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Modeling the growth of microalgae Spirulina sp. with application of illuminance and magnetic field
Author(s) -
Piazzi Ana Carolina Ferreira,
Veiga Mayara Copello,
Santos Lucielen Oliveira,
Costa Jorge Alberto Vieira,
Kuhn Raquel Cristine,
Salau Nina Paula Gonçalves
Publication year - 2019
Publication title -
journal of chemical technology and biotechnology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.64
H-Index - 117
eISSN - 1097-4660
pISSN - 0268-2575
DOI - 10.1002/jctb.5942
Subject(s) - illuminance , magnetic field , particle swarm optimization , biological system , field (mathematics) , mathematics , computer science , mathematical optimization , optics , physics , biology , quantum mechanics , pure mathematics
BACKGROUND There is a lack of studies that investigate the application of magnetic field in microalgae cultures in the literature. Aiming to understand the effects of the application of magnetic field, along with illuminance, on the growth of microalgae, an accurate first‐principles mathematical model must be designed and evaluated. Furthermore, the parameters associated with illuminance and magnetic field in the new model must be obtained through a reliable and robust parameter estimation procedure, since for magnetic field effects there are no models and parameters reported in the literature. RESULTS A new model for microalga growth with application of illuminance and magnetic field was designed to predict biomass productivity. Fourteen different Verhulst‐based mathematical equations were proposed and compared. The results showed the significant influence of the illuminance and magnetic field on the microalga growth and indicated that the designed model accurately fitted the experimental data. CONCLUSIONS A new model to predict microalga growth with application of illuminance and magnetic field was designed considering two new parameters, K I and K M , affinity of a microorganism to illuminance and magnetic field, respectively, in a Verhulst‐based equation. The parameters were accurately estimated though a hybrid combination of particle swarm optimization and nonlinear least‐squares algorithms, allowing the model to accurately predict microalga growth. © 2019 Society of Chemical Industry