Prediction of char production from slow pyrolysis of lignocellulosic biomass using multiple nonlinear regression and artificial neural network. Li, T. Y. Journal of Analytical and Applied Pyrolysis, 2021. abstract bibtex 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 (\textgreater0.5) 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.
@article{li_prediction_2021,
title = {Prediction of char production from slow pyrolysis of lignocellulosic biomass using multiple nonlinear regression and artificial neural network},
abstract = {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 ({\textgreater}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.},
language = {en},
journal = {Journal of Analytical and Applied Pyrolysis},
author = {Li, Ting Yan},
year = {2021},
keywords = {ANN, Pyrolysis modeling, pyrolysis, slow pyrolysis},
pages = {13},
}
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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 (\\textgreater0.5) 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.","language":"en","journal":"Journal of Analytical and Applied Pyrolysis","author":[{"propositions":[],"lastnames":["Li"],"firstnames":["Ting","Yan"],"suffixes":[]}],"year":"2021","keywords":"ANN, Pyrolysis modeling, pyrolysis, slow pyrolysis","pages":"13","bibtex":"@article{li_prediction_2021,\n\ttitle = {Prediction of char production from slow pyrolysis of lignocellulosic biomass using multiple nonlinear regression and artificial neural network},\n\tabstract = {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 ({\\textgreater}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. 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