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<p>Eriochrome Black T (EBT) is a stable and toxic azo dye widely used in textiles and analytical applications. Its resistance to degradation makes it difficult to remove using conventional treatment methods, which are often expensive and inefficient. This study investigates the use of acid treated gasification char (GC), a byproduct of biomass gasification, as a low-cost adsorbent for EBT removal. Process conditions were optimized using response surface methodology (RSM), while artificial neural networks (ANN) were employed for predictive modeling. GC was characterized using FTIR, SEM, and BET surface area analysis. The adsorption results showed strong agreement with both models (R<sup>2</sup> = 0.9999 for RSM and R<sup>2</sup> = 0.9887 for ANN). Among the isotherms evaluated, the nonlinear n-BET model provided the best fit (R<sup>2</sup> = 0.9889), indicating multilayer adsorption on a heterogeneous surface. These findings highlight the potential of GC as an effective waste derived adsorbent for dye removal and support the suitability of the n-BET model in describing complex adsorption behavior.</p> Graphical abstract <p></p>

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Adsorptive removal of Eriochrome Black T using gasified woodchips: experimental and neural network analysis

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年9月11日

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  • N. F. Fauzi,
  • A. A. Ahmad,
  • M. A. Ahmad

摘要

Eriochrome Black T (EBT) is a stable and toxic azo dye widely used in textiles and analytical applications. Its resistance to degradation makes it difficult to remove using conventional treatment methods, which are often expensive and inefficient. This study investigates the use of acid treated gasification char (GC), a byproduct of biomass gasification, as a low-cost adsorbent for EBT removal. Process conditions were optimized using response surface methodology (RSM), while artificial neural networks (ANN) were employed for predictive modeling. GC was characterized using FTIR, SEM, and BET surface area analysis. The adsorption results showed strong agreement with both models (R2 = 0.9999 for RSM and R2 = 0.9887 for ANN). Among the isotherms evaluated, the nonlinear n-BET model provided the best fit (R2 = 0.9889), indicating multilayer adsorption on a heterogeneous surface. These findings highlight the potential of GC as an effective waste derived adsorbent for dye removal and support the suitability of the n-BET model in describing complex adsorption behavior.

Graphical abstract