Using Machine Learning and GPT Models To Enhance Electrochemical Pretreatment of Anaerobic Cofermentation: Prediction, Early Warning, and Biomarker Identification

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作者信息关键词期刊信息基础信息归属信息摘要

成果类型：

期刊论文

作者：

Jiang, Jinqi (58682866800);Lin, Qingshan (57402558000);Guan, Xiaohong (35236983900);Zhou, Shuai (56374497900);Zhong, Shifa (55872651400);...

通讯作者：

Gang Guo

作者机构：

[Jiang, Jinqi (58682866800); Guo, Gang (49663137200); Wang, Zongping (24765770300); Guo, G] Sch Environm Sci & Engn, Hubei Key Lab Multimedia Pollut Cooperat Control Y, Wuhan 430074, Peoples R China.

[Jiang, Jinqi (58682866800); Xiang, Xiang (57214019389)] Huazhong Univ Sci & Technol, Inst Artificial Intelligence, Wuhan 430074, Peoples R China.

[Lin, Qingshan (57402558000)] Chongqing Univ Arts & Sci, Chongqing Key Lab Resource Utilizat Heavy Met Wast, Yongchuan 402160, Peoples R China.

[Lin, Qingshan (57402558000)] Chongqing Univ, Sch Energy & Power Engn, Key Lab Low Grade Energy Utilizat Technol & Syst, Minist Educ, Chongqing 400044, Peoples R China.

[Guan, Xiaohong (35236983900); Zhong, Shifa (55872651400)] East China Normal Univ, Inst Ecochongming, Shanghai Engn Res Ctr Biotransformat Organ Solid W, Sch Ecol & Environm Sci, Shanghai 200241, Peoples R China.

通讯机构：

[Guo, G ] S

Sch Environm Sci & Engn, Hubei Key Lab Multimedia Pollut Cooperat Control Y, Wuhan 430074, Peoples R China.

语种：

英文

关键词：

machine learning;generative pretrain transformer;waste activated sludge;food waste;anaerobic cofermentation

期刊：

ACS ES&T Engineering

ISSN：

2690-0645

年：

2025

卷：

期：

页码：

1149–1159

DOI：

10.1021/acsestengg.4c00830

基金类别：

National Natural Science Foundation of China [52470035]; National Natural Science Foundation of China [2023YFC3207404]; National Key Research and Development Program of China [0026/2022/A1]; Science and Technology Development Fund, Macao S.A.R (FDCT) [R2023HH04]; Scientific Research Foundation of Chongqing University of Arts and Sciences

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本校为其他机构

院系归属：

土木工程学院

摘要：

Electrochemical enhancing anaerobic cofermentation of waste activated sludge and food waste to produce volatile fatty acids (VFAs) represents an innovative and promising approach. Despite its potential, optimizing system performance, providing early warnings, and identifying biomarkers remain challenging tasks due to the intricate interplay of numerous environmental variables and unclear dynamics of microbial interactions. This study first employed machine learning (ML) models including XGBoost, random forest (RF), support vector regression (SV...

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Using Machine Learning and GPT Models To Enhance Electrochemical Pretreatment of Anaerobic Cofermentation: Prediction, Early Warning, and Biomarker Identification

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