Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
7080990 | Bioresource Technology | 2013 | 7 Pages |
Abstract
This study examines the use of artificial neural networks as predictive tools for the growth of the dinoflagellate microalga Protoceratium reticulatum. Feed-forward back-propagation neural networks (FBN), using Levenberg-Marquardt back-propagation or Bayesian regularization as training functions, offered the best results in terms of representing the nonlinear interactions among all nutrients in a culture medium containing 26 different components. A FBN configuration of 26-14-1 layers was selected. The FBN model was trained using more than 500 culture experiments on a shake flask scale. Garson's algorithm provided a valuable means of evaluating the relative importance of nutrients in terms of microalgal growth. Microelements and vitamins had a significant importance (approximately 70%) in relation to macronutrients (nearly 25%), despite their concentrations in the culture medium being various orders of magnitude smaller. The approach presented here may be useful for modelling multi-nutrient interactions in photobioreactors.
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Authors
L. López-Rosales, J.J. Gallardo-RodrÃguez, A. Sánchez-Mirón, A. Contreras-Gómez, F. GarcÃa-Camacho, E. Molina-Grima,