LAPSE:2021.0768
Published Article
LAPSE:2021.0768
Using Neural Networks to Obtain Indirect Information about the State Variables in an Alcoholic Fermentation Process
October 14, 2021
This work provides a manual design space exploration regarding the structure, type, and inputs of a multilayer neural network (NN) to obtain indirect information about the state variables in the alcoholic fermentation process. The main benefit of our application is to help experts reduce the time needed for making the relevant measurements and to increase the lifecycles of sensors in bioreactors. The novelty of this research is the flexibility of the developed application, the use of a great number of variables, and the comparative presentation of the results obtained with different NNs (feedback vs. feed-forward) and different learning algorithms (Back-Propagation vs. Levenberg−Marquardt). The simulation results show that the feedback neural network outperformed the feed-forward neural network. The NN configuration is relatively flexible (with hidden layers and a number of nodes on each of them), but the number of input and output nodes depends on the fermentation process parameters. After laborious simulations, we determined that using pH and CO2 as inputs reduces the prediction errors of the NN. Thus, besides the most commonly used process parameters like fermentation temperature, time, the initial concentration of the substrate, the substrate concentration, and the biomass concentration, by adding pH and CO2, we obtained the optimum number of input nodes for the network. The optimal configuration in our case was obtained after 1500 iterations using a NN with one hidden layer and 12 neurons on it, seven neurons on the input layer, and one neuron as the output. If properly trained and validated, this model can be used in future research to accurately predict steady-state and dynamic alcoholic fermentation process behaviour and thereby improve process control performance.
Keywords
fermentation process, neural network, prediction application
Suggested Citation
Sipos A, Florea A, Arsin M, Fiore U. Using Neural Networks to Obtain Indirect Information about the State Variables in an Alcoholic Fermentation Process. (2021). LAPSE:2021.0768
Author Affiliations
Sipos A: Lucian Blaga University of Sibiu, Faculty of Agricultural Sciences, Food Industry and Environmental Protection, 7−9 Dr. Ion Ratiu Street, 550012 Sibiu, Romania [ORCID]
Florea A: Department of Computer Science, Lucian Blaga University of Sibiu, Faculty of Engineering, 4 Emil Cioran Street, 550025 Sibiu, Romania [ORCID]
Arsin M: Department of Computer Science, Lucian Blaga University of Sibiu, Faculty of Engineering, 4 Emil Cioran Street, 550025 Sibiu, Romania
Fiore U: Department of Management and Quantitative Studies, Parthenope University of Napoli, 80132 Napoli, Italy [ORCID]
Journal Name
Processes
Volume
9
Issue
1
First Page
pr9010074
Year
2020
Publication Date
2020-12-31
Published Version
ISSN
2227-9717
Version Comments
Original Submission
Other Meta
PII: pr9010074, Publication Type: Journal Article
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LAPSE:2021.0768
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doi:10.3390/pr9010074
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Oct 14, 2021
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CC BY 4.0
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[v1] (Original Submission)
Oct 14, 2021
 
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Oct 14, 2021
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http://psecommunity.org/LAPSE:2021.0768
 
Original Submitter
Calvin Tsay
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