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

作者：Jinqi Jiang, Qingshan Lin, Xiaohong Guan, Shuai Zhou, Shifa Zhong, Xiang Xiang, Zongping Wang, Guanghao Chen, Gang Guo · 发表于：ACS ES&T Engineering · 年份：2025 · DOI：10.1021/acsestengg.4c00830 · 被引用次数：9 · 研究领域：Artificial Intelligence in Healthcare

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 (SVR), and CatBoost to forecast VFA production by integrating initial feedstock properties, electrochemical pretreatment conditions, and fermentation parameters. CatBoost demonstrated the highest R 2 of 0.977 and the lowest root-mean-square error (RMSE) at 95.69 mg COD/L. Key environmental factors, including fermentation days (VFA production reaching 90% by day 5), salinity (0.5–1.0 g/L), and the carbon-to-nitrogen (C/N) ratio (16.53–22), were identified as optimal for VFA production. To enhance long-term monitoring and facilitate early warning systems, process indicators (pH, ORP, PNs, SCOD, and PSs) from the last day were used to predict VFA production on the following day by fine-tuning the generative pretrain transformer (GPT), with the gpt-3.5-turbo-0125 model exhibiting the highest R 2 of 0.837 ± 0.004 and lowest RMSE of 296.98 ± 3.65 mg COD/L. Local sensitivity analysis revealed that SCOD was the most important process factor affect...