Title: Biodiesel production from third-generation feedstock: process parameter modelling and optimisation using RSM-ANN approach

Authors: Aqueel Ahmad; Ashok Kumar Yadav; Shifa Hasan

Addresses: Department of Mechanical Engineering, Graphic Era (Deemed to be University), Dehradun, 248002, India ' Department of Mechanical Engineering, Raj Kumar Goel Institute of Technology, Ghaziabad, Uttar Pradesh 201003, India ' Department of Management Studies, Netaji Subhas University of Technology, Dwarka Sector 3, New Delhi 110078, India

Abstract: This study employed response surface methodology (RSM) coupled with a central composite design (CCD) approach to ascertain the optimal conditions for biodiesel production from Neochloris oleoabundans microalgae oil. Four key process variables, including the methanol-to-oil molar ratio, catalyst concentration, reaction time, and temperature, were investigated across five levels to develop an L₃₀ orthogonal array for experimentation. An artificial neural network (ANN)-based prediction model was developed using the experimentally obtained data, yielding high accuracy with mean square error (MSE) values of 0.019, 2.4327, and 0.8269 and coefficient of determination (R²) values of 0.9996, 0.9796, and 0.9890 for training, validation, and testing sets, respectively, indicating robust predictive capability. The optimisation analysis reveals a biodiesel yield of 94.94% under optimised conditions: 6.92:1 molar ratio, 1.22% catalyst concentration, 64.36 min reaction time, and 56.46°C temperature. Experimental validation confirmed the reliability of the optimisation results, demonstrating a marginal error of 2%. [Received: November 25, 2022; Accepted: April 30, 2024]

Keywords: biodiesel production; Neochloris oleoabundans; response surface methodology; RSM; artificial neural network; ANN; sustainable fuel; central composite design; CCD; mean square error; MSE.

DOI: 10.1504/IJOGCT.2025.144530

International Journal of Oil, Gas and Coal Technology, 2025 Vol.37 No.2, pp.235 - 254

Accepted: 30 Apr 2024
Published online: 18 Feb 2025 *

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