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<p>The synthesis of sulfur–nitrogen co-doped Fe<sub>2</sub>O<sub>3</sub> nanostructures is crucial for enhancing dye removal efficiency in wastewater treatment. However, optimizing the synthesis process to achieve accurate predictions of dye removal performance remains a challenging task due to the complex interactions between parameters. This research presents a new approach for synthesizing sulfur–nitrogen co-doped Fe<sub>2</sub>O<sub>3</sub> nanostructures by hybridizing the similarity-navigated graph neural network (SNGNN) with the Namib beetle optimization (NBO) algorithm, hereafter referred to as the SNGNN-NBO method. The main aim of this work is to accurately predict the methylene blue dye removal efficiency. The SNGNN method is employed to predict the dye removal percentage, ensuring that the synthesis process is optimized for enhanced nanostructure properties. The NBO algorithm is used to optimize the weight parameter of the SNGNN method. The proposed technique is evaluated using MATLAB and compared with various existing approaches, such as ant colony optimization (ACO), artificial neural network-genetic algorithm (ANN-GA), and adaptive particle swarm optimization (APSO). The proposed method achieves a low error of 0.1% and a high dye removal percentage of 98%, outperforming the existing methods. This demonstrates the effectiveness of the SNGNN-NBO approach in accurately predicting and optimizing dye removal efficiency, offering a comprehensive solution for the synthesis of sulfur–nitrogen co-doped Fe<sub>2</sub>O<sub>3</sub> nanostructures.</p>

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Hybrid approach for enhanced synthesis and efficiency prediction of sulfur–nitrogen co-doped Fe2O3 nanostructures in methylene blue dye removal processes

  • 期刊论文

  • 发表日期：

    2025年12月18日

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  • V. S. Anusuya Devi,
  • K. A. Jayasheel Kumar,
  • Rakesh Chandrashekar,
  • Jagannath J. Kadam

摘要

The synthesis of sulfur–nitrogen co-doped Fe2O3 nanostructures is crucial for enhancing dye removal efficiency in wastewater treatment. However, optimizing the synthesis process to achieve accurate predictions of dye removal performance remains a challenging task due to the complex interactions between parameters. This research presents a new approach for synthesizing sulfur–nitrogen co-doped Fe2O3 nanostructures by hybridizing the similarity-navigated graph neural network (SNGNN) with the Namib beetle optimization (NBO) algorithm, hereafter referred to as the SNGNN-NBO method. The main aim of this work is to accurately predict the methylene blue dye removal efficiency. The SNGNN method is employed to predict the dye removal percentage, ensuring that the synthesis process is optimized for enhanced nanostructure properties. The NBO algorithm is used to optimize the weight parameter of the SNGNN method. The proposed technique is evaluated using MATLAB and compared with various existing approaches, such as ant colony optimization (ACO), artificial neural network-genetic algorithm (ANN-GA), and adaptive particle swarm optimization (APSO). The proposed method achieves a low error of 0.1% and a high dye removal percentage of 98%, outperforming the existing methods. This demonstrates the effectiveness of the SNGNN-NBO approach in accurately predicting and optimizing dye removal efficiency, offering a comprehensive solution for the synthesis of sulfur–nitrogen co-doped Fe2O3 nanostructures.