Explainable Artificial Intelligence Assisted Modeling of Malachite Green Adsorption onto SBA-15–Zn–Fe Composite

Vollständiger Abstract

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Dye-containing industrial effluents pose substantial risks to aquatic ecosystems. In this study, a material prepared using the stated SBA-15–Zn–Fe synthesis procedure was evaluated for malachite green (MG) removal and modeled using machine-learning and explainable artificial intelligence techniques. The dataset comprised 315 experimental observations covering initial MG concentrations of 100–500 mg L−1, adsorbent concentrations of 0.5–3.0 g L−1, pH values of 5–9, temperatures of 25–45 ∘C, and contact times of 0–360 min. At an initial MG concentration of 500 mg L−1, an adsorbent concentration of 1.0 g L−1, pH 9.0, 45 ∘C, and 90 min, the material achieved 99.65% MG removal. The Langmuir-estimated maximum monolayer capacity was 1428.57 mg g−1. A multilayer perceptron (MLP) with a 100–50–25–12 hidden-layer architecture, hyperbolic tangent activation, and L-BFGS optimization was developed to predict residual MG concentration. The prespecified MLP achieved an R2 of 0.9624 on the strictly held-out 20% internal test subset. Five-fold cross-validation yielded a mean R2 of 0.8420 with a fold-wise standard deviation of 0.1273, while pooled contact-time-based LOGO-CV yielded an R2 of 0.7411, indicating reduced transferability when entire contact-time groups were excluded from fitting. Nominal 95% CV+ prediction intervals achieved 98.41% empirical coverage on the held-out test subset, although their relatively broad widths indicated non-negligible predictive uncertainty. PFI and SHAP showed that the fitted MLP relied most strongly on contact time and initial MG concentration. Longer contact times were generally associated with lower predicted residual concentrations, whereas higher initial concentrations were associated with higher predicted residual concentrations. Overall, the framework provided an interpretable assessment of MG adsorption within the investigated experimental domain; external validity and extrapolation beyond this domain were not established.