Optimized Biodiesel Production from Dunaliella Salina, A Unicellular Green Algae through Artificial Neural Network

Year 2025, Volume: 9 Issue: 2, 179 - 188

Hariram V , Godwin John J , Saravanan A , Sangeethkumar E , Vinoth Kumar M , Balachandar M , Ramanathan V , Baskar S

https://doi.org/10.31127/tuje.1525407

Abstract

Renewable energy is a sure bet for energy needs in the future and algae biofuels for instance can go a long way to provide the energy needed. In this work, the process of obtaining the bio-oil from Dunaliella Salina, a hypersaline, unicellular microalga, having greenish-orange color, is described. The microalgae were cultured in f/2 nutrition medium supplemented with carbon dioxide and vitamins and trace metals. 650 mL of bio-oil were obtained, where ultrasonic extraction frequency of 60 Hz was carried out on the sample for a period of 90 minutes to isolate the bio-oil. The bio-oil was then processed to biodiesel through a single stage base-catalyzed transesterification using methanol and sodium hydroxide as the catalyst. This procedure produced pure Dunaliella Salina biodiesel with an extraction efficiency of 93%. The prediction tool ANN was utilized with trainlm algorithm which predicted the yield which was similar to experimental yield with error percentage of 0.09016. Nuclear Magnetic Resonance (NMR) spectral analysis, Gas Chromatography-Mass Spectrometry (GCMS), and Fourier Transform Infrared Spectroscopy (FTIR) are the three analysis performed on the produced biodiesel. This work paves the path for more research and development in the field of algae biofuels by demonstrating the effectiveness of Dunaliella Salina as a sustainable feedstock for biofuel production and offering a thorough explanation of the mechanisms involved in its conversion to biodiesel.

Keywords

Algae, Biodiesel, Characterization, Dunaliella Salina., Artificial Neural Network

Project Number

Nil

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