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• W2894777145 abstract "Abstract Fatty acid methyl esters (FAME) were synthesized from the waste cooking oil (WCO) by direct transesterification with methanol. The WCO was pretreated to increase the efficiency of FAME production with 1% of potassium methoxide as a catalyst. The free fatty acid of raw source was reduced using 2% of activated charcoal as adsorbent. The process variables were optimized using Box-Behnken design of Response Surface Methodology (RSM), and developed Artificial Neural Network (ANN) model to predict the FAME yield. The highest percentage of conversion (95%) was achieved at optimum conditions, for the catalyst concentration of 1%, alcohol to oil ratio of 6:1, temperature of 75 °C and time of 60 s. The yield of biodiesel was estimated by higher R2 values of RSM (0.98) and ANN (0.99), respectively. The produced FAME from pretreated WCO at 75 °C by microwave irradiation was examined by proton nuclear magnetic resonance (1H NMR), carbon nuclear magnetic resonance (13C NMR), Fourier transform infrared (FT-IR) spectroscopy, and gas chromatography-mass spectrometry (GC–MS). The esters exhibited their two characteristically strong absorption bands arising from carbonyl (C O) at 1741 cm−1 of fresh oil, and C O stretching between 1300 and 1000 cm−1 in the FT-IR spectra. A representative spectrum of 13CNMR for the FAME from WCO was confirmed the presence of methyl esters in the biodiesel of ester carbonyl ( COO ) and C O at 174.2 ppm and 51.4 ppm, respectively. The characteristic peak of 1H NMR at 1.00 ppm of methoxy protons was observed as a singlet at 3.67 ppm, which proved occurrence of the methyl ester (CH3COOR). In addition, a significant weight loss at 139 °C was observed through thermogravimetric (TG) analyses of FAME." @default.
• W2894777145 created "2018-10-12" @default.
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• W2894777145 date "2019-02-01" @default.
• W2894777145 modified "2023-09-26" @default.
• W2894777145 title "Microwave mediated production of FAME from waste cooking oil: Modelling and optimization of process parameters by RSM and ANN approach" @default.
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• W2894777145 doi "https://doi.org/10.1016/j.fuel.2018.09.147" @default.
• W2894777145 hasPublicationYear "2019" @default.
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