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Vinyl Chloride Degradation Using Ozone-Based Advanced Oxidation Processes: Bridging Groundwater Treatment and Machine

Jelena Molnar Jazić1, Marko Arsenović2, Tajana Simetić1

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Summary

Advanced oxidation processes (AOPs) effectively remove toxic vinyl chloride (VC) from groundwater. Combining ozone with hydrogen peroxide (peroxone process) and AI modeling significantly enhances VC degradation and predicts treatment outcomes for safe drinking water.

Keywords:
advanced machine learning modelsartificial intelligencegroundwater treatmentozone-based AOPsvinyl chloride

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Area of Science:

  • Environmental Chemistry
  • Water Treatment Technologies
  • Advanced Oxidation Processes (AOPs)

Background:

  • Water scarcity necessitates innovative treatment solutions for contaminants like vinyl chloride (VC).
  • VC is a toxic and carcinogenic groundwater contaminant, posing risks to potable water supplies.
  • Advanced Oxidation Processes (AOPs) show promise for effective contaminant removal.

Purpose of the Study:

  • To investigate vinyl chloride (VC) removal using ozone-based advanced oxidation processes (AOPs).
  • To integrate experimental data with data-driven modeling for predicting VC degradation.
  • To assess the impact of water matrix conditions on VC removal efficacy.

Main Methods:

  • Conducted ozonation and peroxone (O3/H2O2) experiments on contaminated groundwater samples.
  • Utilized advanced machine learning models, including ensemble methods (Random Forest, Neural Network), for prediction.
  • Trained models using water characteristics and treatment parameters as input features.

Main Results:

  • Ozonation alone partially removed VC; peroxone treatment achieved 70-97% degradation.
  • Peroxone or O3/H2O2/UV photodegradation was needed for VC levels below 0.5 µg/L in complex matrices.
  • An ensemble model (Random Forest + Neural Network) achieved high predictive accuracy (R² = 0.99).

Conclusions:

  • Ozone-based AOPs, particularly the peroxone process, are effective for VC removal from groundwater.
  • AI-driven ensemble models can accurately predict VC degradation, aiding treatment optimization.
  • The developed model offers a generalizable and interpretable tool for AOP treatment outcome prediction.