The use of feedforward neural networks for process modeling has proven very successful for
steadystate applications, but suitable applications for dynamic systems are still being …

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 …

The use of artificial neural networks in model based control, both as process models and as
controllers, is investigated: two nonlinear model based control strategies, internal model …

In this paper, a continuous time recurrent neural network (CTRNN) is developed to be used
in nonlinear model predictive control (NMPC) context. The neural network represented in a …

This work focuses on machine learning modeling and predictive control of nonlinear
processes using noisy data. We use long short‐term memory (LSTM) networks with training …

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 …

Artificial Neural Networks (ANN) have been used for a wide variety of chemical applications
because of their ability to learn system features. This paper presents the use of feedforward …

Recurrent neural networks (RNNs) have been widely used to model nonlinear dynamic
systems using time-series data. While the training error of neural networks can be rendered …

Abstract Machine learning techniques have demonstrated their capability in capturing
dynamic behavior of complex, nonlinear chemical processes from operational data. As full …

Design and implementation are studied for a neural network-based predictive controller
meant to govern the dynamics of non-linear processes. The advantages of using neural …