Hybrid evolutionary machine learning framework optimizing biochar production in biomass pyrolysis
Year: 2026
Type: article
Abstract: Growing interest in renewable energy worldwide has heightened the need for efficient modeling of biomass conversion. This study proposes a hybrid framework coupling five machine-learning models (ELM, ANN, RF, SVR, and XGB) with six population-based optimizers (SGA, PSO, DE, ABC, GWO, and xNES) to predict biochar yield in biomass pyrolysis. The database contains 423 experimental records from 44 lignocellulosic feedstocks, integrating proximate/elemental properties with operating conditions. Five learning algorithms were benchmarked, and each was tuned by every optimizer, enabling a systematic direct comparison of 30 hybrid configurations. Evolutionary hyperparameter tuning, evaluated using 5-fold cross-validation and 30 stratified train–test repetitions, improved accuracy and generalization relative to non-optimized learners. The best configuration (ABC–XGB) achieved R 2 = 0 . 815 , RMSE = 3.845, and MAE = 2.199 on the test sets. ANOVA ( p < 0 . 05 ) indicates that performance differences are driven mainly by the ML estimator rather than the optimizer, although DE, ABC, and GWO provided stable tuning across runs. Reliability was further examined using propagation-based uncertainty quantification with bounded input sampling, yielding narrow confidence intervals, near-zero mean prediction error, and low variability. The proposed evolutionary-ML surrogate model captures nonlinear thermochemical relationships and supports the design, optimization, and control of biomass pyrolysis and related bioenergy systems.
Source: Fuel Processing Technology
Language: English
Subfield: Biomedical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,180
Funders: Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, Conselho Nacional de Desenvolvimento Científico e Tecnológico, Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro, Financiadora de Estudos e Projetos, Fundação de Amparo à Pesquisa do Estado de Minas Gerais, Universidade Federal de Juiz de Fora
OpenAlex ID: W7129696097