Modeling the light-dependent growth kinetics of the green microalga Chlorella zofingiensis


Aladağ E.

4th Global Conference on Engineering Research, Balıkesir, Türkiye, 16 - 19 Ekim 2024, ss.199-206

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Basıldığı Şehir: Balıkesir
  • Basıldığı Ülke: Türkiye
  • Sayfa Sayıları: ss.199-206
  • Van Yüzüncü Yıl Üniversitesi Adresli: Evet

Özet

Microalgae are photosynthetic microorganisms used as feedstock in many fields, from biofuels to food sources. In this research, Chlorella zofingiensis, a green microalgae species that is widely cultivated due to its ability to obtain a variety of products and its applicability in various industrial sectors, was used. Microalgae biomass productivity is affected by many environmental factors such as temperature, light, salinity and nutrient composition. Light intensity plays a key role in microalgae cultivation under autotrophic conditions. The relationship between light intensity and growth rate was investigated using Monod and Haldane kinetic models. The specific growth rates of microalgae cultivated at different light intensities of 25, 50, 100, 250 and 500 μmol photon m-2 s-1 were calculated as 0.25, 0.31, 0.35, 0.34 and 0.33 d-1, respectively. When the light intensity was increased from 25 to 100 μmol photon m-2 s-1, the specific growth rate increased by 40%. The kinetic constants of the Monod equation μmax and KI were found as 0.36 d-1 and 10.70 μmol photon m-2 s-1, respectively. The kinetic constants of the Haldane equation μmax, KI and Ki were found to be 0.42 d-1, 17.11 μmol photon m-2 s-1 and 1991.26 μmol photon m-2 s-1, respectively. RMSE and R2 were calculated to determine the goodness of fit of the Monod and Haldane models with experimental data. RMSE values were found to be 0.018 and 0.006 and R2 values were found to be 0.82 and 0.98, respectively. According to both performance criteria, it can be said that the Haldane model explains the light-dependent microalgal growth better. Mathematical models can be used to predict microalgal growth and optimize operational parameters. These useful tools minimize the necessity for labor-intensive and expensive experiments.