Parameter identification in a simple chemostat model using neural networks

Moulai-Khatir, Anes (2025) Parameter identification in a simple chemostat model using neural networks. Annales Mathematicae et Informaticae, 62. pp. 99-115. ISSN 1787-5021 (Print) 1787-6117 (Online)

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Abstract

This paper investigates the use of a neural network approach for parameter estimation in the chemostat model, relevant to applications like wastewater treatment and bioreactor design. Accurate parameter charac terization serves as the foundation for understanding system dynamics and making reliable predictions. Traditional optimization-based methods face challenges such as noise and high-dimensional data. Neural networks offer a promising alternative due to their ability to handle complex datasets. The work applies a simple neural network model, demonstrating its effectiveness for estimating chemostat parameters. While advanced techniques like neural architecture search (NAS) are not included, the approach provides a practical solution for parameter identification in dynamic models.

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