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

Abstract β-Carotene has a positive impact on human health as a precursor of vitamin A.
Building a kinetic model for its production using Saccharomyces cerevisiae in a batch …

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 …

This work develops a physics-informed transfer learning framework for modeling and control
of a nonlinear process network with limited training data. Unlike the conventional transfer …

Due to the occurrence of over-fitting at the learning phase, the modeling of chemical
processes via artificial neural networks (ANN) by using corrupted data (ie, noisy data) is an …

This work develops a transfer learning (TL) framework for modeling and predictive control of
nonlinear systems using recurrent neural networks (RNNs) with the knowledge obtained in …

Gasoline, as an extremely important petroleum product, is of great significance to ensure
people's living standards and maintain national energy security. In the actual gasoline …

Sparse identification of nonlinear dynamics (SINDy) is a recent nonlinear modeling
technique that has demonstrated superior performance in modeling complex time-series …

In order to approximate nonlinear dynamic systems utilizing time-series data, recurrent
neural networks (RNNs) and long short-term memory (LSTM) networks have frequently been …