A fascinating exploration into nitrite accumulation into low concentration reactors using cutting-edge machine learning techniques.

In the last couple decades, more use of nitrogenous chemical fertilizers and improper disposable of wastewater has harmed water and it cause water pollution. Low concentrated Nitrite (NO 2) is the one of hazardous pollution and it is difficult to remove through biological processes, while it occurs in low concentration. Many technologies have been developed to accumulate NO 2 in the mainstream. However, most of them use chemical inhibitors for nitrite oxidizing bacteria (NOB). In past studies high concentrated reactor performance have been modeled using mathematical models. In this study, machine learning application (MLA) was applied to model the performance of reactors. The reactor was low concentrated, continuous stirred tank reactor (CSTR) with in-fluent total ammonia nitrogen (TAN) concentration was (∼30PPM-TAN and ∼50PPM-TAN) and lower output TAN concentration was (∼1PPM-TAN). However, 216 days of water treatment data from CSTR were used, and the CSTR's efficiency (%) of nitrite accumulation was estimated using in-fluent and effluent quantities. Then efficiency is predicted with 70 % of the data that is used to train the algorithms. Confusion matrix was used to access the performance of algorithms and actual and predicted classes (efficiencies) were compared. The DTC and XGB over-performed other algorithms. [Display omitted] • A novel machine learning application was applied to model the performance of reactors. • More use of nitrogenous chemical fertilizers has harmed groundwater and soil composition. • DTC and XGB are the most accurate, followed by RF and LR, while KNN is the least accurate. [ABSTRACT FROM AUTHOR]