Optimizing mixture properties of biodiesel production using genetic algorithm-based evolutionary support vector machine. Issue 15 (7th December 2016)

• Record Type: Journal Article
• Authors: Cheng, Min-Yuan; Prayogo, Doddy; Ju, Yi-Hsu; Wu, Yu-Wei; Sutanto, Sylviana
• Is Part Of: International journal of green energy. Volume 13:Issue 15(2016)
• Page Start: 1599
• Page End: 1607
• Publication Date: 2016-12-07
• Subjects: Biodiesel production -- evolutionary support vector machine -- genetic algorithm -- in situ process -- rice bran; Power resources -- Research -- Periodicals; Energy industries -- Periodicals; Energy development -- Periodicals; 333.79
• DOI: 10.1080/15435075.2016.1206549
• Languages: English
• ISSNs: 1543-5075

Abstract

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%.