Prediction of wastewater treatment quality using LSTM neural network

Nitzan Farhi*, Efrat Kohen, Hadas Mamane, Yuval Shavitt

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Wastewater treatment (WWT) process is used to prevent pollution of water sources, improves sanitation condition, and reuse the water (mostly for agricultural purposes). One of the main goals of wastewater treatment is removal of nutrients, such as nitrogen which exists in the form of ammonia in the sewage. Excessive nitrogen concentration in the effluent is well known for eutrophication in aquatic environments and may cause a decrease of groundwater quality as a result of irrigation. However, it is not uncommon that the biological process results with undesirably high concentrations of nutrients, and therefore Wastewater Treatment Plants (WWTP) monitor nutrients to alert operators of this problem. It is desirable to identify WWT problems in the process ahead in order to achieve a better treatment. Thus, we suggest a novel machine learning method, based on Long-Short Term Memory (LSTM) architecture, that is able to predict effluent concentration of ammonia NH4+ and nitrate NO3 a few hours ahead. We used measurements from the biological reactors sampled every minute, and combine it, for the first time in the literature, with climate measurements for improved prediction accuracy. Our proposed method showed an accuracy rate of 99% and F1-Scoreof 88% when predicting ammonia concentrations and an accuracy rate of 90% and F1-Scoreof 93% when predicting nitrate concentrations.

Original languageEnglish
Article number101632
JournalEnvironmental Technology and Innovation
Volume23
DOIs
StatePublished - Aug 2021

Keywords

  • Activated sludge
  • Fault prediction
  • LSTM
  • Sliding window
  • Wastewater Treatment Plants

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