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Voravich Ganthavee, Antoine P. Trzcinski. 2025: Superior decomposition of xenobiotic RB5 dye using three-dimensional electrochemical treatment: Response surface methodology modelling, artificial intelligence, and machine learning-based optimisation approaches. Water Science and Engineering, 18(1): 1-10. doi: 10.1016/j.wse.2024.05.003
Citation: Voravich Ganthavee, Antoine P. Trzcinski. 2025: Superior decomposition of xenobiotic RB5 dye using three-dimensional electrochemical treatment: Response surface methodology modelling, artificial intelligence, and machine learning-based optimisation approaches. Water Science and Engineering, 18(1): 1-10. doi: 10.1016/j.wse.2024.05.003
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Superior decomposition of xenobiotic RB5 dye using three-dimensional electrochemical treatment: Response surface methodology modelling, artificial intelligence, and machine learning-based optimisation approaches

doi: 10.1016/j.wse.2024.05.003

  • School of Agriculture and Environmental Science, University of Southern Queensland, Toowoomba, QLD 4350, Australia

  • Received Date: 2023-11-28
  • Accepted Date: 2024-05-13
    Abstract
  • The highly efficient electrochemical treatment technology for dye-polluted wastewater is one of hot research topics in industrial wastewater treatment. This study reported a three-dimensional electrochemical treatment process integrating graphite intercalation compound (GIC) adsorption, direct anodic oxidation, and ·OH oxidation for decolourising Reactive Black 5 (RB5) from aqueous solutions. The electrochemical process was optimised using the novel progressive central composite design-response surface methodology (CCD-NPRSM), hybrid artificial neural network-extreme gradient boosting (hybrid ANN-XGBoost), and classification and regression trees (CART). CCD-NPRSM and hybrid ANN-XGBoost were employed to minimise errors in evaluating the electrochemical process involving three manipulated operational parameters: current density, electrolysis (treatment) time, and initial dye concentration. The optimised decolourisation efficiencies were 99.30%, 96.63%, and 99.14% for CCD-NPRSM, hybrid ANN-XGBoost, and CART, respectively, compared to the 98.46% RB5 removal rate observed experimentally under optimum conditions: approximately 20 mA/cm2 of current density, 20 min of electrolysis time, and 65 mg/L of RB5. The optimised mineralisation efficiencies ranged between 89% and 92% for different models based on total organic carbon (TOC). Experimental studies confirmed that the predictive efficiency of optimised models ranked in the descending order of hybrid ANN-XGBoost, CCD-NPRSM, and CART. Model validation using analysis of variance (ANOVA) revealed that hybrid ANN-XGBoost had a mean squared error (MSE) and a coefficient of determination (R2) of approximately 0.014 and 0.998, respectively, for the RB5 removal efficiency, outperforming CCD-NPRSM with MSE and R2 of 0.518 and 0.998, respectively. Overall, the hybrid ANN-XGBoost approach is the most feasible technique for assessing the electrochemical treatment efficiency in RB5 dye wastewater decolourisation.

     

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