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Open AccessFeature PaperArticle

Modelling Acetification with Artificial Neural Networks and Comparison with Alternative Procedures

1
Department of Electrical Engineering and Automatic Control, University of Cordoba, Campus Rabanales, 14071 Cordoba, Spain
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Department of Chemical Engineering, University of Cordoba, Campus Rabanales, 14071 Cordoba, Spain
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Author to whom correspondence should be addressed.
Processes 2020, 8(7), 749; https://doi.org/10.3390/pr8070749
Received: 28 May 2020 / Revised: 21 June 2020 / Accepted: 24 June 2020 / Published: 27 June 2020
(This article belongs to the Section Biological Systems)
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. View Full-Text
Keywords: bioreactor systems; acetification; vinegar; modelling; artificial neural networks; multilayer perceptron bioreactor systems; acetification; vinegar; modelling; artificial neural networks; multilayer perceptron
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Jiménez-Hornero, J.E.; Santos-Dueñas, I.M.; García-García, I. Modelling Acetification with Artificial Neural Networks and Comparison with Alternative Procedures. Processes 2020, 8, 749.

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