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Transfer Learning in Wastewater Treatment Plant Control Design: From Conventional to Long Short-Term Memory-Based

Ivan Pisa1,2, Antoni Morell1, Ramón Vilanova2

  • 1Wireless Information Networking (WIN) Group, Escola d'Enginyeria, Universitat Autònoma de Barcelona, 08193 Bellaterra, Spain.

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Summary
This summary is machine-generated.

Transfer Learning (TL) simplifies designing Artificial Neural Networks (ANNs) for industrial control. This method enhances control performance in Wastewater Treatment Plants (WWTPs), reducing errors and oscillations.

Keywords:
WWTPcontrol designindustrial controltransfer learning

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Area of Science:

  • Industrial Automation and Control
  • Artificial Intelligence in Engineering
  • Environmental Engineering

Background:

  • Industrial control processes increasingly utilize Artificial Neural Networks (ANNs).
  • Designing and training ANNs can be complex and time-consuming.
  • Transfer Learning (TL) offers a method to reduce ANN development time by reusing knowledge.

Purpose of the Study:

  • To analyze the application of Transfer Learning (TL) methodologies for designing and implementing control loops in Wastewater Treatment Plants (WWTPs).
  • To evaluate the effectiveness of TL in developing control loops without extensive process knowledge.
  • To compare the control performance of TL-based methods against conventional control structures.

Main Methods:

  • Development and application of Transfer Learning (TL) methodologies for ANN control.
  • Implementation of TL to design control loops for a Wastewater Treatment Plant (WWTP).
  • Comparative analysis of TL-based control performance against conventional methods using Integrated Absolute Error (IAE) and Integrated Square Error (ISE).

Main Results:

  • TL-based methodology enables control loop development with reduced reliance on deep process knowledge.
  • Significant improvements in control performance were observed compared to conventional structures.
  • Reductions in oscillations during set-point tracking were achieved, with IAE improvements ranging from 40.17% to 94.29% and ISE improvements from 34.27% to 99.71%.

Conclusions:

  • Transfer Learning is an effective methodology for designing and implementing ANN-based control loops in industrial environments like WWTPs.
  • TL reduces the complexity and time required for ANN design, making advanced control more accessible.
  • The TL-based approach significantly enhances control performance, offering substantial error reduction and improved stability.