Statističko modelovanje i optimizacija proizvodnje biodizela u prisustvu ultrazvuka primenom različitih eksperimentalnih planova

Jelena Avramović,Dragana Radosavljević,Olivera Stamenković,Ana Veličković,Ivan Stojković,Vlada Veljković

doi:10.5937/zasmat1901070a

Abstract

The present study compares the performances of the regression models developed by the response surface methodology combined with the full factorial, Box-Behnken or face central composite designs applied for the ultrasound-assisted KOH-catalyzed methanolysis of sunflower oil. While all models led to similar optimal reaction conditions, the models based on the simpler designs had the smaller corrected Akaike information criterion values, the insignificant lack of fit and the more favorable statistical criteria than the model based on the full factorial design. Including fewer experiments, the Box-Behnken design can be recommended for the optimization of ultrasound-assisted biodiesel production processes.

Highlights

In recent years, the design of experiment and the response surface methodology (RSM) are commonly employed in statistical modeling and optimization of many processes in different scientific fields including biodiesel production
full factorial design (FFD) requires the largest number of experimental runs, resulting in a more reliable regression model in the selected experimental cubic space but larger costs, more work and longer time for conducting
Being a part of an FFD experimental cubic space, Box–Behnken design (BBD) and face central composite design (CCD) (FCCD) involve a smaller number of experiments but their experimental points are suited at different places (Fig. 1)

Summary

Introduction

The design of experiment and the response surface methodology (RSM) are commonly employed in statistical modeling and optimization of many processes in different scientific fields including biodiesel production. This combination results in an empirical model describing esters yield or content as a function the influential process factors on the basis of a minimum number of well–planned experimental runs. FFD requires the largest number of experimental runs, resulting in a more reliable regression model in the selected experimental cubic space but larger costs, more work and longer time for conducting. Being a part of an FFD experimental cubic space, BBD and face CCD (FCCD) involve a smaller number of experiments but their experimental points are suited at different places (Fig. 1)

Methods

Results

Conclusion