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<p>Water pollution caused by dye-containing wastewater represents a major global environmental challenge. While various green synthesis approaches have been explored for the removal of Methylene Blue (MB), there remains a critical need to enhance their efficiency through proper process optimization and the exploration of novel eco-friendly materials. In this study, <i>Cascabela thevetia</i> leaves (CTL) were investigated as a low-cost, sustainable biosorbent for MB dye removal from wastewater. CTL powder was characterized using Fourier Transform Infrared (FTIR) spectroscopy, Scanning Electron Microscopy (SEM), and X-ray Diffraction (XRD). To optimize adsorption efficiency, the study employed both machine learning (ML) techniques and response surface methodology (RSM) to evaluate the effects of key variables, including solution pH, contact time, and initial dye concentration. The optimal conditions identified were: 0.1&#xa0;g of adsorbent, 10&#xa0;mg/L dye concentration, 50&#xa0;min contact time, and pH 11. The adsorption process was best described by the Freundlich isotherm model (R<sup>2</sup> = 0.997), and kinetic analysis indicated that a pseudo-second-order model accurately represented the adsorption behavior. Thermodynamic parameters (ΔG°, ΔH°, ΔS°) confirmed the process to be spontaneous and exothermic. Notably, the CTL powder demonstrated excellent recyclability with no significant loss in adsorption capacity over multiple cycles. This is the first study to integrate machine learning optimization with conventional adsorption modeling for MB dye removal using CTL powder. This approach not only improves prediction accuracy but also advances the development of cost-effective, sustainable solutions for dye-contaminated wastewater treatment. Moreover, the study addresses existing research gaps at the intersection of adsorption science and data-driven process optimization, setting the stage for future innovations in environmental remediation.</p>

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Response surface methodology and machine learning-based optimization of methylene blue adsorption on cascabela thevetia leaves powder

  • 期刊论文

  • 发表日期：

    2026年1月23日

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  • U. Bashir,
  • N. S. Zain,
  • M. A. Khan,
  • N. Akhtar,
  • M. I. Khan,
  • M. H. Lashari,
  • A. Shanableh,
  • R. Luque

摘要

Water pollution caused by dye-containing wastewater represents a major global environmental challenge. While various green synthesis approaches have been explored for the removal of Methylene Blue (MB), there remains a critical need to enhance their efficiency through proper process optimization and the exploration of novel eco-friendly materials. In this study, Cascabela thevetia leaves (CTL) were investigated as a low-cost, sustainable biosorbent for MB dye removal from wastewater. CTL powder was characterized using Fourier Transform Infrared (FTIR) spectroscopy, Scanning Electron Microscopy (SEM), and X-ray Diffraction (XRD). To optimize adsorption efficiency, the study employed both machine learning (ML) techniques and response surface methodology (RSM) to evaluate the effects of key variables, including solution pH, contact time, and initial dye concentration. The optimal conditions identified were: 0.1 g of adsorbent, 10 mg/L dye concentration, 50 min contact time, and pH 11. The adsorption process was best described by the Freundlich isotherm model (R2 = 0.997), and kinetic analysis indicated that a pseudo-second-order model accurately represented the adsorption behavior. Thermodynamic parameters (ΔG°, ΔH°, ΔS°) confirmed the process to be spontaneous and exothermic. Notably, the CTL powder demonstrated excellent recyclability with no significant loss in adsorption capacity over multiple cycles. This is the first study to integrate machine learning optimization with conventional adsorption modeling for MB dye removal using CTL powder. This approach not only improves prediction accuracy but also advances the development of cost-effective, sustainable solutions for dye-contaminated wastewater treatment. Moreover, the study addresses existing research gaps at the intersection of adsorption science and data-driven process optimization, setting the stage for future innovations in environmental remediation.