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

An overview of the recent developments of time-series neural network modeling is
presented along with its use in model predictive control (MPC). A tutorial on the construction …

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 …

The growth of data collection in industrial processes has led to a renewed emphasis on the
development of data-driven soft sensors. A key step in building an accurate, reliable soft …

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 …

This paper aims to discuss and analyze the potentialities of Recurrent Neural Networks
(RNN) in control design applications. The main families of RNN are considered, namely …

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 …

Modern industrial processes often operate under different modes, which brings challenges
to model predictive control (MPC). Recently, most MPC related methods would establish …

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 …