Efficient Catalytic Conversion of Waste Peanut Shells into Liquid Biofuel : An Artificial Intelligence Approach

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

15 Scopus Citations
View graph of relations

Author(s)

  • Pan Li
  • Zeji Du
  • Chun Chang
  • Shiqiang Zhao
  • Guizhuan Xu

Detail(s)

Original languageEnglish
Pages (from-to)1791-1801
Journal / PublicationEnergy and Fuels
Volume34
Issue number2
Online published17 Jan 2020
Publication statusPublished - 20 Feb 2020
Externally publishedYes

Abstract

Artificial intelligence approach can be used to solve complicated process problems. A hybrid methodology comprising artificial neural network (ANN) and genetic algorithms (GA) was utilized to model and optimize the methanolysis process of waste peanut shells. Acid catalytic methanolysis of waste peanut shells into liquid biofuel-methyl levulinate (ML) was investigated. The combination of sulfuric acid with extremely low concentration and Al2(SO4)3 was identified as the efficient mixed acid catalytic system. The ML yield under optimal conditions optimized by response surface methodology (RSM) was 16.49%, while the ML yield optimized by ANN-GA was 17.61%. The results showed that ANN-GA had a higher optimizing ability than the RSM model. Meanwhile, the methanolysis kinetics provided insights into the reaction routes for ML production. Moreover, Al2(SO4)3 can be recycled and reused five times without much decrease of the ML yield. This study suggested that waste peanut shells can be used as potential raw materials for ML production and ANN-GA can serve as a powerful tool for biofuel processing technology. © 2020 American Chemical Society.

Citation Format(s)

Efficient Catalytic Conversion of Waste Peanut Shells into Liquid Biofuel: An Artificial Intelligence Approach. / Li, Pan; Du, Zeji; Chang, Chun et al.
In: Energy and Fuels, Vol. 34, No. 2, 20.02.2020, p. 1791-1801.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review