Formal verification of wastewater treatment processes using events detected from continuous signals by means of artificial neural networks. Case study: SBR plant

Luca Luccarini, Gianni Luigi Bragadin, Gabriele Colombini, Maurizio Mancini, Paola Mello, Marco Montali, Davide Sottara

Research output: Contribution to journalArticle

35 Scopus citations

Abstract

This paper proposes a modular architecture for the analysis and the validation of wastewater treatment processes. An algorithm using neural networks is used to extract the relevant qualitative patterns, such as "apexes", "knees" and "steps", from the signals acquired in the reaction tanks. These patterns, which show changes in the signals trend, are mapped to events in the process and logged using an appropriate XML format. The logs, in turn, are considered traces of the execution of a manufacturing process and validated using tools commonly applied for the Verification of Business Processes. The system has been applied to the data collected from a Sequencing Batch Reactor (SBR) for municipal wastewater treatment, equipped with probes for the on-line acquisition of signals such as pH, oxidation--reduction potential (ORP) and dissolved oxygen (DO). A SBR has turned out to be a suitable case study since the commonly acknowledged criteria for monitoring the biological processes (nitrification and denitrification) can be expressed in the form or qualitative constraints, which are easily translated into formal rules. The process logs, hence, are matched against these rules, which act as filters and quality classifiers.

Original languageEnglish (US)
Pages (from-to)648-660
Number of pages13
JournalEnvironmental Modelling and Software
Volume25
Issue number5
DOIs
StatePublished - May 1 2010

Keywords

  • Artificial neural networks
  • Business process management
  • Event detection
  • Intelligent systems
  • Rule-based management system
  • SBR

ASJC Scopus subject areas

  • Software
  • Environmental Engineering
  • Ecological Modeling

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