Work

General Models for Predicting Torrefaction Solid Yield for Any Type of Biomass and Reactor: A Comparison of Empirical, Kinetic and Machine Learning Methods

Year: 2026

Type: article

Abstract: Torrefaction is a pre-treatment that can be applied to improve the fuel properties of biomass, reducing solid mass while simultaneously increasing carbon content, reducing oxygen content, and increasing higher heating values. Solid yield from torrefaction is particularly important as it affects many other properties. It is challenging to obtain a generalized model that can predict solid yield for any type of biomass with any type of torrefaction reactor. A comparison is made here between empirical, kinetic, and machine learning models for predicting solid yield. The highest-accuracy general model, based on a dataset of 400 points representing the torrefaction of a diverse range of biomass types and reactor types, is a gradient boosting method with a testing accuracy R2 of 0.925 and an average absolute error of 3.4%. However, for data outside the training range (e.g., high moisture or higher carbon content biomass), the average absolute error for this method increases to 11.7%, while a new optimized 2-step kinetic model is shown to give average absolute errors from 7.6% to 9.4% for data inside and outside the training range, offering a simple alternative that is more accurate than existing kinetic models and correlations.

Source: Energies

DOI: 10.3390/en19215318

Author: Michael Binns

Institution: Dongguk University

Language: English

Topic: Thermochemical Biomass Conversion Processes

Subfield: Fuel Technology

Field: Energy

Domain: Physical Sciences

SDG: Affordable and clean energy

Open Access status: gold

Estimated APC paid: $3,137