Algorithmic prediction and thermochemical interpretability of biochar cation exchange capacity utilizing optimized gradient boosting decision trees

Optimizing biomass pyrolysis for environmental remediation is bottlenecked by the complex, non-linear thermochemical pathways governing the cation exchange capacity (CEC) of biochar. To circumvent costly empirical trial-and-error, this study aimed to develop a generalized machine learning framework capable of accurately predicting biochar CEC while decoupling its underlying mechanistic pathways. A comprehensive dataset encompassing elemental biomass compositions and operational parameters was modeled using a Gradient Boosting Decision Tree architecture. To ensure robust out-of-sample generalization, structural hyperparameters were optimized across four distinct algorithms: Statistical analysis revealed that Gaussian Process Optimization (GPO) achieved the optimal predictive equilibrium, yielding an exceptional testing coefficient of determination of 0.9353 with minimal error variations, effectively suppressing the overfitting observed in aggressively exploitative Bayesian variants. Additionally, the integration of Shapley Additive Explanations (SHAP) provided critical mathematical interpretability. Global and directional feature analyses validated a dual-pathway mechanism, demonstrating that CEC is primarily driven by the resilient inorganic ash fraction, while secondary organic functional potential is significantly throttled by devolatilization at elevated pyrolysis temperatures. This optimized framework offers a reliable computational foundation for tailoring biochar synthesis without exhaustive physical characterizations. It should be noted that due to current literature data constraints, the model omits critical predictors such as lignocellulosic ratios, heating rate, and categorical feedstock types, which remains a major limitation for future frameworks to address.

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