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Published on: February 7, 2017
Deciphering peroxydisulfate vs. peroxymonosulfate activation by biochar catalysts via explainable machine learning
Xiaochong Wang1, Wei Zhang2, Ruobing Cheng2
1Key Laboratory of Environmental Risk Assessment and Control on Chemical Process, School of Resources and Environmental Engineering, East China University of Science and Technology, Shanghai 200237, China; Laboratory for Industrial Water and Ecotechnology (LIWET), Department of Green Chemistry and Technology, Ghent University Campus Kortrijk, Sint-Martens-Latemlaan 2B, Kortrijk B-8500, Belgium.
Machine learning models reveal distinct activation mechanisms for peroxymonosulfate (PMS) and peroxydisulfate (PDS) using biochar (BC) catalysts. This research guides the design of efficient biochar for water contaminant removal via persulfate advanced oxidation processes (PS-AOPs).
Area of Science:
- Environmental Chemistry
- Materials Science
- Water Treatment Technologies
Background:
- Biochar (BC) activated persulfate advanced oxidation processes (PS-AOPs), using peroxymonosulfate (PMS) and peroxydisulfate (PDS), are effective for removing water contaminants.
- A lack of systematic comparison between PMS and PDS activation by biochar hinders catalyst design and process optimization.
Purpose of the Study:
- To systematically compare the catalytic behavior and pollutant removal performance of PMS and PDS activated by non-metal-doped biochar.
- To utilize machine learning (ML) to investigate the distinct activation mechanisms and identify key biochar properties and experimental conditions for optimizing PS-AOPs.
Main Methods:
- Development of distinct ML models for PMS and PDS activation by non-metal-doped biochar.
- Application of SHAP analysis to determine feature importance for both oxidants.
- Classification of contaminants by degradability using ionization potential and development of contaminant-directed sub-models.
Main Results:
- PMS activation is primarily influenced by experimental conditions (71.3% importance), while PDS activation is more dependent on biochar properties (70.1% importance).
- Key biochar properties (physico-porous structure, surface elements, preparation) and their optimal ranges were identified for each oxidant.
- Contaminant degradability was classified, enabling targeted mechanistic analysis and optimization strategies.
Conclusions:
- ML analysis provides mechanistic insights into PMS and PDS activation by biochar, highlighting distinct influencing factors.
- Optimal biochar design strategies require tuning specific parameters like biomass precursor, surface area, and defect degree based on the oxidant-contaminant combination.
- This study offers guidance for developing efficient, sustainable, and tailored biochar catalysts for pollutant-specific water treatment.
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