. However, it has …
Robust machine learning modeling for predictive control using Lipschitz-constrained neural networks
Neural networks (NNs) have emerged as a state-of-the-art method for modeling nonlinear
systems in model predictive control (MPC). However, the robustness of NNs, in terms of …
systems in model predictive control (MPC). However, the robustness of NNs, in terms of …
Stochastic optimal control of mesostructure of supramolecular assemblies using dissipative particle dynamics and dynamic programming with experimental validation
The self-assembly process, where molecules form complex structures through interaction
forces, has broad applications in various fields. However, controlling the dynamics of self …
forces, has broad applications in various fields. However, controlling the dynamics of self …
Nonlinear model predictive control of a multiscale thin film deposition process using artificial neural networks
G Kimaev, LA Ricardez-Sandoval - Chemical Engineering Science, 2019 - Elsevier
The purpose of this study was to employ Artificial Neural Networks (ANNs) to develop data-
driven models that would enable the shrinking horizon nonlinear model predictive control of …
driven models that would enable the shrinking horizon nonlinear model predictive control of …