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On-line prediction of fermentation variables using neural networks
J Thibault1, V Van Breusegem, A Chéruy
1Department of Chemical Engineering, Laval University, Sainte-Foy, Quebec, Canada, G1K 7P4.
Biotechnology and Bioengineering
|December 5, 1990
Summary
Neural network models accurately predict bioprocess fermentation variables, even with noisy data. This advanced method learns complex relationships without needing prior model structure knowledge.
Area of Science:
- Bioprocess Engineering
- Computational Biology
- Artificial Intelligence
Background:
- Dynamic modeling is crucial for optimizing bioprocesses.
- Traditional prediction methods often struggle with complex, non-linear relationships.
- Accurate prediction of fermentation variables is essential for process control and efficiency.
Purpose of the Study:
- To introduce neural network computational algorithms for dynamic bioprocess modeling.
- To evaluate the predictive performance of dynamic neural models in bioprocessing.
- To compare neural network models against traditional prediction techniques.
Main Methods:
- Development and application of dynamic neural network models.
- Utilizing simulation results from a continuous stirred tank fermentor.
- Comparative analysis against a traditional prediction method.
Main Results:
- Neural network models demonstrated high accuracy in predicting key fermentation variables.
- The dynamic neural models exhibited robustness and noise immunity.
- Neural networks effectively captured complex process relationships without explicit model structure definition.
Conclusions:
- Neural network computational algorithms offer a powerful and flexible approach for dynamic bioprocess modeling.
- These models provide superior capabilities in learning intricate biological system dynamics compared to traditional methods.
- The findings support the adoption of neural networks for enhanced bioprocess prediction and control.
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