Work

Integrating Experimental Pyrolysis and Machine Learning for Sustainable Biochar Yield Prediction from Lignocellulosic Waste

Year: 2026

Type: article

Abstract: Biochar production from lignocellulosic waste represents a sustainable route for biomass valorization and carbon management within circular bioeconomy frameworks. In this study, biochar was produced from two abundant agricultural wastes in Türkiye—tea-brewing residues and almond husks—via controlled non-isothermal pyrolysis, and biochar yield was modeled using data-driven machine learning approaches. The effects of key process parameters, including carbonization temperature (37–850 °C covering drying/pre-pyrolysis and pyrolysis regions), residence time (1–150 min), and heating rate (10–60 °C min−1), were evaluated using regression-based, ensemble, and deep learning models. Model performance was evaluated using cross-validation on training and testing datasets. The results showed that linear models exhibited limited predictive capability (R2 < 0.95), while regularized and ensemble models improved performance (R2 ≈ 0.97–0.99). Among all approaches, Gaussian Process Regression (GPR) achieved the highest predictive performance (R2 ≈ 0.99, RMSE ≈ 0.06), indicating its superior ability to capture nonlinear relationships, particularly for limited datasets. Sensitivity and partial dependence analyses identified carbonization temperature as the dominant factor controlling biochar yield, with sharp declines observed above 600 °C. Optimal yields of 52–55% were obtained at 400–500 °C and residence times of 10–15 min, while lower heating rates enhanced yield stability. Overall, the results demonstrate that advanced machine learning models provide reliable tools for optimizing biochar production and supporting sustainable thermochemical conversion of lignocellulosic waste for energy and carbon-oriented sustainability applications.

Source: Sustainability

DOI: 10.3390/su18105203

Authors: Abdulkarim Aljomah, Şeyda Taşar

Institution: Fırat University

Language: English

Topic: Thermochemical Biomass Conversion Processes

Subfield: Biomedical Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Responsible consumption and production, Affordable and clean energy, Climate action

Open Access status: gold

Estimated APC paid: $2,896