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Artificial intelligence models for methylene blue removal using functionalized carbon nanotubes.

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Artificial intelligence (AI) models effectively predict the methylene blue (MB) adsorption capacity of functionalized carbon nanotubes (CNTs). This research optimizes CNT synthesis and validates AI

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Area of Science:

  • Materials Science
  • Environmental Chemistry
  • Computational Chemistry

Background:

  • Functionalized carbon nanotubes (CNTs) are promising adsorbents for pollutant removal.
  • Methylene blue (MB) is a common model pollutant used to evaluate adsorbent performance.
  • Optimizing CNT synthesis is crucial for maximizing adsorption efficiency.

Purpose of the Study:

  • To assess the feasibility of using artificial intelligence (AI) to model the adsorption of methylene blue (MB) onto functionalized carbon nanotubes (CNTs).
  • To determine optimal conditions for synthesizing CNTs via acetylene pyrolysis for enhanced MB adsorption.
  • To evaluate the performance of different AI models, including recurrent neural networks (RNNs) and feed-forward neural networks (FFNNs), in predicting MB adsorption capacity.

Main Methods:

  • CNTs were synthesized through acetylene pyrolysis at an optimized temperature of 550 °C, reaction time of 37.3 min, and H2/C2H2 gas ratio of 1.0.
  • Experimental adsorption data of MB on CNTs was fitted to kinetic (Pseudo-second-order) and isotherm (Langmuir) models.
  • AI models, specifically RNN and FFNN, were developed and trained using experimental data to predict MB adsorption capacity.

Main Results:

  • The CNT synthesis yielded optimal properties for MB adsorption.
  • Experimental MB adsorption data showed excellent fit to the Pseudo-second-order kinetic model (R²=0.998) and Langmuir isotherm model (R²=0.989, qm=250.0 mg/g).
  • AI modeling demonstrated high predictive accuracy, with FFNN achieving R²=0.9658 and RNN achieving R²=0.9471, indicating strong correlation between predicted and experimental adsorption capacities.

Conclusions:

  • AI modeling, particularly FFNN, shows significant potential for accurately predicting the adsorption performance of CNTs for MB removal.
  • Optimized CNTs synthesized under specific pyrolysis conditions exhibit high MB adsorption capacity.
  • The integration of AI with experimental data offers a powerful approach to enhance the efficiency of CNT-based wastewater treatment solutions.