A soft computing approach to optimize the production of biodiesel

Resumen

There is an increasing global concern for environmental protection for the conservation of non-renewal natural resources. It needs to be obtain an alternative, renewable and biodegradable combustible like biodiesel. Waste cooking oil is a potential replacement for vegetable oils in the production of biodiesel. Biodiesel is synthesized by direct transesterification of vegetable oils, which is controlled by several inputs or process variables, including the dosage of catalyst, process temperature, mixing speed, mixing time, humidity and impurities of waste cooking oil. This study proposes a methodology to improve the production of biodiesel based on the use of soft computing techniques to predict several features of biodiesel production. The method selected a group of regression models based on Support Vector Machines (SVM) techniques to perform a prediction of several properties of a biodiesel sample taking into account a configuration of 7 test inputs. This test inputs were: molar ratio, dosage of catalyst, temperature, mixing speed, mixing time, humidity and impurities. Then and based on these inputs, the features to predict were: yield, turbidity, density, viscosity and high heating to obtain a better understanding of the process. Finally, considering the samples of the design of experiments studied, it has been observed that SVM models, based on a radial basic function kernel, record accurate results, with the best performance in four of the five features, improving in all the cases the accuracy obtained using linear regression. © Springer International Publishing AG 2017.