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Biodiesel production from discarded fish waste lipids using CaO-based heterogeneous catalyst: kinetics modeling, process optimization and validation using Artificial neural network

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dc.contributor.author Muzafar Zulkifli
dc.contributor.author (UNIKL MICET)
dc.date.accessioned 2026-04-21T01:55:06Z
dc.date.available 2026-04-21T01:55:06Z
dc.date.issued 2026-04-21
dc.identifier.uri http://hdl.handle.net/ir.unikl.edu.my/34286
dc.description This article is index by Scopus en_US
dc.description.abstract The present study explores the effective conversion of lipids extracted from discarded fish waste into biodiesel using a CaO(Ca3Al2O6) as a heterogeneous catalyst. The properties and the catalyst structure were explored using XRD, FT-IR, SEM, and EDX analyses. The lipid extraction from discarded fish waste was performed through Soxhlet extraction utilizing methanol as a solvent. The catalytic transesterification of biodiesel from discarded fish waste lipids was conducted employing CaO(Ca3Al2O6) as a heterogeneous catalyst by varying lipids-to-methanol ratio (1:6-1:16), catalyst doses (1-6 wt%), reaction time (1-6 h), and temperature (40-70 °C). The experimental conditions were optimized using response surface methodology (RSM) and validated using an artificial neural network (ANN). The highest biodiesel yield obtained was about 93% at optimal experimental conditions of lipid to methanol ratio of 1:8.79, reaction time of 4.11 h, reaction temperature of 65.49 °C, and catalyst loading of 4 wt%. Kinetic and thermodynamic studies discovered that the transesterification reaction is non-spontaneous and endothermic, and it requires a low activation energy (12.787 kJ/mol). Physicochemical properties of the synthesized biodiesel complied with the biodiesel standard specifications of EN 14214 and ASTM D6751, highlighting its suitability as a renewable alternative to conventional diesel fuels. en_US
dc.title Biodiesel production from discarded fish waste lipids using CaO-based heterogeneous catalyst: kinetics modeling, process optimization and validation using Artificial neural network en_US
dc.type Article en_US


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