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An influent generator for WRRF design and operation based on a recurrent neural network with multi-objective

Feiyi Li1, Peter A Vanrolleghem1

  • 1modelEAU, Université Laval, 1065, Avenue de la Médecine, Québec, QC G1 V 0A6, Canada E-mail: feiyi.li.1@ulaval.ca; CentrEau, Québec Water Research Center, 1065 avenue de la Médecine, Québec, QC G1 V 0A6, Canada.

Water Science and Technology : a Journal of the International Association on Water Pollution Research
|March 15, 2022
PubMed
Summary
This summary is machine-generated.

New influent generator models for Water Resource Recovery Facilities (WRRF) use a multi-objective approach to better capture real-world influent variability. This improves simulations for WRRF design and operation.

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Area of Science:

  • Environmental Engineering
  • Wastewater Treatment Technologies
  • Computational Fluid Dynamics

Background:

  • Water Resource Recovery Facilities (WRRF) utilize modeling, automation, and control for upgrading and optimization.
  • Influent generator (IG) models are crucial for providing time-series data for dynamic WRRF simulations.
  • Existing IG models often lack representativeness of temporal variability due to single-criterion calibration.

Purpose of the Study:

  • To develop a data-driven influent generator model that accurately captures temporal variability.
  • To enhance the representativeness of influent time series for WRRF design and operation.
  • To improve the evaluation of WRRF performance under dynamic conditions.

Main Methods:

  • Development of a data-driven IG model using Long Short-Term Memory (LSTM) recurrent neural networks.
  • Optimization of the LSTM model using a multi-objective genetic algorithm focusing on mean percentage error and variability.
  • Extension of the model with a random walk process to further enhance temporal variability.

Main Results:

  • The developed LSTM-based IG model demonstrates improved representativeness of influent time series probability distributions.
  • The multi-objective optimization successfully balances accuracy and variability capture.
  • The addition of a random walk process further enhances the approximation of true influent variability.

Conclusions:

  • The novel multi-objective IG model provides a more realistic influent description for WRRF simulations.
  • This approach is vital for optimizing WRRF operations, especially in managing peak loads and evaluating control systems.
  • The enhanced IG model contributes to more robust WRRF design and improved operational strategies.