Experimentation, Modelling, and Analysis of Biodiesel Conversion and Their Engine Performance and Emission Characteristics

The use of alternative fuels in internal combustion engines has significantly increased due to their promising characteristics. Many researchers use computational analysis, a reliable technique that provides accurate results comparable to experimental findings, to analyse engine performance, combustion, and emission characteristics. The cost of converting feedstock into biodiesel ranges from 70% to 95%. Garcinia Gummi-Gutta (GGG) is a commercially valuable spice tree that helps reduce biodiesel production costs, generating Rs. 82,666 annually for farmers. The oil yield from GGG seeds was 18% for boiling, 23% for mechanical expeller, and 42% for solvent extraction methods, with physical properties meeting ASTM biodiesel standards. The crude oil from GGG seeds had a free fatty acid content of 33.56%, necessitating esterification followed by transesterification. GC-MS and FTIR analyses confirmed biodiesel conversion and the design of experiments (DOE) explored process kinetics, resulting in 96.2% biodiesel yield under optimized transesterification conditions using teacher-learner-based optimization (TLBO). The addition of silver oxide nanoparticles increased biodiesel yield to 97.8%, maintaining 90.1% after six reuse cycles. The estimated cost of biodiesel conversion is $0.8393. Three biofuel type blends (B10, B20, and B30) were prepared for use, with computed overall uncertainty in experiments of ±3.15%. The experiments were conducted with four varying variables: engine load (EL), BT, injection pressure (IP), and compression ratio (CR). The researchers evaluated the performance (brake thermal efficiency: BTE, and brake-specific fuel consumption: BSFC) as well as the emissions (carbon monoxide: CO, nitrogen oxide: NOx, unburnt hydrocarbon: UHC) characteristics. The models developed for emission and performance characteristics showed a high coefficient of determination, indicating their statistical adequacy for prediction and optimization.