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

Area of Science:

• Environmental Microbiology
• Wastewater Treatment Engineering
• Computational Biology

Background:

• Anaerobic ammonium oxidation (anammox) is a key biological process for nitrogen removal in wastewater.
• Nitrite accumulation negatively impacts anammox bacteria activity.
• Current online nitrite monitoring equipment is expensive and challenging to implement.

Purpose of the Study:

• To develop a cost-effective and accurate method for on-line nitrite concentration prediction in anammox reactors.
• To optimize machine learning algorithms for predicting nitrite levels.
• To validate the predictive model's performance in real-world wastewater treatment scenarios.

Main Methods:

• Ensemble regression tree algorithm for predictive modeling.
• Bayesian algorithm for systematic optimization of machine learning parameters.
• Validation against experimental data using coefficient of determination (R²) and root mean squared error (RMSE).

Main Results:

• The ensemble regression tree model accurately predicted nitrite concentrations (R² = 0.91, RMSE = 4.81).
• The model demonstrated good performance when applied to a different anammox reactor (R² = 0.84, RMSE = 6.34).
• The developed model showed superior performance compared to other commonly used machine learning algorithms.

Conclusions:

• Machine learning, specifically ensemble regression trees, offers a viable and cost-effective solution for on-line nitrite monitoring in anammox processes.
• Accurate nitrite prediction enables better control of anammox reactor conditions, enhancing nitrogen removal efficiency.
• The developed predictive model has practical applications in optimizing wastewater treatment plant operations.