The chemical processing industry has relied on modeling techniques for process monitoring,
control, diagnosis, optimization, and design, especially since the third industrial revolution …

This paper discusses recent developments in the data-based modeling and control of
nonlinear chemical process systems using sparse identification of nonlinear dynamics …

This article is concerned with Model Predictive Control (MPC) algorithms that use Short
Term Memory (LSTM) and Gated Recurrent Unit (GRU) neural networks for prediction. For …

For prediction and real-time control tasks, machine-learning (ML)-based digital twins are
frequently employed. However, while these models are typically accurate, they are custom …

This work develops a framework for building machine learning models and machine-
learning-based predictive control schemes for batch crystallization processes. We consider …

In this work, physics-based recurrent neural network (RNN) modeling approaches are
proposed for a general class of nonlinear dynamic process systems to improve prediction …

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 …

In this work, we develop reduced-order machine learning models using feature selection
methods for distributed model predictive control (DMPC) of nonlinear processes …

Nonlinear model predictive control (NMPC) is one of the few control methods that can
handle multivariable nonlinear control systems with constraints. Gaussian processes (GPs) …

We present a machine learning-based predictive control scheme that integrates an online
update of the recurrent neural network (RNN) models to capture process nonlinear …