PREDICTION OF NITROGEN REMOVAL PERFORMANCE AND IDENTIFICATION OF KEY PARAMETERS OF PARTIAL NITRIFICATION/PARTIAL DENITRIFICATION-ANAMMOX PROCESS BASED ON MACHINE LEARNING

WU Yulun; LI Zemin; CHENG Xiaoqian; QIU Guanglei; WEI Chaohai

doi:10.13205/j.hjgc.202409017

Volume 42 Issue 9

Sep. 2024

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Article Navigation > ENVIRONMENTAL ENGINEERING > 2024 > 42(9): 180-190

WU Yulun, LI Zemin, CHENG Xiaoqian, QIU Guanglei, WEI Chaohai. PREDICTION OF NITROGEN REMOVAL PERFORMANCE AND IDENTIFICATION OF KEY PARAMETERS OF PARTIAL NITRIFICATION/PARTIAL DENITRIFICATION-ANAMMOX PROCESS BASED ON MACHINE LEARNING[J]. ENVIRONMENTAL ENGINEERING , 2024, 42(9): 180-190. doi: 10.13205/j.hjgc.202409017

Citation:

WU Yulun, LI Zemin, CHENG Xiaoqian, QIU Guanglei, WEI Chaohai. PREDICTION OF NITROGEN REMOVAL PERFORMANCE AND IDENTIFICATION OF KEY PARAMETERS OF PARTIAL NITRIFICATION/PARTIAL DENITRIFICATION-ANAMMOX PROCESS BASED ON MACHINE LEARNING[J]. ENVIRONMENTAL ENGINEERING , 2024, 42(9): 180-190. doi: 10.13205/j.hjgc.202409017

Citation:

WU Yulun, LI Zemin, CHENG Xiaoqian, QIU Guanglei, WEI Chaohai. PREDICTION OF NITROGEN REMOVAL PERFORMANCE AND IDENTIFICATION OF KEY PARAMETERS OF PARTIAL NITRIFICATION/PARTIAL DENITRIFICATION-ANAMMOX PROCESS BASED ON MACHINE LEARNING[J]. ENVIRONMENTAL ENGINEERING , 2024, 42(9): 180-190. doi: 10.13205/j.hjgc.202409017

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PREDICTION OF NITROGEN REMOVAL PERFORMANCE AND IDENTIFICATION OF KEY PARAMETERS OF PARTIAL NITRIFICATION/PARTIAL DENITRIFICATION-ANAMMOX PROCESS BASED ON MACHINE LEARNING

doi: 10.13205/j.hjgc.202409017

1. School of Environment and Energy, South China University of Technology, Guangzhou 510006, China;
2. School of Environment, South China Normal University, Guangzhou 510006, China

Received Date: 2024-07-23
Available Online: 2024-12-02

Abstract

Abstract

The nitrogen removal performance of the partial nitrification-Anammox (PNA) and partial denitrification-Anammox (PDA) processes are affected by many parameters. Predicting the performance of the two processes and identifying the key parameters based on a comprehensive consideration of various parameters can provide an optimization target for their practical engineering applications. When solving the above problems, experimental methods are usually time-consuming and labor-intensive, while traditional mathematical models are difficult to deal with non-linear relationships. Therefore, in this study, machine learning techniques were used. The constructed Random Forest (RF) machine learning model predicted the effluent nitrogen (TN) concentration of the two processes with high accuracy, and the coefficient of determination (R²) of the PNA and the PDA processes were 0.728 and 0.812, respectively. The SHAP method explained the prediction process of the model well and ranked the importance of each parameter. In the PNA process, the effluent TN concentration was mainly influenced by the influent TN concentration and COD concentration; in the PDA process, the effluent TN concentration was firstly constrained by the influent TN concentration and nitrogen load. On this basis, influent COD concentration is another important factor that affects the effluent TN concentration of the PDA process. The common importance of the influent COD concentration in both processes indicated that both processes should be managed and allocated to the carbon source in the wastewater well in advance of practical application. It is of significant importance to consider the pre-separation and application strategies. The machine learning model used in this study can provide methodological guidance for the prediction of the nitrogen removal performance of the PNA and PDA process. The SHAP-based model interpretation can provide a foundation for the identification and optimization of key parameters for the two processes in practical application.
- partial nitrification-Anammox,
- partial denitrification Anammox,
- machine learning,
- SHAP analysis,
- process parameters

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References

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