Application of a Supervised Learning Machine for Accurate Prognostication of Hydrogen Contents of Bio-Oil. (19th July 2021)

• Record Type: Journal Article
• Authors: Xu, Binghui; Chen, Tzu-Chia; Ahangari, Danial; Alizadeh, S. M.; Elveny, Marischa; Makhdoumi, Jeren
• Other Names: Baghban Alireza Academic Editor.
• Is Part Of: International journal of chemical engineering. Volume 2021(2021)
• Publication Date: 2021-07-19
• Subjects: Chemical engineering -- Periodicals; Chemical engineering; Electronic journals; Periodicals; 660
• DOI: 10.1155/2021/7548251
• Languages: English
• ISSNs: 1687-806X

Abstract

Abstract : This paper deals with modeling hydrogen contents of bio-oil (H-BO) as a function of pyrolysis conditions and biomass compositions of feedstock. The support vector machine algorithm optimized by the grey wolf optimization method has been used in modeling this end. Comprehensive data for this purpose were aggregated from previous sources and reports. The results of various analyses showed that this algorithm has a high ability to predict actual results. The calculated values of R 2, MRE (%), MSE, and RMSE were obtained as 0.973, 1.98, 0.0568, and 0.241, respectively. According to the results of various analyses, the high performance of this model in predicting the output values was proved. Also, by comparing this model with the previously proposed models in terms of accuracy, it was observed that this model had a better performance. This algorithm can be a good alternative to costly and time-consuming laboratory data.