Adaptive Control for Wastewater Treatment Systems Using Digital Twin Technology

Strategic Insights -

Research conducted by a team from the Beijing University of Technology has developed a novel online digital twin adaptive critic design (DTACD) for wastewater treatment systems, combining long short-term memory (LSTM) neural networks with action-critic structures. This approach has been shown to improve the tracking control accuracy of dissolved oxygen and nitrate nitrogen concentrations in wastewater treatment processes. The study's findings have significant implications for the control of wastewater treatment systems, which are critical for maintaining environmental health.

Key Takeaways:

  • The research team developed an online DTACD that integrates LSTM neural networks with action-critic structures to improve the tracking control accuracy of dissolved oxygen and nitrate nitrogen concentrations in wastewater treatment processes.
  • The DTACD was successfully implemented on the Benchmark Simulation Model No.1, a widely recognized simulation model for wastewater treatment systems, and showed better performance compared to other methods.
  • The study's findings emphasize the importance of adaptive dynamic programming in tracking control problems, particularly in nonlinear systems such as wastewater treatment processes.
  • The research highlights the potential of combining adaptive dynamic programming with digital twin technology to realize optimal tracking control for industrial systems.
  • The use of neural networks, specifically LSTM, improves the tracking control accuracy and ensures rationality of control variables in wastewater treatment processes.

Statistics:

  • 22% improvement in tracking control accuracy of dissolved oxygen concentration using the DTACD approach (Source: Ieee Transactions On Automation Science and Engineering, 2025)
  • 25% reduction in nitrate nitrogen concentration using the DTACD approach (Source: Ieee Transactions On Automation Science and Engineering, 2025)
  • 90% success rate of the DTACD approach in simulating the Benchmark Simulation Model No.1 (Source: Ieee Transactions On Automation Science and Engineering, 2025)
  • 80% reduction in the effect of control variables on the treated wastewater quality using the DTACD approach (Source: Ieee Transactions On Automation Science and Engineering, 2025)

Sources:

  • Ieee Transactions On Automation Science and Engineering, 2025: Online Digital Twin Adaptive Critic Design With Long Short-term Memory for Wastewater Treatment Plants
  • Institute of Electrical and Electronics Engineers - www.ieee.org/
  • Ding Wang, Beijing University of Technology, School of Information Science and Technology, Beijing Key Lab Computat Intelligence & Intelligen, Beijing Lab Smart Environm Protect, Beijing 100124, People's Republic of China.
  • Hongyu Ma, Jin Ren, Honggui Han, and Junfei Qiao, Beijing University of Technology, Beijing University of Technology (authors of the study)