Identification of behavioural model input data sets for WWTP uncertainty analysis.

Area of Science:

• Environmental Engineering
• Water Resource Management
• Computational Modeling

Background:

• Uncertainty analysis is crucial for the reliable application of wastewater treatment plant (WWTP) models.
• Identifying and quantifying sources of uncertainty is a key challenge in WWTP modeling.

Purpose of the Study:

• To develop and evaluate a methodology for identifying an ensemble of behavioral WWTP model representations.
• To generate a multivariate conditional distribution of input data for uncertainty analysis.

Main Methods:

• Developed a methodology to identify behavioral model representations by combining input data, model structure, and parameter values.
• Utilized historical observations and actual plant data to verify uncertainty distributions of input data.
• Generated samples of likely inputs for WWTP model uncertainty analysis.

Main Results:

• Successfully identified an ensemble of behavioral model representations.
• Created a multivariate conditional distribution of input data for sampling likely inputs.
• Demonstrated an approach to verify input data uncertainty distributions using real-world plant data.

Conclusions:

• The proposed methodology enhances the reliability of WWTP model uncertainty analysis.
• Verifying input data distributions with historical data improves model accuracy.
• The approach facilitates more robust WWTP model applications and decision-making.