Enhancement of lipid accumulation in microalga Desmodesmus sp. VV2: Response Surface Methodology and artificial neural network modeling for biodiesel production

Microalgae are the most attractive renewable energy sources for the production of biofuels because of their luxurious growth and lipid accumulation ability in diverse nutritional conditions. In the present study, Desmodesmus sp. VV2, an indigenous microalga, was evaluated for its biodiesel potential using Response Surface Methodology (RSM) to improve the lipid accumulation with the combination of nutrients stress NaNO3 starvation, CaCl2 depletion, and supplementation of magnesium oxide nanoparticles (MgO). Among different stress conditions, 57.6% lipid content was achieved from RSM optimized media. Owing to this, RSM results were also validated by the Artificial Neural Network (ANN) with 11 training algorithms and it is found that RSM was more significant. Further, the saturated fatty acid (SFA) content was noticeably increased in RSM optimized media (95.8%) while compared with control and previous reports of other Desmodesmus sp. Further, the highest total FAME content 97.21% was also achieved in cells grown in RSM optimized media. Biodiesel quality parameters were further analyzed and found that they are in accordance with international standards. This study is suggesting that the fatty acid profile of Desmodesmus sp. VV2 attained under optimized media conditions would be suitable for biodiesel production for future energy demand.