Enhanced lignin extraction and optimisation from oil palm biomass using neural network modelling

Tazien Rashid, Syed Ali Ammar Taqvi, Farooq Sher, Saddaf Rubab, Murugesan Thanabalan, Muhammad Bilal, Badar ul Islam

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

Lignin from industrial crops is the most promising feedstock which can be used to function modern industrial societies. However, it is very challenging to separate lignin from lignocellulosic biomass effectively. Commercial application of lignin faces many challenges concerning practical applications and sub-optimal extraction approaches. Investigating one factor at a time is a significant limitation in standard experimental protocols. The current processing conditions need to be improved, which can be performed by modelling the processing conditions and identifying the most appropriate process conditions to suit the market demands. In this study, both the response surface methodology (RSM) and an artificial neural network (ANN) model was developed for the enhanced lignin extraction from the available experimental data of our previous work. The effect of various operating parameters such as; extraction temperature, time, particle size range and solid loading affecting the lignin extraction efficiency was optimally analyzed. Likewise, this is the first study reporting a detailed comparison and prediction of lignin extraction using RSM and ANN. The models were evaluated through the coefficient of determination (R2), Root Means Square Error (RMSE) Mean Average Deviation (MAD) and Average Absolute Relative Error (AARE) showing that the ANN was superior (R2 = 0.9933, RMSE = 1.129) to the RSM model (R2 = 0.8805, RMSE = 4.784) for lignin extraction efficiency predictions using various species of oil palm biomass. The results showed the accuracy of the ANN model in the prediction of lignin extraction from empty fruit bunches (EFB), palm mesocarp fibre (PMF) and palm kernel shells (PKS), as compared to the RSM model.

Original languageEnglish
Article number120485
JournalFuel
Volume293
Early online date27 Feb 2021
DOIs
Publication statusE-pub ahead of print - 27 Feb 2021

Bibliographical note

Funding Information:
The authors are grateful for the financial supports from Universiti Teknologi PETRONAS Malaysia and the Engineering and Physical Sciences Research Council (EPSRC) UK.

Publisher Copyright:
© 2021 Elsevier Ltd

Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.

Keywords

  • ANN modelling
  • Artificial Intelligence
  • Lignin extraction
  • Oil palm biomass
  • Prediction modelling
  • Renewable energy
  • RSM

ASJC Scopus subject areas

  • Chemical Engineering(all)
  • Fuel Technology
  • Energy Engineering and Power Technology
  • Organic Chemistry

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