Aeration control based on a neural network in a biological aerated filter for simultaneous removal of ammonia and manganese

Research output: Contribution to journalArticle

Abstract

This study was conducted to monitor and control aeration by means of an online Neural Network (NN) of a Biological Aerated Filter (BAF). The BAF is an advanced drinking water treatment system equipped with Dissolved Oxygen (DO), oxidation-reduction potential, pH, ammonia and nitrate sensors. The main function of the BAF is to treat contaminated water by simultaneously reducing the levels of ammonia and manganese to below permit limits. Aeration was supplied to the BAF and controlled by a neural network. Real-time data was accurately predicted by the NN with errors below 5% for all sensors. The bending point was apparently created in DO neural network data when the simultaneous ammonia and manganese removals were below limits. The NN program detected the bending point and immediately stopped the aeration of the BAF. Hence, NN can optimize the aeration requirement and system performance, shorten time demand and reduce consumption of manpower and electricity.

Original languageEnglish
Pages (from-to)278-288
Number of pages11
JournalJournal of Environmental Science and Technology
Volume8
Issue number6
DOIs
Publication statusPublished - 19 Sep 2015

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aeration
manganese
ammonia
filter
dissolved oxygen
sensor
electricity
removal
nitrate
water

Keywords

  • Aeration
  • Biological aerated filter
  • Neural network
  • Real-time monitoring
  • Simultaneous ammonia and manganese removal

ASJC Scopus subject areas

  • Environmental Science(all)

Cite this

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abstract = "This study was conducted to monitor and control aeration by means of an online Neural Network (NN) of a Biological Aerated Filter (BAF). The BAF is an advanced drinking water treatment system equipped with Dissolved Oxygen (DO), oxidation-reduction potential, pH, ammonia and nitrate sensors. The main function of the BAF is to treat contaminated water by simultaneously reducing the levels of ammonia and manganese to below permit limits. Aeration was supplied to the BAF and controlled by a neural network. Real-time data was accurately predicted by the NN with errors below 5{\%} for all sensors. The bending point was apparently created in DO neural network data when the simultaneous ammonia and manganese removals were below limits. The NN program detected the bending point and immediately stopped the aeration of the BAF. Hence, NN can optimize the aeration requirement and system performance, shorten time demand and reduce consumption of manpower and electricity.",
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