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<p>The current work accentuates the fabrication and advanced characterization of sewage sludge biochar/ MXene (SSB/MXene) composite via a solvent-free ball-milling methodology for the sustainable remediation of Rhodamine B (RhB)-laden aqueous solution. SSB/MXene adsorbent exhibited an utmost monolayered Langmuir adsorption capacity (q<sub>max</sub>) of 14.95&#xa0;mg&#xa0;g<sup>−1</sup>, at RhB concentrations of 15&#xa0;mg L<sup>−1</sup>, with a maximum RhB removal efficacy of 99.89%. RhB batch adsorption experiments signified that dye decolorization follows pseudo-second-order kinetics, designating chemisorption, while the Langmuir adsorption isotherm confirmed homogeneous mono-layered adsorption. The RhB batch adsorption experimental results were modelled via an Artificial Neural Network (ANN) modelling-based soft computing approach employing the Levenberg–Marquardt (LM) Backpropagation Algorithm, optimized by Genetic Algorithm. The designed ANN model exemplified a strong relationship between the empirical and ANN-prognosticated adsorption capacities, ascribable to higher determination, and correlation coefficients (R<sup>2</sup>: 0.9916, R: 0.9954), as well as minimal mean square error (0.3534), mean absolute error (0.2385), and root mean square error (0.4883), demonstrating robust predictiveness towards multi-variate adsorption, thereby enabling reliable AI-driven process forecasting. This work thus establishes SSB/MXene composite as a high-performance, sustainable adsorbent, while the ANN framework imparts a scalable, data-intelligent pathway for next-generation wastewater remediation.</p> Graphical Abstract <p></p>

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Experimental Investigation and Genetic Algorithm Optimization-Assisted ANN Modelling of Rhodamine B Adsorption Onto Biochar/MXene Composite

  • 期刊论文

  • 发表日期：

    2026年3月30日

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  • Neelaambhigai Mayilswamy,
  • Balasubramanian Kandasubramanian,
  • Anish Gore,
  • Rishikesh Sonawane,
  • Bhavana V P

摘要

The current work accentuates the fabrication and advanced characterization of sewage sludge biochar/ MXene (SSB/MXene) composite via a solvent-free ball-milling methodology for the sustainable remediation of Rhodamine B (RhB)-laden aqueous solution. SSB/MXene adsorbent exhibited an utmost monolayered Langmuir adsorption capacity (qmax) of 14.95 mg g−1, at RhB concentrations of 15 mg L−1, with a maximum RhB removal efficacy of 99.89%. RhB batch adsorption experiments signified that dye decolorization follows pseudo-second-order kinetics, designating chemisorption, while the Langmuir adsorption isotherm confirmed homogeneous mono-layered adsorption. The RhB batch adsorption experimental results were modelled via an Artificial Neural Network (ANN) modelling-based soft computing approach employing the Levenberg–Marquardt (LM) Backpropagation Algorithm, optimized by Genetic Algorithm. The designed ANN model exemplified a strong relationship between the empirical and ANN-prognosticated adsorption capacities, ascribable to higher determination, and correlation coefficients (R2: 0.9916, R: 0.9954), as well as minimal mean square error (0.3534), mean absolute error (0.2385), and root mean square error (0.4883), demonstrating robust predictiveness towards multi-variate adsorption, thereby enabling reliable AI-driven process forecasting. This work thus establishes SSB/MXene composite as a high-performance, sustainable adsorbent, while the ANN framework imparts a scalable, data-intelligent pathway for next-generation wastewater remediation.

Graphical Abstract