Modeling the Physiological Parameters of Brewer’s Yeast during Storage with Natural Zeolite-Containing Tuffs Using Artificial Neural Networks
Abstract
:1. Introduction
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- To design the ANNs to predict the output values of the process;
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- To determine the accuracy of the ANNs;
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- Process optimization.
2. Material and Methods
2.1. Experiment
2.2. Parameter Measurement Procedure
2.3. Experimental Data Preparation
2.4. Research Tools
3. Results
3.1. Experimental Results
3.2. Designing the ANN
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- The number of hidden layers;
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- The number of neurons in the hidden layers;
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- The activation function;
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- The loss function;
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- The step;
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- The optimizer;
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- Regularization;
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- The size and number of batches;
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- The number of epochs.
4. Discussion
4.1. Defining the ANN Accuracy
4.2. Process Optimization
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
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Shafrai, A.V.; Permyakova, L.V.; Borodulin, D.M.; Sergeeva, I.Y. Modeling the Physiological Parameters of Brewer’s Yeast during Storage with Natural Zeolite-Containing Tuffs Using Artificial Neural Networks. Information 2022, 13, 529. https://doi.org/10.3390/info13110529
Shafrai AV, Permyakova LV, Borodulin DM, Sergeeva IY. Modeling the Physiological Parameters of Brewer’s Yeast during Storage with Natural Zeolite-Containing Tuffs Using Artificial Neural Networks. Information. 2022; 13(11):529. https://doi.org/10.3390/info13110529
Chicago/Turabian StyleShafrai, Anton V., Larisa V. Permyakova, Dmitriy M. Borodulin, and Irina Y. Sergeeva. 2022. "Modeling the Physiological Parameters of Brewer’s Yeast during Storage with Natural Zeolite-Containing Tuffs Using Artificial Neural Networks" Information 13, no. 11: 529. https://doi.org/10.3390/info13110529
APA StyleShafrai, A. V., Permyakova, L. V., Borodulin, D. M., & Sergeeva, I. Y. (2022). Modeling the Physiological Parameters of Brewer’s Yeast during Storage with Natural Zeolite-Containing Tuffs Using Artificial Neural Networks. Information, 13(11), 529. https://doi.org/10.3390/info13110529