ABSTRACT
In this work, waste chicken eggshell (WCES) was used as a heterogeneous catalyst for the production of biodiesel from waste cooking oil (WCO). The catalyst was prepared via calcination technique. Response Surface Method (RSM) optimisation and Artificial Neural Network (ANN) modelling were performed to achieve maximum biodiesel yield. Both models perform reasonably well in achieving maximum biodiesel yield (91%). However, the efficacy of the models was determined with R2, R (coefficient of determination), and MSE (mean square error). Results show that the ANN model achieved the highest R2 (98.48), R (99.24), and lowest MSE (0.08) compared to the RSM model. This shows that ANN predictive capability was more accurate. The fatty acid composition (FAC) analysis by GCMS reveals that 56.75% unsaturated and 41.99% saturated were recognised. The key physicochemical properties of biodiesel satisfy the standards of ASTMD6751 and EN 14,214.
Nomenclature
ANN:Artificial Neural Network
CaO:Calcium oxide
CC:Catalyst Concentration
FFA:Free Fatty Acids
MMT:Million Metric Tons
MR:Molar Ratio
MSE:Mean Square Error
RSM:Response Surface Method
RTe:Reaction Temperature
RTi:Reaction Time
WCES:Waste Chicken Eggshell
WCO:Waste Cooking Oil
Disclosure statement
No potential conflict of interest was reported by the authors.
Additional information
Notes on contributors
Aditya Kolakoti
Aditya Kolakoti received his Ph.D. degree in Marine Engineering (Internal Combustion Engines) in 2017 and Masters degree in Marine Engineering and Mechanical Handling in 2013 from the Andhara University, Visakhapatnam, India. Currently, he is Assistant Professor at the Department of Mechanical Engineering, Raghu Engineering College, Visakhapatnam, India.
G Satish
G Satish currently working as Associate Professor in the Department of Mechanical Engineering, Pragati Engineering College, Kakinada, India.