Modelling and process optimization for biodiesel production from Nannochloropsis salina using artificial neural...

Area of Science:

• Green Chemistry
• Biotechnology
• Materials Science

Background:

• Biodiesel production is crucial for renewable energy.
• Developing efficient and sustainable nanocatalysts is essential.
• Eggshells and Nannochloropsis salina offer a promising source for catalyst synthesis.

Purpose of the Study:

• To synthesize and characterize a calcium oxide (CaO) nanocatalyst from eggshell and Nannochloropsis salina.
• To optimize process variables for biodiesel production using the synthesized nanocatalyst.
• To evaluate the efficiency of the nanocatalyst in converting oil to fatty acid methyl esters (FAME).

Main Methods:

• Nanocatalyst synthesis from eggshell and Nannochloropsis salina.
• Characterization using Scanning Electron Microscopy (SEM) and Fourier-Transform Infrared Spectroscopy (FTIR).
• Process optimization via Response Surface Methodology (RSM) and Artificial Neural Network (ANN).
• Biodiesel analysis using Gas Chromatography-Mass Spectrometry (GC-MS).

Main Results:

• SEM and FTIR confirmed the nanocatalyst's morphology and functional groups.
• RSM and ANN models showed high fitness (R² values of 0.8751 and 0.957, respectively).
• Maximum FAME conversion of 86.1% was achieved at 3% (w/v) nanocatalyst, 1:6 oil to methanol ratio, 60°C, and 55 min.

Conclusions:

• The synthesized CaO nanocatalyst is effective for biodiesel production.
• Optimized conditions significantly enhance fatty acid methyl ester conversion.
• This approach offers a sustainable route for biodiesel synthesis using waste materials.