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Physical, Chemical and Biological Characterization of Six Biochars Produced for the Remediation of Contaminated Sites

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Published on: November 28, 2014

Discovery of an End-to-End Pattern for Contaminant-Oriented Advanced Oxidation Processes Catalyzed by Biochar with

Rupeng Wang¹, Honglin Chen¹, Zixiang He¹

¹State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin, Heilongjiang Province 150090, China.

Environmental Science & Technology

|August 20, 2024

Summary

This study introduces a hierarchical machine learning pipeline for optimizing biochar-catalyzed peroxymonosulfate advanced oxidation processes (BC-PMS AOPs) for specific emerging contaminants. The approach enables contaminant-oriented degradation optimization, bridging ML development and environmental application.

Keywords:

advanced oxidation processes contaminant-oriented degradation emerging contaminant end-to-end pattern machine learning

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Published on: January 7, 2019

Area of Science:

Environmental Chemistry
Water Treatment Technologies
Machine Learning Applications

Background:

Biochar-catalyzed peroxymonosulfate advanced oxidation processes (BC-PMS AOPs) are effective for emerging contaminant (EC) mitigation.
Machine learning (ML) has been used for predicting reaction rates in BC-PMS AOPs, but contaminant-specific optimization is lacking.

Purpose of the Study:

To develop a hierarchical ML pipeline for end-to-end (E2E) contaminant-oriented optimization of BC-PMS AOPs.
To enable specific degradation strategies for diverse ECs using ML.

Main Methods:

Developed an XGB model for predicting EC degradation reaction constants in BC-PMS AOPs.
Created EC-specific submodels based on HOMO-LUMO gaps for targeted optimization.
Implemented a hierarchical ML pipeline for an E2E operating pattern.

Main Results:

The comprehensive XGB model accurately predicted reaction constants for various ECs.
Submodels provided tailored optimization strategies for specific ECs based on their electronic properties.
The E2E pipeline demonstrated effective contaminant-oriented optimization of BC-PMS AOPs.

Conclusions:

The hierarchical ML pipeline successfully bridges the gap between ML model development and practical environmental applications in AOPs.
This study advances the understanding and application of contaminant-oriented optimization for BC-PMS AOPs.
The developed approach facilitates targeted mitigation of emerging contaminants through advanced oxidation processes.