ANN-based Internal Model Control strategy applied in the WWTP industry

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3 Citations (Scopus)

Abstract

Wastewater Treatment Plants (WWTPs) are industries where highly complex and non-linear processes are performed to reduce the pollutant concentrations of residual waters. However, some nitrogen and phosphorus derived pollutants are generated in these processes. As a consequence, certain control strategies have been developed to maintain these pollutants under certain limits. Benchmark Simulation Model No.1 (BSM1), a framework emulating the behaviour of a general purpose WWTP, considers a default controller strategy based on Proportional Integral (PI) controllers. Nevertheless, these controllers are based on linearised models of the WWTP behaviour. For that reason, this work proposes a new control approach based on Internal Model Controllers (IMC) adopting Artificial Neural Networks (ANNs), which are able to model the real plant behaviour without performing linearisation. Results show that the proposed IMC is improving the default controller performance around a 16% and a 53% in terms of the Integral Absolute Error (IAE) and the Integral Square Error (ISE), respectively.

Original languageEnglish
Pages (from-to)1477-1480
Number of pages4
JournalIEEE International Conference on Emerging Technologies and Factory Automation, ETFA
DOIs
Publication statusPublished - Sep 2019

Keywords

  • Artificial Neural Networks
  • BSM1
  • Internal Model Controller
  • Wastewater Treatment Plants

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