Predicting bio-oil yield obtained from lignocellulosic biomass pyrolysis using artificial neural networks

ABSTRACT

The prediction of bio-oil yield from pyrolysis of lignocellulosic biomass is important for processes optimization, modeling, and installation’s design. This work models the bio-oil of lignocellulosic biomass yield by Artificial Neural Networks (ANN), being the inputs: cellulose, hemicellulose, lignin, pyrolysis temperature, heating rate, N₂ flow rate, and particle size. A database was created with 34 biomass types, modeling with 329 samples, training with 80%, and validating with 20%. A previous stage of screening was carried out with 100% of data for choosing the algorithm of the second phase and the number of neurons in the hidden layer; the selection criteria were the mean absolute error (MAE) and the correlation coefficient. The best performance was for backpropagation/Levenberg-Marquardt with 7:13:1 as ANN architecture. All ANN with less than 1% of MAE were tested for validating and the weight’s matrix of the best one is shown. The selected network with a correlation coefficient of 0.9739 and MAE of 1.7159% for validation, only had four outlier values between 5 and 6%, the remaining 62 samples with all 263 used in training, had less than 5% of difference compared to the experimental values, thus representing a very accurate model for predicting bio-oil yield.

KEYWORDS:

• Bio-oil
• artificial neural network
• pyrolysis
• biomass
• conversion

Acknowledgments

The authors wish to express their thanks to Bundesministerium für Bildung und Forschung (BMBF), funding the project entitled “Potentiale biogener Ressourcen für eine nachhaltige und umweltverträgliche energetische Nutzung in Kuba (BioReSCu).

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the Bundesministerium für Bildung, Wissenschaft und Forschung [01DN18018].