LAPSE:2020.1016
Published Article
LAPSE:2020.1016
Modelling Acetification with Artificial Neural Networks and Comparison with Alternative Procedures
Jorge E. Jiménez-Hornero, Inés María Santos-Dueñas, Isidoro García-García
October 6, 2020
Modelling techniques allow certain processes to be characterized and optimized without the need for experimentation. One of the crucial steps in vinegar production is the biotransformation of ethanol into acetic acid by acetic bacteria. This step has been extensively studied by using two predictive models: first-principles models and black-box models. The fact that first-principles models are less accurate than black-box models under extreme bacterial growth conditions suggests that the kinetic equations used by the former, and hence their goodness of fit, can be further improved. By contrast, black-box models predict acetic acid production accurately enough under virtually any operating conditions. In this work, we trained black-box models based on Artificial Neural Networks (ANNs) of the multilayer perceptron (MLP) type and containing a single hidden layer to model acetification. The small number of data typically available for a bioprocess makes it rather difficult to identify the most suitable type of ANN architecture in terms of indices such as the mean square error (MSE). This places ANN methodology at a disadvantage against alternative techniques and, especially, polynomial modelling.
Keywords
acetification, artificial neural networks, bioreactor systems, Modelling, multilayer perceptron, vinegar
Suggested Citation
Jiménez-Hornero JE, Santos-Dueñas IM, García-García I. Modelling Acetification with Artificial Neural Networks and Comparison with Alternative Procedures. (2020). LAPSE:2020.1016
Author Affiliations
Jiménez-Hornero JE: Department of Electrical Engineering and Automatic Control, University of Cordoba, Campus Rabanales, 14071 Cordoba, Spain [ORCID]
Santos-Dueñas IM: Department of Chemical Engineering, University of Cordoba, Campus Rabanales, 14071 Cordoba, Spain
García-García I: Department of Chemical Engineering, University of Cordoba, Campus Rabanales, 14071 Cordoba, Spain
Journal Name
Processes
Volume
8
Issue
7
Article Number
E749
Year
2020
Publication Date
2020-06-27
Published Version
ISSN
2227-9717
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Original Submission
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PII: pr8070749, Publication Type: Journal Article
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LAPSE:2020.1016
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doi:10.3390/pr8070749
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Oct 6, 2020
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Calvin Tsay
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