Optimizing mixture properties of biodiesel production using genetic algorithm-based evolutionary support vector machine

Cheng, Min-Yuan and Prayogo, Doddy and Ju, Yi-Hsu and Wu, Yu-Wei and Sutanto, Sylviana (2016) Optimizing mixture properties of biodiesel production using genetic algorithm-based evolutionary support vector machine. International Journal of Green Energy , 13 (15). pp. 1599-1607. ISSN 15435075

PDF Download (1398Kb)

Abstract

Nowadays, biodiesel is used as one of the alternative renewable energy due to the increasing energy demand. However, optimum production of biodiesel still requires a huge number of expensive and time-consuming laboratory tests. To address the problem, this research develops a novel Genetic Algorithm-based Evolutionary Support Vector Machine (GA-ESIM). The GA-ESIM is an Artificial Intelligence (AI)-based tool that combines K-means Chaotic Genetic Algorithm (KCGA) and Evolutionary Support Vector Machine Inference Model (ESIM). The ESIM is utilized as a supervised learning technique to establish a highly accurate prediction model between the input--output of biodiesel mixture properties; and the KCGA is used to perform the simulation to obtain the optimum mixture properties based on the prediction model. A real biodiesel experimental data is provided to validate the GA-ESIM performance. Our simulation results demonstrate that the GA-ESIM establishes a prediction model with better accuracy than other AI-based tool and thus obtains the mixture properties with the biodiesel yield of 99.9%, higher than the best experimental data record, 97.4%.

Actions (login required)