Fermenter control has been an active area of research and has attracted more attention in
recent years. This is due to the new developments in other related areas which can be …

As a result of good modeling capabilities, neural networks have been used extensively for a
number of chemical engineering applications such as sensor data analysis, fault detection …

Over the past few decades, several data‐driven methods have been developed for
identifying a model that accurately describes the process dynamics. Lately, sparse …

Successful industrial applications and favorable comparisons with conventional alternatives
have motivated the development of a large number of schemes for neural-network-based …

We devise a novel technique to control the shape of polymer molecular weight distributions
(MWDs) in atom transfer radical polymerization (ATRP). This technique makes use of recent …

This paper experimentally demonstrates a novel approach for process control that uses
neural networks for gain prediction within nonlinear, multivariable and constraint …

Concentrations of substrates, glucose, and ammionia in biological processes have been on-
line monitored by using glucose-flow injection (FIA) and ammonia-FIA systems. Based on …

Active process control of nitric oxide (NO) emissions from a two-stage combustor burning
ethylene (doped with ammonia) in air is demonstrated using two clusters of feed-forward …

A lot of effort has been put into the modelling of non-linear dynamic systems owing to their
presence 'in every day life'. Neural networks are often used as modelling tools, since they …

Deep learning (DL) is a subfield of neural network (NN) and machine learning (ML) that
deals with deep NN-based modeling. This paper reviews the recent methods and …