Superior decomposition of xenobiotic RB5 dye using three-dimensional electrochemical treatment: Response surface methodology modelling, artificial intelligence, and machine learning-based optimisation approaches
Article
Ganthavee, Voravich and Trzcinski, Antoine P.. 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), pp. 1-10. https://doi.org/10.1016/j.wse.2024.05.003
| Article Title | Superior decomposition of xenobiotic RB5 dye using three-dimensional electrochemical treatment: Response surface methodology modelling, artificial intelligence, and machine learning-based optimisation approaches |
|---|---|
| Article Category | Article |
| Authors | Ganthavee, Voravich and Trzcinski, Antoine P. |
| Journal Title | Water Science and Engineering |
| Journal Citation | 18 (1), pp. 1-10 |
| Number of Pages | 10 |
| Year | 2025 |
| Publisher | Editorial Board of Water Science and Engineering |
| Elsevier | |
| Place of Publication | China |
| ISSN | 1674-2370 |
| 2405-8106 | |
| Digital Object Identifier (DOI) | https://doi.org/10.1016/j.wse.2024.05.003 |
| Web Address (URL) | https://www.sciencedirect.com/science/article/pii/S1674237024000516 |
| 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. |
| Keywords | Analysis of variance; Three-dimensional electrochemical treatment; Dye-polluted wastewater; Artificial intelligence; Machine learning; Optimisation; Error function analysis |
| Related Output | |
| Is part of | Superior decomposition of xenobiotic dyes and pharmaceutical contaminants in wastewater using response surface methodology, artificial intelligence and machine learning for optimisation of a novel three-dimensional electrochemical technology |
| Contains Sensitive Content | Does not contain sensitive content |
| ANZSRC Field of Research 2020 | 401102. Environmentally sustainable engineering |
| Public Notes | This article is part of a UniSQ Thesis by publication. See Related Output. |
| Byline Affiliations | School of Agriculture and Environmental Science |
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