Accurate modeling of biochar yield based on proximate analysis

Accurate prediction of biochar yield from biomass pyrolysis is essential for optimizing production in sustainable agriculture, yet remains technically challenging due to multiple interacting factors. This study developed a predictive framework using a curated dataset of 211 samples, each including 14 normalized input features (chemical, physical, operational) and one output variable (biochar yield, wt%). Machine learning modeling utilized Gradient Boosted Decision Trees (GBDT), with hyperparameters exhaustively tuned via Gaussian Processes Optimization (GPO), Evolutionary Strategies (ES), Bayesian Probability Improvement (BPI), and Batch Bayesian Optimization (BBO). Models were evaluated on a train-test split (90% training, 10% testing) and the best performance was achieved by the GBDT–BPI model: total R² = 0.982, mean squared error (MSE) = 1.65, average absolute relative error percentage (AARE%) = 1.35; on the test set, R² = 0.693, MSE = 15.2, AARE% = 9.54. Comparative analysis showed GBDT–BPI outperformed GBDT–GPO (total R² = 0.978; MSE = 2.01; AARE% = 1.72), GBDT–ES (total R² = 0.976; MSE = 2.13; AARE% = 3.81), and GBDT–BBO (total R² = 0.980; MSE = 1.81; AARE% = 2.58). Sensitivity study presented reside duration, temperature, and fixed carbon as the top parameters of yield. Time efficiency was comparable for all optimizers, with BBO taking the longest (313 s/500 iterations). Diagnostic leverage analysis demonstrated high data quality, with less than 1% flagged as influential outliers. This integrated approach delivered high-accuracy, interpretable prediction, and revealed critical parameters for process optimization in biomass pyrolysis workflows.