Microalgae have been highlighted as one of the promising feedstocks for renewable fuel production owing to their high growth rate, ability to grow on wastewater, and to convert CO2 into lipids, the main source of biodiesel. However, lipid extraction from microalgal cells is cumbersome due to its thick cell wall which precludes commercial biodiesel production from microalgae, and to improve the lipid extraction efficiency, optimization of the operational parameters is crucial. One of the well-known challenges is to develop an economical technique to obtain a high lipid extraction efficiency. Hence, this study aimed to investigate the optimized conditions of lipid extraction from marine species of Nannochloropsis sp. PTCC 6016 isolated from the Persian Gulf using Soxhlet, Bligh & Dyer, and ultrasonication methods. Initially, Nannochloropsis sp. was cultivated in a 1000L open pond for the purpose of lipid evaluation and process optimization, to be transferred to a 2000 L and subsequently, a 30000L open pond. Effects of various solvent ratios, mixing time and mixing frequency, pre-treatment, and cell age have been evaluated, and the optimal conditions have been determined. Lipid extraction prediction has been investigated by artificial neural network (ANN) and support vector regression (SVR) methods. Experimental results show that the maximum extraction yield of 0.56 gr extracted lipid/gr dry biomass was obtained using Soxhlet method after 90 days of cultivation using chloroform-methanol (1:1) as extraction solvents. However, considering 10 times higher solvent used in Soxhlet method compared to Bligh and Dyer and ultrasonication methods, the ultrasonication method was the most desired option for lipid extraction efficiency of Nannochloropsis sp. in large-scale operations. In this regard, cost-effective biofuel production should be considered as the focus of future studies to achieve sustainability, energy security and addressing the climate change due to high greenhouse gas emissions. [GRAPHICS]
