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Quality evaluation methods for wastewater treatment plant data.

M Thomann1

  • 1Holinger AG, Galmsstrasse 4, CH-4410, Liestal, Switzerland. michael.thomann@holinger.com

Water Science and Technology : a Journal of the International Association on Water Pollution Research
|June 4, 2008
PubMed
Summary

This study introduces statistical methods to detect systematic errors in wastewater treatment plant data. These methods identify significant data errors, improving process control and decision-making.

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Area of Science:

  • Environmental Engineering
  • Data Science
  • Process Control

Background:

  • Unidentified systematic errors in wastewater treatment plant (WWTP) data can lead to critical failures in process control, modeling, and infrastructure planning.
  • Ensuring data accuracy is crucial for efficient and reliable operation of WWTPs.

Purpose of the Study:

  • To present statistical methods for identifying systematic errors in full-scale WWTP datasets.
  • To evaluate the reliability of on-line sensors used in WWTP monitoring.

Main Methods:

  • Utilized a redundant mass balance approach analyzing five distinct mass balances to detect systematic errors.
  • Employed Shewhart control charts for statistical and graphical analysis of on-line sensor data quality.

Main Results:

  • Identified systematic errors ranging from 10%-20% of input fluxes at a 5% significance level using the mass balance approach.
  • On-line analyzers for nitrate (NO3-), phosphate (PO4-), and ammonium (NH4-) in filter effluent, and Mixed Liquor Suspended Solids (MLSS) sensors in aeration tanks, showed no systematic errors for 85-95% of the 19-month monitoring period.
  • Control chart intervals indicated measurement variability: +/-12-17% for NO3-N, +/-35-40% for PO4-P, +/-83% for NH4-N, and +/-12-15% for TS.

Conclusions:

  • Statistical methods, including redundant mass balance and Shewhart control charts, are effective for identifying systematic errors in WWTP data.
  • Most on-line sensors monitored demonstrated reliable performance, though significant variability was noted for certain parameters like ammonium.
  • Accurate data is essential for informed decision-making in wastewater treatment operations and management.