Monte Carlo Simulation, Artificial Intelligence and Machine Learning-based Modelling and Optimization of Three-dimensional Electrochemical Treatment of Xenobiotic Dye Wastewater
Article
Ganthavee, Voravich, Fernando, Merenghege M. R. and Trzcinski, Antoine P.. 2024. " Monte Carlo Simulation, Artificial Intelligence and Machine Learning-based Modelling and Optimization of Three-dimensional Electrochemical Treatment of Xenobiotic Dye Wastewater." Environmental Processes. 11 (3). https://doi.org/10.1007/s40710-024-00719-1
| Article Title | Monte Carlo Simulation, Artificial Intelligence and Machine Learning-based Modelling and Optimization of Three-dimensional Electrochemical Treatment of Xenobiotic Dye Wastewater |
|---|---|
| Article Category | Article |
| Authors | Ganthavee, Voravich, Fernando, Merenghege M. R. and Trzcinski, Antoine P. |
| Journal Title | Environmental Processes |
| Journal Citation | 11 (3) |
| Article Number | 41 |
| Number of Pages | 31 |
| Year | 2024 |
| Publisher | Springer |
| Place of Publication | Germany |
| ISSN | 2198-7491 |
| 2198-7505 | |
| Digital Object Identifier (DOI) | https://doi.org/10.1007/s40710-024-00719-1 |
| Web Address (URL) | https://link.springer.com/article/10.1007/s40710-024-00719-1 |
| Abstract | The present study investigates the synergistic performance of the three-dimensional electrochemical process to decolourise methyl orange (MO) dye pollutant from xenobiotic textile wastewater. The textile dye was treated using electrochemical technique with strong oxidizing potential, and additional adsorption technology was employed to effectively remove dye pollutants from wastewater. Approximately 98% of MO removal efficiency was achieved using 15 mA/cm2 of current density, 3.62 kWh/kg of energy consumption and 79.53% of current efficiency. The 50 mg/L MO pollutant was rapidly mineralized with a half-life of 4.66 min at a current density of 15 mA/cm2. Additionally, graphite intercalation compound (GIC) was electrically polarized in the three-dimensional electrochemical reactor to enhance the direct electrooxidation and.OH generation, thereby improving synergistic treatment efficiency. Decolourisation of MO-polluted wastewater was optimized by artificial intelligence (AI) and machine learning (ML) techniques such as Artificial Neural Networks (ANN), Support Vector Machine (SVM), and random forest (RF) algorithms. Statistical metrics indicated the superiority of the model followed this order: ANN > RF > SVM > Multiple regression. The optimization results of the process parameters by artificial neural network (ANN) and random forest (RF) approaches showed that a current density of 15 mA/cm2, electrolysis time of 30 min and initial MO concentration of 50 mg/L were the best operating parameters to maintain current and energy efficiencies of the electrochemical reactor. Finally, Monte Carlo simulations and sensitivity analysis showed that ANN yielded the best prediction efficiency with the lowest uncertainty and variability level, whereas the predictive outcome of random forest was slightly better. |
| Keywords | Adsorption and electrochemical treatment; Dye removal ; Artifcial neural network; Support vector machine; Random forest ; Monte Carlo simulation |
| Contains Sensitive Content | Does not contain sensitive content |
| ANZSRC Field of Research 2020 | 400410. Wastewater treatment processes |
| Byline Affiliations | School of Agriculture and Environmental Science |
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