Neural Network Applications in Non-Linear Modelling of (Bio)Chemical Processes

Neural Network Applications in Non-Linear Modelling of (Bio)Chemical Processes

In recent years, neural networks have attracted much attention for their potential to address a number of difficult problems in modelling and controlling nonlinear dynamic systems, especially in (bio) chemical engineering. The objective of this paper is to review some of the most widely used approac...

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Main Authors: C. Renotte, A. Vande Wouwer, Ph. Bogaerts, M. Remy
Format: Article
Language:English
Published: SAGE Publishing 2001-09-01
Series:Measurement + Control
Online Access:https://doi.org/10.1177/002029400103400702
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spelling doaj-56a32d0af1114d918813d5798d73daac2020-11-25T03:09:24ZengSAGE PublishingMeasurement + Control0020-29402001-09-013410.1177/002029400103400702Neural Network Applications in Non-Linear Modelling of (Bio)Chemical ProcessesC. Renotte0A. Vande Wouwer1Ph. Bogaerts2M. Remy3 Laboratoire d'Automatique, Faculté Polytechnique de Mons, Belgium Laboratoire d'Automatique, Faculté Polytechnique de Mons, Belgium Service d'Automatique et d'Analyse des Systemes, Université Libre de Bruxelles, Belgium Laboratoire d'Automatique, Faculté Polytechnique de Mons, BelgiumIn recent years, neural networks have attracted much attention for their potential to address a number of difficult problems in modelling and controlling nonlinear dynamic systems, especially in (bio) chemical engineering. The objective of this paper is to review some of the most widely used approaches to neural-network-based modelling, including plain black box as well as hybrid neural network — first principles modelling. Two specific application examples are used for illustration purposes: a simple tank level-control system is studied in simulation while a challenging bioprocess application is investigated based on experimental data. These applications allow some original concepts and techniques to be introduced.https://doi.org/10.1177/002029400103400702
collection DOAJ
language English
format Article
sources DOAJ
author C. Renotte
A. Vande Wouwer
Ph. Bogaerts
M. Remy
spellingShingle C. Renotte
A. Vande Wouwer
Ph. Bogaerts
M. Remy
Neural Network Applications in Non-Linear Modelling of (Bio)Chemical Processes
Measurement + Control
author_facet C. Renotte
A. Vande Wouwer
Ph. Bogaerts
M. Remy
author_sort C. Renotte
title Neural Network Applications in Non-Linear Modelling of (Bio)Chemical Processes
title_short Neural Network Applications in Non-Linear Modelling of (Bio)Chemical Processes
title_full Neural Network Applications in Non-Linear Modelling of (Bio)Chemical Processes
title_fullStr Neural Network Applications in Non-Linear Modelling of (Bio)Chemical Processes
title_full_unstemmed Neural Network Applications in Non-Linear Modelling of (Bio)Chemical Processes
title_sort neural network applications in non-linear modelling of (bio)chemical processes
publisher SAGE Publishing
series Measurement + Control
issn 0020-2940
publishDate 2001-09-01
description In recent years, neural networks have attracted much attention for their potential to address a number of difficult problems in modelling and controlling nonlinear dynamic systems, especially in (bio) chemical engineering. The objective of this paper is to review some of the most widely used approaches to neural-network-based modelling, including plain black box as well as hybrid neural network — first principles modelling. Two specific application examples are used for illustration purposes: a simple tank level-control system is studied in simulation while a challenging bioprocess application is investigated based on experimental data. These applications allow some original concepts and techniques to be introduced.
url https://doi.org/10.1177/002029400103400702
work_keys_str_mv AT crenotte neuralnetworkapplicationsinnonlinearmodellingofbiochemicalprocesses
AT avandewouwer neuralnetworkapplicationsinnonlinearmodellingofbiochemicalprocesses
AT phbogaerts neuralnetworkapplicationsinnonlinearmodellingofbiochemicalprocesses
AT mremy neuralnetworkapplicationsinnonlinearmodellingofbiochemicalprocesses
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