Data-driven machine learning improves prediction of sulfonamide antibiotic adsorption by biochar in aqueous phase

Mudi Zhai; Bomin Fu; Zhaozhong Wu; Junsen Wang; Weijie Wang; Hongtao Wang

doi:10.1016/j.biortech.2025.132773

Data-driven machine learning improves prediction of sulfonamide antibiotic adsorption by biochar in aqueous phase

Mudi Zhai, Bomin Fu^*, Zhaozhong Wu, Junsen Wang, Weijie Wang, Hongtao Wang^*

^*Corresponding author for this work

Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review

8 Citations (Scopus)

Abstract

Sulfonamide antibiotics (SAs) have attracted much attention due to their environmental risks to aquatic ecosystems. Biochars (BCs), as excellent adsorbent materials, have been used to remove SAs from aqueous phases. To achieve effective evaluation of adsorption, machine learning (ML) strategies are increasingly being developed. However, no applicable data-driven ML models have been studied to predict the adsorption of SAs by BCs in water. Therefore, this study employed an ML approach based on Wasserstein generative adversarial network (WGAN) data augmentation to predict the adsorption of SAs on BCs in the aqueous phase. The results indicated that the WGAN could generate virtual data highly similar to the original adsorption dataset. By expanding the original data using WGAN, the performance of the extreme gradient boosting model in predicting the adsorption amount improved. This study provides new insights into predicting the adsorption behavior of waste-based BCs for SAs in water environments.

© 2025 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

Original language	English
Article number	132773
Journal	Bioresource Technology
Volume	434
Online published	4 Jun 2025
DOIs	https://doi.org/10.1016/j.biortech.2025.132773
Publication status	Published - Oct 2025

Funding

This study was supported by the National Natural Science Foundation of China (No. 42407061 and 52270188), the Macau Young Scholars Program (No. AM2021006), and the Visiting Fellowship Scheme of State Key Laboratory of Marine Pollution which receives regular research funding from Innovation and Technology Commission (ITC) of the Hong Kong SAR Government.

Research Keywords

Wastewater
Data augmentation
Wasserstein generative adversarial network
Generative artificial intelligence

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10.1016/j.biortech.2025.132773

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title = "Data-driven machine learning improves prediction of sulfonamide antibiotic adsorption by biochar in aqueous phase",

abstract = "Sulfonamide antibiotics (SAs) have attracted much attention due to their environmental risks to aquatic ecosystems. Biochars (BCs), as excellent adsorbent materials, have been used to remove SAs from aqueous phases. To achieve effective evaluation of adsorption, machine learning (ML) strategies are increasingly being developed. However, no applicable data-driven ML models have been studied to predict the adsorption of SAs by BCs in water. Therefore, this study employed an ML approach based on Wasserstein generative adversarial network (WGAN) data augmentation to predict the adsorption of SAs on BCs in the aqueous phase. The results indicated that the WGAN could generate virtual data highly similar to the original adsorption dataset. By expanding the original data using WGAN, the performance of the extreme gradient boosting model in predicting the adsorption amount improved. This study provides new insights into predicting the adsorption behavior of waste-based BCs for SAs in water environments.{\textcopyright} 2025 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.",

keywords = "Wastewater, Data augmentation, Wasserstein generative adversarial network, Generative artificial intelligence",

author = "Mudi Zhai and Bomin Fu and Zhaozhong Wu and Junsen Wang and Weijie Wang and Hongtao Wang",

year = "2025",

month = oct,

doi = "10.1016/j.biortech.2025.132773",

language = "English",

volume = "434",

journal = "Bioresource Technology",

issn = "0960-8524",

publisher = "Elsevier Ltd.",

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TY - JOUR

T1 - Data-driven machine learning improves prediction of sulfonamide antibiotic adsorption by biochar in aqueous phase

AU - Zhai, Mudi

AU - Fu, Bomin

AU - Wu, Zhaozhong

AU - Wang, Junsen

AU - Wang, Weijie

AU - Wang, Hongtao

PY - 2025/10

Y1 - 2025/10

N2 - Sulfonamide antibiotics (SAs) have attracted much attention due to their environmental risks to aquatic ecosystems. Biochars (BCs), as excellent adsorbent materials, have been used to remove SAs from aqueous phases. To achieve effective evaluation of adsorption, machine learning (ML) strategies are increasingly being developed. However, no applicable data-driven ML models have been studied to predict the adsorption of SAs by BCs in water. Therefore, this study employed an ML approach based on Wasserstein generative adversarial network (WGAN) data augmentation to predict the adsorption of SAs on BCs in the aqueous phase. The results indicated that the WGAN could generate virtual data highly similar to the original adsorption dataset. By expanding the original data using WGAN, the performance of the extreme gradient boosting model in predicting the adsorption amount improved. This study provides new insights into predicting the adsorption behavior of waste-based BCs for SAs in water environments.© 2025 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

AB - Sulfonamide antibiotics (SAs) have attracted much attention due to their environmental risks to aquatic ecosystems. Biochars (BCs), as excellent adsorbent materials, have been used to remove SAs from aqueous phases. To achieve effective evaluation of adsorption, machine learning (ML) strategies are increasingly being developed. However, no applicable data-driven ML models have been studied to predict the adsorption of SAs by BCs in water. Therefore, this study employed an ML approach based on Wasserstein generative adversarial network (WGAN) data augmentation to predict the adsorption of SAs on BCs in the aqueous phase. The results indicated that the WGAN could generate virtual data highly similar to the original adsorption dataset. By expanding the original data using WGAN, the performance of the extreme gradient boosting model in predicting the adsorption amount improved. This study provides new insights into predicting the adsorption behavior of waste-based BCs for SAs in water environments.© 2025 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

KW - Wastewater

KW - Data augmentation

KW - Wasserstein generative adversarial network

KW - Generative artificial intelligence

UR - https://www.webofscience.com/wos/woscc/full-record/WOS:001509146900002

U2 - 10.1016/j.biortech.2025.132773

DO - 10.1016/j.biortech.2025.132773

M3 - RGC 21 - Publication in refereed journal

SN - 0960-8524

VL - 434

JO - Bioresource Technology

JF - Bioresource Technology

M1 - 132773

ER -

Data-driven machine learning improves prediction of sulfonamide antibiotic adsorption by biochar in aqueous phase

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