In this chapter we describe a software tool for modelling fermentation processes, the FerMoANN, which allows researchers in biology and biotechnology areas to access the potential of Artificial Neural Networks (ANNs) for this task. The FerMoANN is tested and validated using two fermentation processes, an Escherichia coli recombinant protein production and the production of a secreted protein with Saccharomyces cerevisiae in fed-batch reactors. The application to these two case studies, tested for different configurations of feedforward ANNs, illustrate the usefulness of these structures, when trained according to a supervised learning paradigm
According to our best knowledge, this is the first report applying Artificial neural networks (ANN) ...
An algorithm using feedforward neural network model for determining optimal substrate feeding polici...
In view of the looming energy crisis as a result of depleting fossil fuel resources and environmenta...
Publicado em "2nd International Workshop on Practical Applications of Computational Biology and Bioi...
Since artificial neural networks represent a commercially attractive tool for process modelling and ...
Abstract — The growth rate of the micro-organisms in bio-logical reactors is described by some compl...
This work provides a manual design space exploration regarding the structure, type, and inputs of a ...
In this work a hybrid neural modelling methodology, which combines mass balance equations with funct...
In the present work, the fermentation process aimed at obtaining bio-ethanol starting from ricotta c...
Expert systems and neural networks are new tools for the control of fermentation processes. With exp...
In this paper the bioethanol production in batch culture by free Saccharomyces cerevisiae cells fro...
Machine learning through artificial neural networks have emerged as vital tools to predict chemical ...
Fermentation is a complex phenomenon well studied which still provides challenges to brewers. In thi...
This dissertation aims to develop neural networks for bioprocess systems. It is shown that back-prop...
Methods that can provide adequate accuracy in the estimation of variables from incomplete informati...
According to our best knowledge, this is the first report applying Artificial neural networks (ANN) ...
An algorithm using feedforward neural network model for determining optimal substrate feeding polici...
In view of the looming energy crisis as a result of depleting fossil fuel resources and environmenta...
Publicado em "2nd International Workshop on Practical Applications of Computational Biology and Bioi...
Since artificial neural networks represent a commercially attractive tool for process modelling and ...
Abstract — The growth rate of the micro-organisms in bio-logical reactors is described by some compl...
This work provides a manual design space exploration regarding the structure, type, and inputs of a ...
In this work a hybrid neural modelling methodology, which combines mass balance equations with funct...
In the present work, the fermentation process aimed at obtaining bio-ethanol starting from ricotta c...
Expert systems and neural networks are new tools for the control of fermentation processes. With exp...
In this paper the bioethanol production in batch culture by free Saccharomyces cerevisiae cells fro...
Machine learning through artificial neural networks have emerged as vital tools to predict chemical ...
Fermentation is a complex phenomenon well studied which still provides challenges to brewers. In thi...
This dissertation aims to develop neural networks for bioprocess systems. It is shown that back-prop...
Methods that can provide adequate accuracy in the estimation of variables from incomplete informati...
According to our best knowledge, this is the first report applying Artificial neural networks (ANN) ...
An algorithm using feedforward neural network model for determining optimal substrate feeding polici...
In view of the looming energy crisis as a result of depleting fossil fuel resources and environmenta...