Machine learning-assisted prediction and identification of key factors affecting nitrogen metabolism for aerobic granular sludge.

• Tác giả: Yingying He, Mengxian Hu, Runyao Huang, Huiping Li, Kailin Liu, Weihai Pang, Li Xie, Dianhai Yang, Haosheng Yang, Jianhua Yuan, Baiqin Zhou
• Ngôn ngữ: eng
• Ký hiệu phân loại: 769.17 Techniques of reproduction
• Thông tin xuất bản: Netherlands : Environmental research , 2025
• Bộ sưu tập: NCBI
• ID: 702425

Tóm tắt

To achieve higher denitrification efficiency with reduced energy consumption in aerobic granular sludge (AGS) system, a systematic evaluation of the carbon and nitrogen metabolism process for AGS under different stage is essential. Herein, this study established the prediction models via interpretable machine learning (ML) for simulating the nitrogen metabolism by using 312 sets of data collected from four reactors with different kinds of AGS. The results indicated Gradient Boosting Decision Trees (GBDT) achieved R