Optimization and Mathematical Modeling of Biodiesel Production using Homogenous Catalyst from Waste Cooking Oil
Teshome Tayye Anbessa1, Karthikeyan S2

1Karthikeyan S*, Associate Professor, Department of Biotechnology, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur, Andhra Pradesh, India.
2Teshome Tayye Anbessa, PG Student, Department of Biotechnology, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur, Andhra Pradesh, India.
Manuscript received on September 22, 2019. | Revised Manuscript received on October 20, 2019. | Manuscript published on October 30, 2019. | PP: 1733-1739 | Volume-9 Issue-1, October 2019 | Retrieval Number: F9005088619/2019©BEIESP | DOI: 10.35940/ijeat.F9005.109119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: In the present investigation, the transesterification of waste cooking oil (WCO) to biodiesel over homogenous catalyst KOH have been carried out. To optimize the transesterification process variables both response surface method (RSM) and artificial neural network (ANN) mathematical models were applied to study the impact of process variables temperature, catalyst loading, methanol to oil ratio and the reaction time on biodiesel yield. The experiments were planned with a central composite design matrix using 24 factorial designs. A performance validation assessment was conducted between RSM and ANN. ANN models showed a high precision prediction competence in terms of coefficient of determination (R2 = 0.9995), Root Mean Square Error (RMSE = 0.5702), Standard Predicted Deviation (SEP = 0.0133), Absolute Average Deviation (AAD = 0.0115) compared to RSM model. The concentration of catalyst load was identified as the most significant factor for the base catalyzed transesterification. Under optimum conditions, the maximum biodiesel yield of 88.3% was determined by the artificial neural network model at 60 ºC, 1.05 g catalyst load, 7:1 methanol to oil ratio and 90 min transesterification reaction time. The biodiesel was analyzed by GCMS and it showed the presence of hexadecanoic acid, 9-octadecenoic acid, 9, 12, 15-octadecatrienoic acid, eicosenoic acid, methyl 18-methyl-nonadecanoate, docosanoic acid, and tetracosanoic acid as key fatty acid methyl esters.
Keywords: Transesterification; Catalyst; Optimization; Mathematical model; Biodiesel.