Advanced multi-criteria optimization for sustainable biofuels: AHP-guided k-means clustering approach

Biodiesel, though considered one of the cleanest renewable fuels, faces limited global adoption due to low yield efficiency and inconsistent availability across regions. These challenges necessitate advanced production techniques and alternative feedstocks to make biodiesel a more viable and scalable energy solution. The study’s objective is to establish ideal operating conditions for converting Eichhornia crassipes (E. crassipes) oil into biodiesel using an ultrasonic reactor enhanced with Sulphonated Graphene (SGR) catalyst. E. crassipes is chosen as a biodiesel feedstock due to its high lipid content, rapid growth, and invasive nature, offering a low-cost, sustainable biomass ideal for efficient transesterification. The research employs Analytic Hierarchy Process (AHP) and k-means clustering since it can handle the nonlinear influence of parameters, ensuring accurate identification of optimal conditions for maximum biodiesel yield. The novel application of SGR and hydrogen in ultrasonic reactor for biodiesel synthesis, significantly enhances transesterification efficiency, increasing biodiesel yield from 58 % to 94 %. The catalytic action led to substantial reduction in cellulose, hemicellulose, and lignin content (94.1 %, 91.4 %, and 95.5 %, respectively), improving the conversion of E. crassipes biomass into biodiesel. The priority measured by AHP for operating variables is: Molar Ratio – 32 %, Temperature – 25 %, Time – 21 %, Frequency – 14 %, and Hydrogen – 8 %. The ideal operating conditions after optimization were molar ratio 10:1, temperature 50 °C, time 12 min, frequency 36 kHz, and hydrogen supply 3 %, resulting in a biodiesel yield of 94.1 % for trial 17. The study offers a high-yield, efficient pathway for utilizing an invasive aquatic plant and advanced catalyst as a sustainable biodiesel source.