Prediction of char production from slow pyrolysis of lignocellulosic biomass using multiple nonlinear regression and artificial neural network

Ting Yan Li, Huan Xiang, Yang Yang, Jiawei Wang*, Güray Yildiz

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


Char produced from lignocellulosic biomass via slow pyrolysis have become one of the most feasible alternatives that can partially replace the utilisation of fossil fuels for energy production. In this study, the relationship between compositions of lignocellulosic biomass, operating conditions of slow pyrolysis, and characteristics of produced char have been analysed by using multiple nonlinear regression (MnLR) and artificial neural networks (ANN). Six input variables (temperature, solid residence time, production capacity, particle size, and fixed carbon and ash content) and five responses (char yield, and fixed carbon, volatile matter, ash content, HHV of produced char) were selected. A total of 57 literature references with 393 - 422 datasets were used to determine the correlation and coefficient of determination (R2) between the input variables and responses. High correlation results (>0.5) existed between pyrolysis temperature and char yield (-0.502) and volatile matter of produced char (-0.619), ash content of feedstock and fixed carbon (-0.685), ash content (0.871) and HHV (-0.571) of produced char. Whilst the quadratic model was selected for the regression model, then the model was further optimised by eliminating any terms with p-values greater than 0.05. The optimised MnLR model results showed a reasonable prediction ability of char yield (R2 = 0.5579), fixed carbon (R2 = 0.7763), volatile matter (R2 = 0.5709), ash (R2 = 0.8613), and HHV (R2 = 0.5728). ANN model optimisation was carried out as the results showed “trainbr” training algorithm, 10 neurons in the hidden layer, and “tansig” and “purelin” transfer function in hidden and output layers, respectively. The optimised ANN models had higher accuracy than MnLR models with the R2 greater than 0.75, including 0.785 for char yield, 0.855 for fixed carbon, 0.752 for volatile matter, 0.951 for ash and 0.784 for HHV, respectively. The trained models can be used to predict and optimise the char production from slow pyrolysis of biomass without expensive experiments.
Original languageEnglish
Article number105286
JournalJournal of Analytical and Applied Pyrolysis
Early online date10 Aug 2021
Publication statusPublished - 1 Oct 2021

Bibliographical note

© 2021, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International

Funding: Institutional Links grant (No. 527641843), under the Turkey partnership. The grant is funded by the UK Department for Business, Energy and Industrial Strategy together with the Scientific and Technological Research Council of Turkey (TÜBİTAK; Project no.119N302) and delivered by the British Council.


  • Artificial neural network
  • Char
  • Lignocellulosic biomass
  • Multiple nonlinear regression
  • Slow pyrolysis


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