Growth model for raceway pond cultivation of Desmodesmus sp. MCC34 isolated from a local water body

Abstract

Biofuel production by microalgae has the advantage of higher biomass productivity over land crops. The selection of potential microalgae depends on the growth in outdoor mass cultivation during different seasons, which can be predicted by a mathematical model. Here, freshwater green algae were isolated from a local water body in Pilani, Rajasthan, India (geographical coordinates: 28°22′N 75°36′E) and characterized by microscopy and ribosomal RNA analysis. The strain was submitted to the Indian Agricultural Research Institute's microbial culture collection (IARI, India) and identified as Desmodesmus sp. MCC34. This strain, along with a fresh water green algae (Chlorella minutissima), two marine green algae species (Dunaliella salina and Dunaliella tertiolecta) and two nitrogen fixing cyanobacteria (Nostoc muscorum and Anabaena doliolum), were screened for lipid productivity and growth kinetics under culture room and raceway pond conditions. Desmodesmus sp. MCC34 showed the highest specific growth rate (0.26 day−1), biomass production (1.9 g L−1) and lipid productivity (103 mg L−1 day−1). The optimal temperature and saturating light intensity for maximal growth of Desmodesmus sp. MCC34 were 35 °C and 75 μmol m−2 s−1 with molar extinction coefficient of 0.22 m2 g−1, respectively. Desmodesmus sp. MCC34 was then subjected to outdoor cultivation in a 20‐m long raceway pond for 18 days during March and November 2013. The areal biomass productivity and volumetric biomass productivity were 13946.23 kg ha−1 year−1 and 56.94 mg L− 1day−1 during the month of March, decreasing to 6262.28 kg ha−1 year−1 and 25.57 mg L− 1day−1 during the month of November. A mathematical model was constructed to explain the relationship between biomass production and growth parameters such as temperature, light intensity and nutrient concentration. The productivity values predicted with the proposed model correspond well with the experimental data, suggesting the validity of the model.