Optimisation of Fuel-grade Hydrocarbons' Production from Spent Coffee Grounds Using Green Processes
Mkhonto, Bhekuyise
Chetty, Manimagalay

How to Cite

Mkhonto B., Chetty M., 2022, Optimisation of Fuel-grade Hydrocarbons’ Production from Spent Coffee Grounds Using Green Processes, Chemical Engineering Transactions, 96, 157-162.


The present study focused on the optimisation of production of fuel-grade hydrocarbons using lipids or oil extracted from spent coffee grounds (SCGs). The oil was extracted from SCGs using 2-methyltetrahydrofuran as a green solvent and calcium oxide synthesised from chicken eggshells was used as a green catalyst during the SCGs oil transesterification. Calcium oxide was doped with lithium (Li-CaO(s)) to improve catalyst activity. The oil extraction and the transesterification processes were optimised using the design of experiment (DoE). The response surface methodology (RSM) and the Box-Behnken design were used to optimise both processes. The combination of extraction time and solvent-to-solids ratio were optimised for the oil extraction process. The combination of reaction time and catalyst loading were optimised for the transesterification process. Fourteen 14 experiments were predicted by Box-Behnken design for both the oil extraction and transesterification processes. The model predicted that the optimum extraction conditions were, 1:18 (w/v) SCGs-to-solvent ratio and 4.5 h extraction period, providing a maximum of 25.10 wt% oil yield. The model predicted optimal yield was confirmed experimentally, obtaining an oil yield of 24.60 wt%, which was 0.5 wt% lower than the value predicted by the model. During transesterification the reactor temperature was maintained at 65 °C, a SCGs oil-to-methanol molar ratio of 1:12 and 165 rpm mixing speed. The model predicted the optimum reaction time of 2 h, 5 wt% of the oil used catalyst loading, providing 98.21 wt % oil conversion. The predicted optimal yield was confirmed experimentally, achieving an oil conversion of 97.81 wt%, which was 0.4 wt% lower than the predicted by the model.