A novel control strategy for enhancing biological N-removal in a granular sequencing batch reactor: A model-based study

Eduardo Isanta, Mónica Figueroa, Anuska Mosquera-Corral, Luis Campos, Julián Carrera, Julio Pérez

Research output: Contribution to journalArticlepeer-review

19 Scopus citations

Abstract

Biological nitrogen removal in aerobic granular sequencing batch reactors is sensitively affected by process conditions (e.g. dissolved oxygen (DO) concentration, nitrogen loading rate (NLR), influent C/N ratio, among others). The variation of one of these process conditions affects the others, because often they are tightly linked. These interrelationships are a drawback for the experimental assessment of the target domain of process conditions required to enhance N-removal. Here, we have developed a model to determine the guidelines to design an automatic control strategy with the final aim of enhancing biological N-removal in a granular sequencing batch reactor. The model was first calibrated with experimental data from a granular sequencing batch reactor treating swine wastewater. Specific simulations were designed to elucidate the effect of DO concentration (0.5-8mgO2L-1), granule size (0.5-3.5mm), influent C/N ratio (4-10gO2g-1N) and NLR (0.41-0.82gNL-1d-1) on the nitrification-denitrification efficiency. Simulation results showed that, in general, high N-removal efficiencies (from 70% to 85%) could be obtained only setting the appropriate DO concentration. That appropriate DO concentration could be easily found based on effluent ammonium concentration. Those results were used to propose a control strategy to enhance N-removal efficiencies. The control strategy was based on a closed DO loop with variable DO set-point. The DO set-point was established at a constant value for the whole cycle (i.e. once per cycle), based on the on-line measurement of ammonium concentration at the end of the previous cycle.

Original languageEnglish
Pages (from-to)468-477
Number of pages10
JournalChemical Engineering Journal
Volume232
DOIs
StatePublished - Oct 2013
Externally publishedYes

Keywords

  • Aerobic granular sludge
  • Dissolved oxygen concentration
  • Mathematical modeling
  • Nitrification-denitrification
  • On-line ammonium concentration
  • Particle size

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