Bulletin of the National Research Centre (Jan 2020)

Optimization of cultivation conditions for Microcystis aeruginosa for biodiesel production using response surface methodology

  • Samar A. El-Mekkawi,
  • N. N. El-Ibiari,
  • Ola A. El-Ardy,
  • Nabil M. Abdelmonem,
  • Ahmed H. Elahwany,
  • Magdi F. Abadir,
  • Ibrahim M. Ismail

DOI
https://doi.org/10.1186/s42269-019-0265-9
Journal volume & issue
Vol. 44, no. 1
pp. 1 – 9

Abstract

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Abstract Background Biodiesel is expected to play a key role in the development of a sustainable, economical, and environmentally safe source of energy. The third generation of biodiesel is derived from microalgae and cyanobacteria that have sufficient amount of oil. The optimization of biomass and oil content in biodiesel production based on algal cultivation relies upon several factors. The present experimental work aims at optimizing some of the cultivation conditions to obtain maximum oil and biomass yield and create a prediction model that describe the effect of the initial inoculum concentration, and irradiance on the biomass yield and oil concentration were designed using Design Expert 6.0.8. Results The results revealed that the optimum surface-to-volume ratio for the airlift bubble column photobioreactor was 0.9, and the most applicable model for describing Microcystis aeruginosa growth was the hyperbolic tangent model with a model constant value of 1.294 mg·L− 1·d− 1/μmol·m− 2·s− 1. The optimum cultivation conditions were 81 μmol·m− 2·s− 1 irradiance and 67 mg·L− 1 initial inoculum concentration, and these conditions achieved a biomass yield of 163 mg·L− 1·d− 1 and an oil concentration of 143 mg·L− 1. Conclusions This work focused on the cultivation of microalgae in closed systems. Cyanobacteria as M. aeruginosa has high lipid content, and high lipid productivity makes it suitable as a lipid feed stock for biodiesel production. The response surface method was the most suitable route to study the simultaneous influence of irradiance and initial inoculum concentration through statistical methods as well as to establish a model for predicting the biomass yield and oil concentration of M. aeruginosa.

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