Application of artificial intelligence to maximize methane production from waste paper

A.G. Olabi*, Ahmed M. Nassef, Cristina Rodriguez, Mohammad A. Abdelkareem, Hegazy Rezk

*Corresponding author for this work

Research output: Contribution to journalArticle

Abstract

This article proposes a methodology based on artificial intelligence to enhance methane production from waste paper. The proposed methodology combines fuzzy logic-based modelling and modern optimization. Firstly, a robust Adaptive Network-based Fuzzy Inference System model of methane production process through fuzzy logic modelling is created using experimental datasets. Second, a particle swarm optimizer was used to obtain the optimal process conditions. During the optimization procedure, the beating time and feedstock/inoculum ratio are employed as decision variables in order to maximize methane production. The obtained resulted from the proposed methodology are compared with those obtained by response surface methodology. The results of the comparison confirmed the superiority of the proposed methodology. The fuzzy model shows a better fitting to the experimental data compared to ANOVA. The fuzzy model showed a higher coefficient of determination and a lower value of root mean squared errors compared to ANOVA. Moreover, the proposed strategy, that is, modelling and optimization, is an effective method for increasing the biomethane yield at extended range conditions.
Original languageEnglish
Number of pages11
JournalInternational Journal of Energy Research
Early online date23 Apr 2020
DOIs
Publication statusE-pub ahead of print - 23 Apr 2020

Keywords

  • biomass
  • biomethane
  • fuzzy logic
  • optimization
  • renewable energy
  • waste paper

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