Machine Learning Applications in Biomass Conversion Technologies

Machine learning has emerged as a transformative tool in the optimisation and understanding of biomass conversion processes, encompassing hydrothermal carbonisation, pyrolysis and gasification. By learning complex, nonlinear relationships between feedstock characteristics, process conditions and product properties, data-driven models can predict yields, compositions and energy outputs with high accuracy. These approaches reduce reliance on time-consuming experiments, accelerate scale-up and support techno-economic and life-cycle assessments. Techniques such as random forest, gradient boosting and neural networks have been applied to forecast biochar and hydrochar quality, syngas composition and liquid biofuel yields. Interpretability methods reveal the relative importance of temperature, residence time, feedstock composition and other factors, guiding process design and control. Intelligent systems incorporating multi-target prediction enable simultaneous optimisation of multiple objectives—such as maximising carbon yield while tailoring surface area—facilitating the development of sustainable bioproducts and contributing to climate-mitigation strategies.

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Machine Learning Applications in Biomass Conversion Technologies publication trend

The graph below shows the total number of articles in machine learning applications in biomass conversion technologies across all publications each year (not limited to Nature Index journals).

Technical terms

Hydrothermal carbonisation: Thermochemical process converting wet biomass into hydrochar under moderate temperature and elevated pressure in an aqueous environment.

Pyrolysis: Thermal decomposition of biomass in the absence of oxygen to yield biochar, bio-oil and combustible gases.

Gasification: Partial oxidation of biomass at high temperature to produce syngas, a mixture of hydrogen, carbon monoxide and methane.

Biochar: Carbon-rich solid residue obtained from pyrolysis, used for soil amendment, carbon sequestration and pollutant adsorption.

Hydrochar: Solid carbonaceous product generated by hydrothermal carbonisation of biomass, characterised by higher oxygen content than biochar.

Random Forest: Ensemble learning method that builds multiple decision trees and aggregates their outputs for regression or classification.

Gradient Boosting: Sequential ensemble technique that combines weak learners by iteratively minimising prediction errors of prior models.

References

1. A novel intelligent system based on machine learning for hydrochar multi-target prediction from the hydrothermal carbonization of biomass. Biochar (2024).
2. Machine-learning-aided thermochemical treatment of biomass: a review. Biofuel Research Journal (2023).
3. Process optimization of biomass gasification with a Monte Carlo approach and random forest algorithm. Energy Conversion and Management (2022).