A GA-ANFIS Model for the Prediction of Biomass Elemental Properties

Obafemi O. Olatunji, Stephen Akinlabi, Nkosinathi Madushele, Paul A. Adedeji

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Citations (Scopus)

Abstract

The elemental composition of biomass is a significant property, which determines the energy content of biomass feedstock. This article develops a prediction model based on a hybrid adaptive neuro-fuzzy inference system (ANFIS) optimized with genetic algorithm (GA). The model inputs were the proximate constituents of biomass which are ash, fixed carbon, and volatile matter. These were used to predict the hydrogen (H), oxygen (O) and carbon (C) content of biomass fuels. The proposed algorithm was evaluated based on some known performance metrics. The root mean squared error (RMSE), mean absolute deviation (MAD), mean absolute percentage error (MAPE), coefficient of correlation (CC), mean absolute error (MAE) are 3.673, 2.4609, 5.1757, 0.9464, 0.309 at computation time (CT) of 33.65 secs for carbon (C); 0.6293, 0.4168, 8.3011, 0.75581, 0.0716 at CT of 40.21 secs for hydrogen (H); 4.4538, 3.1042, 13.3983,0.9167, 0.9899 at CT of 33.57 secs for oxygen (O), respectively. Regression analysis was also carried out to determine the level of dependence among the correlated variables. The model performance shows that GA-ANFIS can be applied in the computation of the elemental composition of biomass for strategic decision-making.

Original languageEnglish
Title of host publicationTrends in Manufacturing and Engineering Management - Select Proceedings of ICMechD 2019
EditorsS. Vijayan, Nachiappan Subramanian, K. Sankaranarayanasamy
PublisherSpringer Science and Business Media Deutschland GmbH
Pages1099-1114
Number of pages16
ISBN (Print)9789811547447
DOIs
Publication statusPublished - 2021
Event2nd International Conference on Mechanical Engineering Design, ICMechD 2019 - Chennai, India
Duration: 25 Apr 201926 Apr 2019

Publication series

NameLecture Notes in Mechanical Engineering
ISSN (Print)2195-4356
ISSN (Electronic)2195-4364

Conference

Conference2nd International Conference on Mechanical Engineering Design, ICMechD 2019
Country/TerritoryIndia
CityChennai
Period25/04/1926/04/19

Keywords

  • Biomass feedstock
  • Efficient utilization
  • Elemental composition
  • GA-ANFIS

ASJC Scopus subject areas

  • Automotive Engineering
  • Aerospace Engineering
  • Mechanical Engineering
  • Fluid Flow and Transfer Processes

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