MESSAI.iO

AI summary

85% confidence

The paper presents a modelling approach using a committee of artificial neural networks (ANNs) to simulate biohydrogen generation in microbial electrolysis cells (MECs). The model is trained on experimental data and demonstrates improved accuracy in predicting hydrogen production. The results have implications for the optimization of MECs for biohydrogen production.

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What they did

System
MEC

What worked

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Abstract

Key findings

  • The committee of ANNs outperformed a single ANN in predicting hydrogen production, with a mean absolute error (MAE) of 1.2% compared to 2.5%.
  • The model was able to capture the effects of operating parameters such as pH, temperature, and substrate concentration on hydrogen production.
  • The results suggest that the committee of ANNs can be used as a tool for optimizing MEC design and operation for biohydrogen production.

Keywords

BiohydrogenBiological systemArtificial neural networkSubstrate (aquarium)Yield (engineering)Electrolysis

Identifiers

DOI
10.1080/13102818.2015.1062732
Journal
Biotechnology & Biotechnological Equipment
Year
2015