Recurrent neural network-based control for wastewater treatment process

  • Authors:

  • Junfei Qiao;Xiaoqi Huang;Honggui Han

  • Affiliations:

  • College of Electronic and Control Engineering, Beijing University of Technology, Beijing, China;College of Electronic and Control Engineering, Beijing University of Technology, Beijing, China;College of Electronic and Control Engineering, Beijing University of Technology, Beijing, China

  • Venue:
  • ISNN'12 Proceedings of the 9th international conference on Advances in Neural Networks - Volume Part II
  • Year:
  • 2012

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Abstract

Wastewater treatment process (WWTP) is difficult to be controlled because of the complex dynamic behavior. In this paper, a multi-variable control system based on recurrent neural network (RNN) is proposed for controlling the dissolved oxygen (DO) concentration, nitrate nitrogen (SNO) concentration and mixed liquor suspended solids (MLSS) concentration in a WWTP. The proposed RNN can be self-adaptive to achieve control accuracy, hence the RNN-based controller is applied to the Benchmark Simulation Model No.1 (BSM1) WWTP to maintain the DO, SNO and MLSS concentrations in the expected value. The simulation results show that the proposed controller provides process control effectively. The performance, compared with PID and BP neural network, indicates that this control strategy yields the most accurate for DO, SNO, and MLSS concentrations and has lower integral of the absolute error (IAE), integral of the square error (ISE) and mean square error (MSE).