Performance and emission analysis of CI engine fueled with Dunaliella salina biodiesel and TiO₂ nanoparticle additives: Experimental and ANN-based Predictive Approach - V Hariram; S Balamurugan; R Mohan; R Karthick; Nandagopal Kaliappan; K Barathiraja; J Godwin John; K Kamakshi Priya (Results in Engineering)
Growing demand for sustainable and renewable energy sources has intensified research into alternative fuels. Dunaliella salina, a green microalga, presents a promising feedstock for biodiesel production owing to its high oil yield and adaptability to extreme saline environments. This study aims to optimize the biodiesel production process and evaluate its feasibility in compression ignition (CI) engine applications. The algal oil was extracted from Dunaliella salina using the Soxhlet extraction technique and subsequently converted into biodiesel through a single-stage transesterification process using NaOH as a catalyst and methanol as the esterifying agent. The optimal reaction conditions - molar ratio of 1:8, catalyst concentration of 0.75 % by weight, and reaction temperature of 85 °C were identified. Gas Chromatography-Mass Spectrometry and Fourier Transform Infrared Spectroscopy analyses confirmed the presence of Linolenic acid and Arachidic acid in significant proportions. To enhance combustion characteristics and reduce emissions, biodiesel-diesel blends were evaluated in the presence of oxygenated titanium dioxide (TiO₂) nanoparticles at various compression ratios. An Artificial Neural Network (ANN) model was developed using the Levenberg-Marquardt algorithm, incorporating 27 datasets generated through a Response Surface Methodology (RSM)-based d-optimal design to predict engine performance and emission characteristics. Experimental results identified the D80DuBD20TiO₂ blend at a compression ratio of 18 (CR18) as optimal, demonstrating improved combustion efficiency and reduced emissions. The developed ANN model exhibited high predictive accuracy, with correlation coefficients (R) of 0.998, 0.993, and 0.996 for training, validation, and testing datasets, respectively. The techno-economic analysis assessed the Return on Investment and Operating cost of this investigation.