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. In addition, the saturated fatty acid (SFA) content was noticeably increased in RSM optimized media (95.8%) while compared with control. Further, the highest total FAME content 97.21% was also achieved in cells grown in RSM optimized media. Biodiesel quality parameters were analyzed and found that they are in accor- dance with international standards. Thus, this study 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.

Automated summary

This study evaluated the biodiesel potential of indigenous microalgae Desmodesmus sp. VV2 and optimized the culture medium using Response Surface Methodology (RSM) to enhance lipid accumulation. The results showed that under optimized conditions, the microalgae exhibited significantly increased lipid and saturated fatty acid (SFA) content, with the highest total FAME content achieved. The quality of the biodiesel produced from the optimized culture medium also met international standards.