Forecasting influent flow rate and composition with occasional data for supervisory management system by time series...

Area of Science:

• Environmental Engineering
• Wastewater Treatment Technology
• Time Series Analysis

Background:

• Accurate influent load data is crucial for wastewater treatment plant (WWTP) modeling and control action evaluation.
• Limited availability of real-time influent data hinders effective WWTP management.
• Predictive modeling is essential for optimizing operational strategies and mitigating environmental impacts.

Purpose of the Study:

• To develop a reliable time series model for forecasting key wastewater influent parameters.
• To evaluate different modeling approaches for predicting flow rate, COD, NH4(+)-N, and PO4(3-)-P.
• To identify the most effective forecasting method for real-world WWTP applications.

Main Methods:

• Utilized 250 days of field operational data for model development and validation.
• Employed spline interpolation and time series modeling to handle missing data.
• Compared three forecasting methods: multi-step ahead (Method 1), multiple models (Method 2), and single-step ahead (Method 3).

Main Results:

• Method 3, a single-step-ahead forecasting model, demonstrated superior reliability.
• This approach effectively minimized accumulated errors inherent in multi-step forecasting.
• Coefficients were estimated simply, enhancing model usability.

Conclusions:

• The developed time series model, particularly Method 3, provides a reliable approach for influent load forecasting.
• Accurate influent prediction supports better control actions and operational efficiency in WWTPs.
• This research offers a practical tool for enhancing WWTP performance through data-driven modeling.