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<p>This study integrates the 3Rs waste management strategy by utilizing lignin extracted from wheat straw as an eco-friendly adsorbent for wastewater treatment. Characterization of lignin revealed a grain size of 33.7&#xa0;µm and a surface area of 2.808 m<sup>2</sup>/g. A hybrid modeling approach combining the Taguchi and artificial neural network (ANN) models was used to optimize dye removal efficiency from textile wastewater. The Taguchi design explored combinations of key parameters (pH, dye concentration, dose, time, and temperature), achieving a maximum removal efficiency of 92.83%. The Taguchi results were validated by an ANN model (<i>R</i><sup>2</sup> = 1) with 13 neurons, demonstrating high predictive accuracy. Adsorption mechanisms were further evaluated using isotherm and kinetic models, where the pseudo-second-order model (<i>R</i><sup>2</sup> = 0.98) demonstrated the adsorption process. The thermodynamic analysis (Δ<i>H</i>° = 2.990 kJ/mol, Δ<i>S</i>° = 0.0314 J/K/mol, Δ<i>G</i>° = − 6593.2 kJ/mol) confirmed favorable conditions for dye removal. Regeneration tests using H<sub>2</sub>O and 0.1 M NaOH showed desorption efficiencies of 78% and 48%, respectively. These findings demonstrate that wheat straw lignin is a promising, low-cost, and sustainable material for synthetic dye remediation, though further validation with real wastewater is necessary.</p>

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Optimization of Textile Dye Removal with Wheat Straw Extracted Lignin Using Taguchi-Artificial Neural Networking Approach

  • 期刊论文

  • 发表日期：

    2026年3月18日

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  • Javeria Javed,
  • Kiran Aftab,
  • Zumaira Siddique,
  • Nighat Javed,
  • Faiz Ahmed

摘要

This study integrates the 3Rs waste management strategy by utilizing lignin extracted from wheat straw as an eco-friendly adsorbent for wastewater treatment. Characterization of lignin revealed a grain size of 33.7 µm and a surface area of 2.808 m2/g. A hybrid modeling approach combining the Taguchi and artificial neural network (ANN) models was used to optimize dye removal efficiency from textile wastewater. The Taguchi design explored combinations of key parameters (pH, dye concentration, dose, time, and temperature), achieving a maximum removal efficiency of 92.83%. The Taguchi results were validated by an ANN model (R2 = 1) with 13 neurons, demonstrating high predictive accuracy. Adsorption mechanisms were further evaluated using isotherm and kinetic models, where the pseudo-second-order model (R2 = 0.98) demonstrated the adsorption process. The thermodynamic analysis (ΔH° = 2.990 kJ/mol, ΔS° = 0.0314 J/K/mol, ΔG° = − 6593.2 kJ/mol) confirmed favorable conditions for dye removal. Regeneration tests using H2O and 0.1 M NaOH showed desorption efficiencies of 78% and 48%, respectively. These findings demonstrate that wheat straw lignin is a promising, low-cost, and sustainable material for synthetic dye remediation, though further validation with real wastewater is necessary.