Computational intelligence for empirical modelling and optimization of methylene blue adsorption phenomena utilizing an activated carbon‐supported [Co( <scp> NH <sub>3</sub> </scp> ) <sub>6</sub> ]Cl <sub>3</sub> complex
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Bibliographic record
Abstract
Abstract The study focuses on the efficiency of hexaamminecobalt (III) chloride (HACo, [Co(NH3) 6 ]Cl 3 ) immobilized on activated carbon for removing methylene blue (MB) from water solutions. The primary objective of this study was to assess the sorption performance of HACo immobilized on activated carbon in removing MB from water solutions. Additionally, predictive models were developed to optimize the MB removal percentage. Lastly, the study aimed to determine the optimal conditions for achieving maximum MB removal. Samples were characterized using scanning electron microscopy. Batch sorption experiments were conducted to analyze the impact of MB concentration, adsorbent mass, pH, temperature, and contact time. Predictive models were built using multiple linear regression and neural network techniques, specifically artificial neural networks (ANN) and hybrid ANN–particle swarm optimization (ANN‐PSO). The PSO‐ANN model with a single hidden layer of eight neurons trained using the Levenberg–Marquardt algorithm demonstrated high accuracy in predicting MB removal percentage, with mean absolute percentage error (MAPE) = 0.083788, root mean square error (RMSE) = 0.11441, and R 2 = 0.99693. The MB adsorption process followed a mono‐layer with one energy model and a pseudo‐first‐order kinetic model. Optimization using the genetic algorithm revealed that the maximum MB removal percentage of 99.56% is achievable at an MB concentration of 9.36 mg/L, adsorbent mass of 15.72 mg, and temperature of 311.2 K. The study confirms the effectiveness of HACo immobilized on activated carbon for MB removal. The PSO‐ANN predictive model proved superior in accuracy compared to empirical models. Optimization results provide the optimal conditions for maximizing MB removal, offering valuable insights for practical applications.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it