Prediction of wastewater treatment quality using LSTM neural network

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-Score of 88% when predicting ammonia concentrations and an accuracy rate of 90% and F1-Scoreof 93% when predicting nitrate concentrations. (C) 2021 Elsevier B.V. All rights reserved.

Automated summary

The main goal of wastewater treatment is to remove nutrients, such as nitrogen, from sewage to prevent water pollution. The biological treatment process can sometimes result in high concentrations of nutrients, leading WWTPs to monitor and alert operators. To improve treatment outcomes, a novel machine learning method based on LSTM architecture is proposed for predicting effluent concentrations of ammonia and nitrate.