Systematic multivariate optimization of biodiesel synthesis from high acid value waste cooking oil : A response surface methodology approach

Aboelazayem, O. and Gadalla, M. and Saha, B (2018) Systematic multivariate optimization of biodiesel synthesis from high acid value waste cooking oil : A response surface methodology approach. In: Systematic multivariate optimization of biodiesel synthesis from high acid value waste cooking oil: A response surface methodology approach :. UNSPECIFIED.

Abstract

Biodiesel has received increasing attention as a green renewable alternative fuel for petroleum diesel. It is synthesised from renewable resources including vegetable oils, animal fats and microalgal cells. Recently, biodiesel production using supercritical technology has been considered as a viable production technique for different feedstocks with potential industrial application. Supercritical production of biodiesel has many advantages over conventional catalysed methods e.g. it neither requires catalyst nor washing water, requires shorter reaction time, provides higher biodiesel yield and produces purer glycerol and purer methanol without involving any dehydration processes. However, the high process energy consumption due to harsh operating conditions is the main obstacle for industrial scale-up of the process. In the present study, a multivariate optimisation technique has been employed for optimising the supercritical production of biodiesel from high acid value waste cooking oil (WCO). The feedstock has been selected as it is widely available from various food industries. The following process variables have been analysed for optimisation e.g. methanol to oil (M:O) molar ratio, temperature, pressure and reaction time. Different responses have been considered for the reaction including overall biodiesel yield, free fatty acids (FFAs) conversion and the conversion of different triglycerides. Response surface methodology (RSM) using central composite design (CCD) have been used to design the experiments and to optimise the process. A quadratic mathematical regression model has been developed for each response function in the reaction variables. The influence of reaction variables and their interactions on the reaction responses have been extensively investigated. The significant process variables have been identified using analysis of variance (ANOVA). Highly significant influences of reaction temperature, pressure and time have been observed. In addition, the interactions between different reaction variables have shown significant effect on reaction responses. The optimum conditions have been identified at M:O molar ratio of 25:1, 266oC reaction temperature and 110 bar pressure within 20 min of reaction time. Finally, the quality of the produced biodiesel showed excellent agreement with the European biodiesel standard (EN14214).