A predictive model for determining the nitrite concentration in the effluent of an anammox reactor using ensemble regression tree algorithm

Abstract

Anaerobic ammonium oxidation (anammox) is a cost-effective biological nitrogen removal method for treating wastewater. Nitrite has strong negative effect on microbial activity of anammox bacteria, while the conventional equitment available for determining nitrite on-line is challenging due to high price. By knowing the concentration of nitrite in the effluent, its concentration in the reactor can be controlled accordingly. To investigate this, an ensemble regression tree algorithm was used to establish the predictive model proposed in the current work. Moreover, the Bayesian algorithm was adopted to systematically optimize various parameters of machine learning algorithms. The predicted concentrations of nitrite were in good agreement with the observed values, and the coefficient of determination (R2) and root mean squared error (RMSE) values reached 0.91 and 4.81, respectively. Furthermore, the model established by the ensemble regression tree algorithm was compared with models established by commonly used machine learning algorithms. Finally, the established models were applied to another anammox reactor, and the predicted results of ensemble regression tree model were found to be in good agreement with the experimental values with R2 and RMSE values of 0.84 and 6.34, respectively.