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April 19, 2026Langmuir1 citations

Melamine-Derived MgO/AC Nanocomposite for Efficient Dye Removal: Integrating RSM and Machine Learning Modeling

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HNHanie NazariIslamic Azad University, TehranFSFarhad SalimiIslamic Azad University, TehranCKChangiz KaramiIslamic Azad University, Tehran

Key Points

  • The aim is to evaluate the effectiveness of a MgO/AC nanocomposite for removing methylene blue dye from water.
  • Synthesis of melamine-derived MgO/AC nanocomposite
  • Optimization of operating parameters using response surface methodology
  • Application of machine learning to model adsorption behavior
  • Comparison of different models with focus on ANFIS performance
  • Achieved 94.72% methylene blue removal efficiency under optimized conditions
  • Despite low BET surface area of 15.19 m²/g, high removal efficiency was noted
  • ANFIS model exhibited the best predictive performance with R² = 0.981

Abstract

In this study, a melamine-derived MgO/activated carbon (MgO/AC) nanocomposite was synthesized and evaluated for methylene blue (MB) removal from aqueous solutions. The incorporation of MgO nanoparticles and nitrogen functionalities originating from melamine generates chemically active adsorption sites that enhance dye-adsorbent interactions beyond surface area effects. Despite a relatively low BET surface area (15.19 m2/g), the MgO/AC composite achieved a high MB removal efficiency of 94.72% under optimized conditions using a low adsorbent dosage. Response surface methodology was employed to statistically optimize the operating parameters, while machine learning models were applied to capture nonlinear adsorption behavior. Among the evaluated models, ANFIS exhibited superior predictive performance (R2 = 0.981), indicating its suitability for modeling complex adsorption systems. This integrated experimental-modeling approach highlights the potential of melamine-derived MgO/AC as an efficiency-oriented adsorbent for dye-contaminated wastewater treatment.

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Cite This Study

Nazari et al. (2026) studied this question.

synapsesocial.com/papers/69e4713b010ef96374d8dc05https://doi.org/10.1021/acs.langmuir.5c05181
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