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Study of growth kinetics of Spirulina platensis with light limitation in the photobioreactor
Lu, X. (1996). Study of growth kinetics of Spirulina platensis with light limitation in the photobioreactor. MSc Thesis. VUB: Brussel. 71 pp.

Available in Author 
  • VLIZ: Archive VLIZ ARCHIVE A.THES6 [17382]
  • VLIZ: Non-open access 228056
Document type: Dissertation

Keyword
    Marine

Author  Top 
  • Lu, X.

Abstract
    Cyanobacterium, Spirulina platensis, is a highly productive photosynthetic micro- organism, it can directly use light as an energy source for its photosynthesis. In our case, S. platensis is cultivated and studied in a photobioreactor, as a photosynthetic compactment of closed actificial ecosystem, in which it is an edible biomass with high protein content coming from waste, CO2 and mineral. The effects of the main limitation by light is considered. As physical limitation, light transfer is characterized by mean incident radiant flux which has been determined by three different methods in the cylindrical photobioreactor . Among these methods, Reinecke's salt actinametry, considered as a new and sensitive method, is firstly used. In the cylindrical photobioreactor, the average incident radiant energy flux ranges from 0 to 300W/m². A two-parameter model based on the simplified, monodimensional equation of SCHUSTER for radiative transfer with parameters obtained from rectangular photobioreactor is used to simulation of profiles of radiant energy in the cylindrical photobioreactor. The model results agree closely with experimental data so that the parameters obtained from rectangular photobioreactor extent to apply for the cylindrical photoreactor. The coupling between light transfer and growth kinetics models are applied to simulate volumetric biomass growth rates of cyanobacterium S. platensis in cylindrical photobioreactor. These calculations are then compared with experimental results obtained from batch culture in a range of incident radiant fluxes from 48 to 300W/m². The comparison indicate that the model has a good predictability and robustness.

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