Fixed-Bed Column Adsorption of Methylene Blue onto Dried Activated Sludge: Modeling and Optimization with ANNs

Authors

  • Meriem Sediri
  • Radhia Yous
  • Hakima Cherifi
  • Mounia Louanes
  • Abdeltif Amrane

DOI:

https://doi.org/10.22399/ijcesen.4275

Keywords:

Artificial neural network (ANN), died activated sludge, methylene blue, fixed bed adsorption, modelling

Abstract

This study investigates the continuous adsorption of methylene blue (MB) onto dried activated sludge in a fixed-bed column. The effects of key operational parameters—initial MB concentration (50-300 mg/L), flow rate (2.5-7 mL/min), and bed height (2.5-5 cm)—on the breakthrough behavior were systematically examined. An Artificial Neural Network (ANN) model was developed to predict the dynamic adsorption process. The ANN demonstrated exceptional predictive power, achieving a near-perfect correlation (R = 0.998) with low error values (RMSE = 0.018). Sensitivity analysis further revealed the relative importance of each input parameter. The results conclusively establish the ANN as a powerful and accurate tool for modeling the complex dynamics of MB adsorption onto this waste-derived adsorbent, facilitating potential process optimization.

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Published

2025-11-12

How to Cite

Meriem Sediri, Radhia Yous, Hakima Cherifi, Mounia Louanes, & Abdeltif Amrane. (2025). Fixed-Bed Column Adsorption of Methylene Blue onto Dried Activated Sludge: Modeling and Optimization with ANNs. International Journal of Computational and Experimental Science and Engineering, 11(4). https://doi.org/10.22399/ijcesen.4275

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Research Article