AbstractAs a subsystem of the bioregenerative life support system (BLSS), light-algae bioreactor (LABR) has properties of high reaction rate, efficiently synthesizing microalgal biomass, absorbing CO 2 and releasing O 2 , so it is significant for BLSS to provide food and maintain gas balance. In order to manipulate the LABR properly, it has been designed as a closed-loop control system, and technology of Artificial Neural Network–Model Predictive Control (ANN-MPC) is applied to design the controller for LABR in which green microalgae, Spirulina platensis is cultivated continuously. The conclusion is drawn by computer simulation that ANN-MPC controller can intelligently learn the complicated dynamic performances of LABR, and automatically, robustly and self-adaptively regulate the light intensity illuminating on the LABR, hence make the growth of microalgae in the LABR be changed in line with the references, meanwhile provide appropriate damping to improve markedly the transient response performance of LABR.


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    Titel :

    The design and optimization for light-algae bioreactor controller based on Artificial Neural Network-Model Predictive Control


    Beteiligte:
    Hu, Dawei (Autor:in) / Liu, Hong (Autor:in) / Yang, Chenliang (Autor:in) / Hu, Enzhu (Autor:in)

    Erschienen in:

    Acta Astronautica ; 63 , 7-10 ; 1067-1075


    Erscheinungsdatum :

    2008-02-20


    Format / Umfang :

    9 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch