A comparison of first principle and neural network modelling for a novel depollution process

Carlos Pelayo-Ortiz (1) , Victor Gonzalez-Alvarez (1) , Jean-Philippe Steyer (2) , Andre Bories (3)

1 Universidad de Guadalajara
2 LBE - Laboratoire de Biotechnologie de l'Environnement [Narbonne]
3 PECH ROUGE - Unité expérimentale de Pech-Rouge

The capability of first principles models and neural networks for predicting the main state variables (biomass and substrate concentrations) in a novel depollution bioprocess has been tested. Experimental data recorded from batch sequential cultures of anaerobic bacteria and yeast to transform organic nitrogen and carbonaceous substrates into useful feed material were used to train the net and validate the first principle model. Both modeling approaches were tested for a number of experiments carried out under different conditions (maximum growth rate cultures, high pH conditions and starving nutrient conditions). The results indicate that the performance of a simple well-trained neural network model was equivalent or better than the first principles model but showed some limitations for providing insight into the mechanism governing the bioprocess. Limitations of both modeling approaches are finally discussed.

Mots clés: mathematical models; first principles; artificial neural networks; depollution process

Domaines: Sciences du Vivant [q-bio]; Sciences de l'environnement; Ingénierie des aliments; Génie des procédés

Carlos Pelayo-Ortiz, Victor Gonzalez-Alvarez, Jean-Philippe Steyer, Andre Bories. A comparison of first principle and neural network modelling for a novel depollution process. Latin American Applied Research, 2012, 42 (3), pp.251-255. ⟨hal-02647251⟩