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Journal ArticleDOI

Hybrid modelling of biochemical processes: A comparison with the conventional approach

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TLDR
In this article, the authors compared three modeling approaches, viz. the conventional mechanistic approach, formulations based on different artificial neural network (ANN) topologies and a hybrid mechanistic-ANN structure.
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This article is published in Computers & Chemical Engineering.The article was published on 1997-05-20. It has received 95 citations till now. The article focuses on the topics: Test data.

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Applications of hybrid models in chemical, petroleum, and energy systems: A systematic review

TL;DR: Different sub-models, hybridization strategies, structural designs, screening criteria, and new directions in hybrid modeling are reviewed, with focus on the corresponding applications in chemical, petroleum, and energy systems.
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Soft-sensor development for fed-batch bioreactors using support vector regression

TL;DR: The results presented here clearly indicate that the SVR is an attractive alternative to artificial neural networks for the development of soft-sensors in bioprocesses.
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Combining first principles modelling and artificial neural networks: a general framework

TL;DR: A bioreactor dynamical hybrid model is proposed that combines first principles modelling with artificial neural networks (ANNs) and the cell population system is represented by an adjustable mixture of neural network and mechanistic representations.
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Tuning of observer-based estimators: theory and application to the on-line estimation of kinetic parameters

TL;DR: A systematic tuning approach that allows a decoupled estimation of each parameter and the assignment of the estimator dynamics independently of the process dynamics is proposed.
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Knowledge-based hybrid modelling of a batch crystallisation when accounting for nucleation, growth and agglomeration phenomena

TL;DR: In this paper, a hybrid model is proposed, which combines a partial mechanistic model that reflects the general mass, energy and population balances with a neural network to express growth rate, nucleation kinetics and agglomeration phenomena.
References
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Book

System Identification: Theory for the User

Lennart Ljung
TL;DR: Das Buch behandelt die Systemidentifizierung in dem theoretischen Bereich, der direkte Auswirkungen auf Verstaendnis and praktische Anwendung der verschiedenen Verfahren zur IdentifIZierung hat.
Book

Neural network design

TL;DR: This book, by the authors of the Neural Network Toolbox for MATLAB, provides a clear and detailed coverage of fundamental neural network architectures and learning rules, as well as methods for training them and their applications to practical problems.
Journal ArticleDOI

Growth of Saccharomyces cerevisiae is controlled by its limited respiratory capacity: Formulation and verification of a hypothesis

TL;DR: A novel mechanistic model for the growth of baker's yeast on glucose based on the fact that glucose degradation proceeds via two pathways under conditions of aerobic ethanol formation allows the prediction of experimental data without parameter adaption in a biologically dubious manner.
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