1
|
Kumari S, Chowdhry J, Kumar M, Garg MC. Machine learning (ML): An emerging tool to access the production and application of biochar in the treatment of contaminated water and wastewater. GROUNDWATER FOR SUSTAINABLE DEVELOPMENT 2024; 26:101243. [DOI: 10.1016/j.gsd.2024.101243] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/20/2024]
|
2
|
Liu Z, Shi X, Yan Z, Sun Z. Synergistic activation of peroxymonosulfate by 3D CoNiO 2/Co core-shell structure biochar catalyst for sulfamethoxazole degradation. BIORESOURCE TECHNOLOGY 2024; 406:130983. [PMID: 38880266 DOI: 10.1016/j.biortech.2024.130983] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/26/2024] [Revised: 06/08/2024] [Accepted: 06/14/2024] [Indexed: 06/18/2024]
Abstract
In this study, a 3D CoNiO2/Co core-shell structure biochar catalyst derived from walnut shell was synthesized by hydrothermal and ion etching methods. The prepared BC@CoNi-600 catalyst exhibited exceptional peroxymonosulfate (PMS) activation. The system achieved 100 % degradation of sulfamethoxazole (SMX). The reactive oxygen species in the BC@CoNi-600/PMS system included SO4-, OH, and O2-. Density functional theory calculations explored the synergistic effects between nickel-cobalt bimetallic and carbon matrix during PMS activation. The unique 3D core-shell structure of BC@CoNi-600 features an outer nickel-cobalt bimetallic layer with exceptional PMS adsorption capacity, while protecting the zero-valence Co of the inner layer from oxidation. Based on the experimental-data, machine learning modeling mechanism, and information theory, a nonlinear modeling method was proposed. This study utilizes a machine learning approach to investigate the degradation of SMX in complex aquatic environments. This study synthesized a novel biochar-based catalyst for activated PMS and provided unique insights into its environmental applications.
Collapse
Affiliation(s)
- Zhibin Liu
- Department of Environmental Engineering, Beijing University of Technology, Beijing 100124, PR China; National Engineering Laboratory for Advanced Municipal Wastewater Treatment and Reuse Technology, Beijing University of Technology, Beijing 100124, PR China
| | - Xuelin Shi
- Department of Environmental Engineering, Beijing University of Technology, Beijing 100124, PR China; National Engineering Laboratory for Advanced Municipal Wastewater Treatment and Reuse Technology, Beijing University of Technology, Beijing 100124, PR China
| | - Zihao Yan
- Department of Environmental Engineering, Beijing University of Technology, Beijing 100124, PR China; National Engineering Laboratory for Advanced Municipal Wastewater Treatment and Reuse Technology, Beijing University of Technology, Beijing 100124, PR China
| | - Zhirong Sun
- Department of Environmental Engineering, Beijing University of Technology, Beijing 100124, PR China; National Engineering Laboratory for Advanced Municipal Wastewater Treatment and Reuse Technology, Beijing University of Technology, Beijing 100124, PR China.
| |
Collapse
|
3
|
Wang N, Yang W, Wang B, Bai X, Wang X, Xu Q. Predicting maturity and identifying key factors in organic waste composting using machine learning models. BIORESOURCE TECHNOLOGY 2024; 400:130663. [PMID: 38583671 DOI: 10.1016/j.biortech.2024.130663] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/02/2024] [Revised: 03/15/2024] [Accepted: 04/04/2024] [Indexed: 04/09/2024]
Abstract
The measurement of germination index (GI) in composting is a time-consuming and laborious process. This study employed four machine learning (ML) models, namely Random Forest (RF), Artificial Neural Network (ANN), Support Vector Regression (SVR), and Decision Tree (DT), to predict GI based on key composting parameters. The prediction results showed that the coefficient of determination (R2) for RF (>0.9) and ANN (>0.9) was higher than SVR (<0.6) and DT (<0.8), suggesting that RF and ANN displayed superior predictive performance for GI. The SHapley additive exPlanations value result indicated that composting time, temperature, and pH were the important features contributing to GI. Composting time was found to have the most significant impact on GI. Overall, RF and ANN were suggested as effective tools for predicting GI in composting. This study offers the reliable approach of accurately predicting GI in composting processes, thereby enabling intelligent composting practices.
Collapse
Affiliation(s)
- Ning Wang
- Shenzhen Engineering Laboratory for Eco-efficient Recycled Materials, School of Environment and Energy, Peking University, Shenzhen Graduate School, University Town, Xili, Nanshan District, Shenzhen 518055, China
| | - Wanli Yang
- Shenzhen Engineering Laboratory for Eco-efficient Recycled Materials, School of Environment and Energy, Peking University, Shenzhen Graduate School, University Town, Xili, Nanshan District, Shenzhen 518055, China
| | - Bingshu Wang
- School of Software, Northwestern Polytechnical University, Xi'an 710129, China
| | - Xinyue Bai
- Shenzhen Engineering Laboratory for Eco-efficient Recycled Materials, School of Environment and Energy, Peking University, Shenzhen Graduate School, University Town, Xili, Nanshan District, Shenzhen 518055, China
| | - Xinwei Wang
- School of Advanced Materials, Peking University Shenzhen Graduate School, Shenzhen 518055, China
| | - Qiyong Xu
- Shenzhen Engineering Laboratory for Eco-efficient Recycled Materials, School of Environment and Energy, Peking University, Shenzhen Graduate School, University Town, Xili, Nanshan District, Shenzhen 518055, China.
| |
Collapse
|