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Experimental design for evaluating WWTP data by linear mass balances.

Quan H Le1, Peter J T Verheijen2, Mark C M van Loosdrecht2

  • 1Department of Green Chemistry and Technology, Ghent University, Belgium.

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|June 17, 2018
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Summary
This summary is machine-generated.

A new experimental design procedure helps wastewater treatment plants (WWTPs) identify key process variables using mass balances. This method optimizes data collection for accurate and cost-effective monitoring.

Keywords:
Data reconciliationData validationExperimental designMass balancesWastewater treatment plant

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Area of Science:

  • Environmental Engineering
  • Process Systems Engineering

Background:

  • Wastewater treatment plants (WWTPs) require reliable data for effective operation and management.
  • Identifiability of key process variables is crucial for accurate process assessment but often hindered by limited measurements.
  • Linear mass balances provide constraints but may not be sufficient for full variable identifiability.

Purpose of the Study:

  • To develop a stepwise experimental design procedure for WWTPs to ensure reliable data acquisition.
  • To determine optimal sets of additional measurements that guarantee the identifiability of key process variables.
  • To balance the accuracy of identified variables with the cost of additional measurements.

Main Methods:

  • Developed a procedure to identify necessary additional measurements based on linear mass balance constraints.
  • Employed multi-objective optimization to find solutions balancing variable accuracy and measurement cost.
  • Utilized a Pareto-optimal front to represent trade-offs between accuracy and cost.
  • Applied the procedure to a full-scale WWTP, analyzing measurement interdependencies and mass balance groups.

Main Results:

  • Identified groups of overlapping mass balances, indicating which measurements are interdependent.
  • Demonstrated that adding measurements only benefits identifiability within specific mass balance groups.
  • Showcased significant reduction in computational effort by applying the procedure to individual mass balance groups.
  • Validated the straightforward applicability of the procedure to diverse WWTPs.

Conclusions:

  • The developed procedure effectively guarantees the identifiability of key process variables in WWTPs.
  • Optimizing experimental design based on mass balance analysis improves data reliability and cost-efficiency.
  • The method simplifies planning for monitoring campaigns and reduces computational load.
  • This approach is adaptable for various WWTPs, regardless of existing data availability.