Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Kavalli, Kiran; Hebbar, Gurumoorthy S.; Rout, Amruta Title A name given to the resource Application of Fuzzy-NSGA-II for achieving maximum biodiesel yield from waste cooking oil Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Environmental Science and Pollution Research;Volume;32;Issue;52;pp.29487-29501 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/s11356-025-36079-y" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/s11356-025-36079-y</a> <br /><br /><a href="https://www.scopus.com/pages/publications/85219698070?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/85219698070?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Kavalli K., Mechanical and Automobile Engineering Department, School of Engineering &amp; Technology, CHRIST University, Karnataka, Bengaluru, 560029, India; Hebbar G.S., Mechanical and Automobile Engineering Department, School of Engineering &amp; Technology, CHRIST University, Karnataka, Bengaluru, 560029, India; Rout A., Mechanical and Automobile Engineering Department, School of Engineering &amp; Technology, CHRIST University, Karnataka, Bengaluru, 560029, India Description An account of the resource The increasing demand for renewable energy and efficient waste management has highlighted the need for innovative biodiesel production techniques. This study optimises biodiesel production from waste cooking oil (WCO) using fuzzy modelling and non-dominated sorting genetic algorithm-II (NSGA-II). The optimisation process focuses on key input parameters: methanol quantity, reaction temperature, reaction time, and catalyst concentration, which were normalised and represented using linguistic variables. Fuzzy logic was employed to predict biodiesel yield, expressed in terms of linguistic variables, and defuzzified to yield crisp output values. The developed model achieved a high R2 value of 96.34%, demonstrating a strong correlation between input variables and biodiesel yield. The NSGA-II algorithm was utilised for multi-objective optimisation, determining the optimal conditions for biodiesel production: 150ml of methanol, a reaction temperature of 62C, a reaction time of 63min, and a catalyst concentration of 7.5g. These parameters resulted in a maximum biodiesel yield of 97.36%. The Box-Behnken experimental design validated the models efficiency, achieving a yield of 96.88%. This study emphasises the practical implications of optimised biodiesel production, such as reducing environmental pollution by recycling WCO and minimising reliance on fossil fuels. The optimised process meets ASTM standards and exhibits scalability potential for industrial-level production with minor modifications. The models robustness makes it suitable for integration into intelligent manufacturing systems, ensuring consistent biodiesel quality and yield through automated monitoring and control mechanisms. Despite its success, challenges such as feedstock variability and initial setup costs must be addressed. Future studies should focus on adaptive models and energy-efficient processing technologies to enhance scalability and sustainability. This research demonstrates a significant step towards sustainable biofuel production, combining waste management with renewable energy generation. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025. Subject The topic of the resource Biodiesel; Fuzzy; NSGA-II; Transesterification; Waste cooking Oil Publisher An entity responsible for making the resource available Springer Relation A related resource ISSN: 9441344; CODEN: ESPLE Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online