Deciphering organic substrate impacts in Anammox systems: A machine learning driven framework for predictive classification and process mechanism analysis
The response mechanisms of anaerobic ammonium oxidation (Anammox) systems to organic compounds have been extensively characterized, yet the synergistic impacts of organic classification and concentration gradients remain debated due to inconsistencies in operation conditions and microbial cultivatio...
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Elsevier
2025-08-01
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| Series: | Environment International |
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| author | Zemin Li Yulun Wu Tao Chen Bo Yan Chaohai Wei |
| author_facet | Zemin Li Yulun Wu Tao Chen Bo Yan Chaohai Wei |
| author_sort | Zemin Li |
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| description | The response mechanisms of anaerobic ammonium oxidation (Anammox) systems to organic compounds have been extensively characterized, yet the synergistic impacts of organic classification and concentration gradients remain debated due to inconsistencies in operation conditions and microbial cultivation environments. This study investigates the critical factors governing nitrogen removal efficiency in Anammox processes, with particular emphasis on the combined effects of influent organic concentration and organic matter characteristics. Three datasets were constructed based on organic types: biodegradable organic compounds, biorefractory organic compounds and combined two types organic compounds. Two machine learning models were employed to predict Anammox performance, with Random Forest (RF) identified as the optimal model, subsequently validated using real coking industry wastewater treatment data. SHapley Additive exPlanations (SHAP) analysis revealed differential regulatory dominance across systems: organic concentration, influent ammonium nitrogen (NH4+-N) manifaested as the primary governing factors in biodegradable system, whereas organic type emerged as the most critical factors in biorefractory and combined systems. The results demonstrate that evaluating organic impacts solely through concentration levels is oversimplified, necessitating concurrent consideration of both organic type and concentration. Mechanistic interpretation through molecular-level inhibition analysis further validates the scientific rationale for employing Biochemical Oxygen Demand/Chemical oxygen demand (BOD/COD) ratio as a comprehensive indicator of carbon source effects on Anammox systems. In summary, machine learning framework effectively integrates and optimizes the regulation of material stoichiometry, environmental parameters, and microbial functionality, thereby advancing the development of energy-efficient nitrogen removal technologies and enhancing the evaluation system for wastewater treatment processes. |
| format | Article |
| id | doaj-art-9cde81d80b8b4555a39d3902480c3412 |
| institution | Kabale University |
| issn | 0160-4120 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Elsevier |
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| spelling | doaj-art-9cde81d80b8b4555a39d3902480c34122025-08-20T03:28:36ZengElsevierEnvironment International0160-41202025-08-0120210963710.1016/j.envint.2025.109637Deciphering organic substrate impacts in Anammox systems: A machine learning driven framework for predictive classification and process mechanism analysisZemin Li0Yulun Wu1Tao Chen2Bo Yan3Chaohai Wei4School of Environment, South China Normal University, Guangzhou, Guangdong 510006, PR China; SCNU Environmental Research Institute, Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety & MOE Key Laboratory of Theoretical Chemistry of Environment, South China Normal University, Guangzhou 510006, PR ChinaSchool of Environment and Energy, South China University of Technology, Guangzhou 510006, PR China; Central & Southern China Municipal Engineering Design and Research Institute Co, Ltd, Wuhan 430000, PR ChinaSchool of Environment, South China Normal University, Guangzhou, Guangdong 510006, PR China; SCNU Environmental Research Institute, Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety & MOE Key Laboratory of Theoretical Chemistry of Environment, South China Normal University, Guangzhou 510006, PR ChinaSchool of Environment, South China Normal University, Guangzhou, Guangdong 510006, PR China; SCNU Environmental Research Institute, Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety & MOE Key Laboratory of Theoretical Chemistry of Environment, South China Normal University, Guangzhou 510006, PR China; Corresponding author at: School of Environment, South China Normal University, Guangzhou, Guangdong 510006, PR China.School of Environment and Energy, South China University of Technology, Guangzhou 510006, PR China; Corresponding author.The response mechanisms of anaerobic ammonium oxidation (Anammox) systems to organic compounds have been extensively characterized, yet the synergistic impacts of organic classification and concentration gradients remain debated due to inconsistencies in operation conditions and microbial cultivation environments. This study investigates the critical factors governing nitrogen removal efficiency in Anammox processes, with particular emphasis on the combined effects of influent organic concentration and organic matter characteristics. Three datasets were constructed based on organic types: biodegradable organic compounds, biorefractory organic compounds and combined two types organic compounds. Two machine learning models were employed to predict Anammox performance, with Random Forest (RF) identified as the optimal model, subsequently validated using real coking industry wastewater treatment data. SHapley Additive exPlanations (SHAP) analysis revealed differential regulatory dominance across systems: organic concentration, influent ammonium nitrogen (NH4+-N) manifaested as the primary governing factors in biodegradable system, whereas organic type emerged as the most critical factors in biorefractory and combined systems. The results demonstrate that evaluating organic impacts solely through concentration levels is oversimplified, necessitating concurrent consideration of both organic type and concentration. Mechanistic interpretation through molecular-level inhibition analysis further validates the scientific rationale for employing Biochemical Oxygen Demand/Chemical oxygen demand (BOD/COD) ratio as a comprehensive indicator of carbon source effects on Anammox systems. In summary, machine learning framework effectively integrates and optimizes the regulation of material stoichiometry, environmental parameters, and microbial functionality, thereby advancing the development of energy-efficient nitrogen removal technologies and enhancing the evaluation system for wastewater treatment processes.http://www.sciencedirect.com/science/article/pii/S0160412025003885Machine learning modelData analyticsAnammox processNitrogen removalIndustrial wastewater treatment |
| spellingShingle | Zemin Li Yulun Wu Tao Chen Bo Yan Chaohai Wei Deciphering organic substrate impacts in Anammox systems: A machine learning driven framework for predictive classification and process mechanism analysis Environment International Machine learning model Data analytics Anammox process Nitrogen removal Industrial wastewater treatment |
| title | Deciphering organic substrate impacts in Anammox systems: A machine learning driven framework for predictive classification and process mechanism analysis |
| title_full | Deciphering organic substrate impacts in Anammox systems: A machine learning driven framework for predictive classification and process mechanism analysis |
| title_fullStr | Deciphering organic substrate impacts in Anammox systems: A machine learning driven framework for predictive classification and process mechanism analysis |
| title_full_unstemmed | Deciphering organic substrate impacts in Anammox systems: A machine learning driven framework for predictive classification and process mechanism analysis |
| title_short | Deciphering organic substrate impacts in Anammox systems: A machine learning driven framework for predictive classification and process mechanism analysis |
| title_sort | deciphering organic substrate impacts in anammox systems a machine learning driven framework for predictive classification and process mechanism analysis |
| topic | Machine learning model Data analytics Anammox process Nitrogen removal Industrial wastewater treatment |
| url | http://www.sciencedirect.com/science/article/pii/S0160412025003885 |
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