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    Deciphering the Effect of Sulfide Derivatives on the Prediction of Nitrite Accumulation in Sulfide-Dosed Partial Nitrification Using Machine Learning

    Source: Journal of Environmental Engineering:;2025:;Volume ( 151 ):;issue: 007::page 04025033-1
    Author:
    Musavir Rafiq
    ,
    Magray Owaes
    ,
    Khalid Muzamil Gani
    ,
    Sheena Kumari
    ,
    Mohammed Seyam
    ,
    Faizal Bux
    DOI: 10.1061/JOEEDU.EEENG-8057
    Publisher: American Society of Civil Engineers
    Abstract: The unstable nature of partial nitrification (PN) has made it difficult to achieve stable nitrite production without robust process control. Sulfide has been reported to be a potential mediator for the achievement of PN by creating an inhibiting environment for nitrite-oxidizing bacteria (NOB). In the field sulfide salt, sodium sulfide (Na2S) increases pH due to the production of hydroxide ions that can inhibit NOBs. Because of the complexity in metabolic pathways and the presence of sulfide in ionized and unionized forms, conventional first principles models have limitations in providing accurate predictions. This study demonstrated a comparative analysis of three machine learning (ML) models to determine the most influential parameter for nitrite accumulation during sulfide addition. pH, HS−/N, H2S/N were selected as input parameters. The data from the lab-scale experiments were used for training of ML algorithms, namely Gaussian process regression (GPR), support vector machines (SVM), and ensemble regression tree (ER). The results showed GPR to be a better performer in prediction highlighting its advantage over other ML models with R2=0.95, RMSE=0.19 and MAE=0.14, and ionized form of sulfide (HS−/N) was found to be the significant parameter for the successful nitrite accumulation. This study highlights the integration of ML techniques to predict nitrite accumulation ratio (NAR) in real-world wastewater treatment applications by demonstrating a practical and impactful approach to optimize biological nitrogen removal processes, addressing both operational challenges and environmental concerns effectively.
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      Deciphering the Effect of Sulfide Derivatives on the Prediction of Nitrite Accumulation in Sulfide-Dosed Partial Nitrification Using Machine Learning

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4307803
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    contributor authorMusavir Rafiq
    contributor authorMagray Owaes
    contributor authorKhalid Muzamil Gani
    contributor authorSheena Kumari
    contributor authorMohammed Seyam
    contributor authorFaizal Bux
    date accessioned2025-08-17T23:01:51Z
    date available2025-08-17T23:01:51Z
    date copyright7/1/2025 12:00:00 AM
    date issued2025
    identifier otherJOEEDU.EEENG-8057.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4307803
    description abstractThe unstable nature of partial nitrification (PN) has made it difficult to achieve stable nitrite production without robust process control. Sulfide has been reported to be a potential mediator for the achievement of PN by creating an inhibiting environment for nitrite-oxidizing bacteria (NOB). In the field sulfide salt, sodium sulfide (Na2S) increases pH due to the production of hydroxide ions that can inhibit NOBs. Because of the complexity in metabolic pathways and the presence of sulfide in ionized and unionized forms, conventional first principles models have limitations in providing accurate predictions. This study demonstrated a comparative analysis of three machine learning (ML) models to determine the most influential parameter for nitrite accumulation during sulfide addition. pH, HS−/N, H2S/N were selected as input parameters. The data from the lab-scale experiments were used for training of ML algorithms, namely Gaussian process regression (GPR), support vector machines (SVM), and ensemble regression tree (ER). The results showed GPR to be a better performer in prediction highlighting its advantage over other ML models with R2=0.95, RMSE=0.19 and MAE=0.14, and ionized form of sulfide (HS−/N) was found to be the significant parameter for the successful nitrite accumulation. This study highlights the integration of ML techniques to predict nitrite accumulation ratio (NAR) in real-world wastewater treatment applications by demonstrating a practical and impactful approach to optimize biological nitrogen removal processes, addressing both operational challenges and environmental concerns effectively.
    publisherAmerican Society of Civil Engineers
    titleDeciphering the Effect of Sulfide Derivatives on the Prediction of Nitrite Accumulation in Sulfide-Dosed Partial Nitrification Using Machine Learning
    typeJournal Article
    journal volume151
    journal issue7
    journal titleJournal of Environmental Engineering
    identifier doi10.1061/JOEEDU.EEENG-8057
    journal fristpage04025033-1
    journal lastpage04025033-12
    page12
    treeJournal of Environmental Engineering:;2025:;Volume ( 151 ):;issue: 007
    contenttypeFulltext
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