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Advancing the Comprehension of Iron Ions with Different Valences in Anammox-Based Processes: Insight into the Response and Mechanism Based on Prediction and Classification Machine Learning Models
https://orcid.org/0009-0003-1068-1145
https://orcid.org/0000-0002-2031-5427
The responses of anammox to Fe2+ and Fe3+ have been widely discussed; however, the critical concentration of Fe2+ and Fe3+ is not certain due to the different culture environments. A reliable and widely applicable predictive classification system based on the anammox-based system with iron-containing wastewater needs to be established, which can surmount the independence between different experiments and be combined with statistical analysis to elucidate the mechanism of the effect of Fe2+ and Fe3+ based on critical concentrations. The results confirmed that 5 mg/L iron promoted the nitrogen removal process, while higher concentrations inhibited nitrogen removal with a ratio of above 80%. Moreover, the results of Spearman correlation analysis proved that Fe2+ showed a more obvious effect on the nitrogen removal rate than Fe3+. To precisely predict the nitrogen removal performance of anammox, Support Vector Machine, XGBoost, and Random Forest (RF) were compared, and the RF model was confirmed as the preferable model (R 2 = 0.99). According to the classification model, the influence of iron ions on the anammox performance could be successfully traced back to the valent states of iron with an accuracy of 97.7%. Furthermore, a mechanism of the nitrogen removal process in the anammox-based system under iron ion stress was proposed.
This study reveals the response mechanism of anammox-based system to different valence iron ions and establishes a reliable machine learning model for prediction and classification.
Advancing the Comprehension of Iron Ions with Different Valences in Anammox-Based Processes: Insight into the Response and Mechanism Based on Prediction and Classification Machine Learning Models
https://orcid.org/0009-0003-1068-1145
https://orcid.org/0000-0002-2031-5427
The responses of anammox to Fe2+ and Fe3+ have been widely discussed; however, the critical concentration of Fe2+ and Fe3+ is not certain due to the different culture environments. A reliable and widely applicable predictive classification system based on the anammox-based system with iron-containing wastewater needs to be established, which can surmount the independence between different experiments and be combined with statistical analysis to elucidate the mechanism of the effect of Fe2+ and Fe3+ based on critical concentrations. The results confirmed that 5 mg/L iron promoted the nitrogen removal process, while higher concentrations inhibited nitrogen removal with a ratio of above 80%. Moreover, the results of Spearman correlation analysis proved that Fe2+ showed a more obvious effect on the nitrogen removal rate than Fe3+. To precisely predict the nitrogen removal performance of anammox, Support Vector Machine, XGBoost, and Random Forest (RF) were compared, and the RF model was confirmed as the preferable model (R 2 = 0.99). According to the classification model, the influence of iron ions on the anammox performance could be successfully traced back to the valent states of iron with an accuracy of 97.7%. Furthermore, a mechanism of the nitrogen removal process in the anammox-based system under iron ion stress was proposed.
This study reveals the response mechanism of anammox-based system to different valence iron ions and establishes a reliable machine learning model for prediction and classification.
Advancing the Comprehension of Iron Ions with Different Valences in Anammox-Based Processes: Insight into the Response and Mechanism Based on Prediction and Classification Machine Learning Models
https://orcid.org/0009-0003-1068-1145
https://orcid.org/0000-0002-2031-5427
The responses of anammox to Fe2+ and Fe3+ have been widely discussed; however, the critical concentration of Fe2+ and Fe3+ is not certain due to the different culture environments. A reliable and widely applicable predictive classification system based on the anammox-based system with iron-containing wastewater needs to be established, which can surmount the independence between different experiments and be combined with statistical analysis to elucidate the mechanism of the effect of Fe2+ and Fe3+ based on critical concentrations. The results confirmed that 5 mg/L iron promoted the nitrogen removal process, while higher concentrations inhibited nitrogen removal with a ratio of above 80%. Moreover, the results of Spearman correlation analysis proved that Fe2+ showed a more obvious effect on the nitrogen removal rate than Fe3+. To precisely predict the nitrogen removal performance of anammox, Support Vector Machine, XGBoost, and Random Forest (RF) were compared, and the RF model was confirmed as the preferable model (R 2 = 0.99). According to the classification model, the influence of iron ions on the anammox performance could be successfully traced back to the valent states of iron with an accuracy of 97.7%. Furthermore, a mechanism of the nitrogen removal process in the anammox-based system under iron ion stress was proposed.
This study reveals the response mechanism of anammox-based system to different valence iron ions and establishes a reliable machine learning model for prediction and classification.
Advancing the Comprehension of Iron Ions with Different Valences in Anammox-Based Processes: Insight into the Response and Mechanism Based on Prediction and Classification Machine Learning Models
Jiang, Zhicheng (author) / Xu, Xinxin (author) / He, Yuhang (author) / Zeng, Ming (author) / Zhang, Meng (author) / Liu, Wei (author) / Wu, Nan (author)
ACS ES&T Water ; 3 ; 3716-3727
2023-11-10
Article (Journal)
Electronic Resource
English
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