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Updated: Jul 28, 2025

Biomass Conversion to Produce Hydrocarbon Liquid Fuel Via Hot-vapor Filtered Fast Pyrolysis and Catalytic Hydrotreating

Biomass Conversion to Produce Hydrocarbon Liquid Fuel Via Hot-vapor Filtered Fast Pyrolysis and Catalytic Hydrotreating

Published on: December 25, 2016

Modelling hydrogen production from biomass pyrolysis for energy systems using machine learning techniques.

Paulino José García-Nieto¹, Esperanza García-Gonzalo², Beatriz María Paredes-Sánchez³

¹Department of Mathematics, Faculty of Sciences, University of Oviedo, 33007, Oviedo, Spain. pjgarcia@uniovi.es.

Environmental Science and Pollution Research International

|May 30, 2023

Summary

This study introduces a smart model using artificial bee colony and support vector machines for predicting hydrogen gas production from biomass pyrolysis. The model accurately characterizes key parameters and forecasts hydrogen yields, demonstrating high predictive performance.

Keywords:

Artificial bee colony (ABC)Bioenergy Hydrogen gas production (HGP)Multilayer perceptron (MLP)Support vector regression (SVR)

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Area of Science:

Chemical Engineering
Artificial Intelligence
Sustainable Energy

Background:

Hydrogen gas is increasingly vital as an energy feedstock within Industry 4.0.
Biomass pyrolysis is a key process for generating hydrogen gas.
Accurate characterization and prediction of hydrogen gas production (HGP) are crucial.

Purpose of the Study:

To develop a novel artificial smart model for characterizing HGP from biomass.
To identify the significance of physico-chemical parameters on HGP.
To accurately forecast HGP using advanced computational methods.

Main Methods:

Utilized support vector machines (SVMs) combined with the artificial bee colony (ABC) optimizer.
Developed an innovative modeling approach for HGP prediction.
Applied the model to an observed dataset for validation.

Main Results:

Achieved a coefficient of determination of 0.9464 for HGP estimation.
Reached a correlation coefficient of 0.9751 for HGP prediction.
Demonstrated the significance of physico-chemical parameters on HGP through the model.

Conclusions:

The ABC/SVM model effectively characterizes and predicts hydrogen gas production from biomass pyrolysis.
The developed procedure shows suitable effectiveness in approximating observed HGP data.
This approach offers a powerful tool for optimizing hydrogen gas generation processes.