Explainable Machine Learning for Methylene Blue Removal Under Irradiation: Assessing Nominal Cu-Loading in AC@NiO
Catalysts, cilt.16, sa.9, 2026 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 16 Sayı: 9
- Basım Tarihi: 2026
- Doi Numarası: 10.3390/catal16090810
- Dergi Adı: Catalysts
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Chemical Abstracts Core, Compendex, Academic Search Ultimate (EBSCO), Materials Science & Engineering Collection (ProQuest), Technology Collection (ProQuest)
- Anahtar Kelimeler: AC@NiO, chronological evaluation, explainable machine learning, methylene blue, nominal Cu-loading, permutation importance, SHAP
- Sivas Cumhuriyet Üniversitesi Adresli: Hayır
Özet
The time-dependent removal performance under irradiation of activated-carbon-supported nickel oxide (AC@NiO) samples containing nominal Cu-loadings of 0%, 3%, 5%, 7.5%, and 10% was examined using experimental concentration data and an explainable machine-learning (ML) framework. Reaction time and nominal Cu-loading were used as predictors. Preliminary screening with (Formula presented.) as the common target showed that Random Forest (RF) outperformed K-Nearest Neighbors, Multi-Layer Perceptron, and Support Vector Regression. The target-specific regularized RF models were fitted for (Formula presented.) and (Formula presented.) ; normalized apparent removal efficiency was derived deterministically as (Formula presented.). Across 10 controlled random seeds, the mean leave-one-out cross-validation (Formula presented.) values were (Formula presented.) for (Formula presented.) and the derived (Formula presented.), and (Formula presented.) for (Formula presented.). A fixed chronological evaluation, trained at (Formula presented.) min and evaluated at (Formula presented.) min, yielded mean (Formula presented.) values of (Formula presented.) and (Formula presented.), respectively. Because the composition-specific RF predictions were constant across this boundary interval, these scores are interpreted as later-time boundary diagnostics rather than evidence of temporal extrapolation. Point-estimate SHAP, impurity-based, and permutation-based analyses assigned greater predictive importance to reaction time, whereas moving-block bootstrap results showed that this ranking was sensitive to temporal resampling. Among the five tested compositions, the nominal 5% Cu-containing sample exhibited the most favorable apparent removal profile. This finding is specific to the investigated conditions and does not establish a universal optimum or a causal physicochemical mechanism.