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Implementing an Extended Kalman Filter for estimating nutrient composition in a sequential batch MBBR pilot plant.

Abhilash M Nair¹, Abaynesh Fanta², Finn Aakre Haugen³

¹Faculty of Science and Technology, Norwegian University of Life Sciences, P.O. Box 5003, 1432 Aas, Norway

Water Science and Technology : a Journal of the International Association on Water Pollution Research

|September 21, 2019

Summary

This study develops a cost-effective soft-sensor using an Extended Kalman Filter (EKF) to estimate key water quality parameters like volatile fatty acids (VFA) and nutrient levels in wastewater. The EKF successfully monitors wastewater treatment processes, offering a flexible alternative to expensive physical sensors.

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Area of Science:

Environmental Engineering
Water Treatment Technologies
Process Control

Background:

Online monitoring of wastewater quality is crucial for efficient treatment plant operations.
Limitations of physical sensors include high cost, maintenance needs, and limited availability.
Soft-sensors offer a cost-effective and flexible alternative for real-time water quality estimation.

Purpose of the Study:

To develop, tune, implement, and validate an Extended Kalman Filter (EKF) based soft-sensor.
To estimate concentrations of volatile fatty acids (VFA), soluble phosphates (PO4-P), ammonia nitrogen (NH4-N), and nitrate nitrogen (NO3-N).
To utilize inexpensive sensors like pH and dissolved oxygen (DO) for monitoring.

Main Methods:

Development of a grey-box model by fitting pilot plant kinetic data to a reduced ASM2d model.
Implementation of the Extended Kalman Filter (EKF) on the grey-box model.
Validation of the EKF soft-sensor against standard laboratory measurements in a sequential batch moving bed biofilm reactor (MBBR) pilot unit.

Main Results:

The EKF successfully estimated VFA, PO4-P, NH4-N, and NO3-N concentrations.
The soft-sensor provided accurate estimations during continuous operation of the MBBR pilot plant.
The developed soft-sensor demonstrated reliable performance validated by laboratory measurements.

Conclusions:

The Extended Kalman Filter (EKF) on a grey-box model provides a viable and cost-effective solution for online water quality monitoring.
Soft-sensors can effectively replace or supplement traditional online sensors in wastewater treatment.
This approach enhances process control and operational efficiency in wastewater treatment plants.