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Automatic reactor model synthesis with genetic programming.

David J Dürrenmatt¹, Willi Gujer

¹Institute of Environmental Engineering, ETH Zurich, 8093 Zurich, Switzerland. david.duerrenmatt@eawag.ch

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

|January 27, 2012

Summary

This study introduces a new method using genetic programming to create accurate hydraulic models for wastewater treatment plants (WWTPs). This approach simplifies modeling, reducing reliance on costly experiments and expert experience.

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Area of Science:

Environmental Engineering
Computational Fluid Dynamics
Wastewater Treatment

Background:

Accurate hydraulic modeling is crucial for wastewater treatment plant (WWTP) process simulation.
Traditional methods like tracer experiments are often impractical due to cost and complexity.
Engineers frequently rely on experience, leading to potential inaccuracies.

Purpose of the Study:

To develop a novel, automated method for constructing hydraulic reactor models for WWTPs.
To overcome the limitations of traditional hydraulic modeling techniques.
To provide engineers with a data-driven approach for selecting suitable reactor models.

Main Methods:

Implementation of grammar-based genetic programming to generate hydraulic models.
Encoding hydraulic reactor models as program trees using building blocks like continuous stirred-tank reactors.
Utilizing only discharge measurements and influent/effluent concentrations as input data.

Main Results:

The genetic programming approach successfully generated comparable hydraulic models from synthetic data.
Temperature measurements from a primary clarifier were used to create a validated reactor model.
A virtual tracer experiment on the generated model showed good agreement with on-site data.

Conclusions:

The proposed method offers a practical and automated solution for hydraulic modeling in WWTPs.
It reduces the need for expensive tracer studies and subjective engineering experience.
The technique enables data-driven selection of the most appropriate reactor models for simulation.