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Related Experiment Video

Updated: Jan 27, 2026

Use of a Battery of Chemical and Ecotoxicological Methods for the Assessment of the Efficacy of Wastewater Treatment Processes to Remove Estrogenic Potency

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Published on: September 11, 2016

ANN-Based Soft Sensor to Predict Effluent Violations in Wastewater Treatment Plants.

Ivan Pisa¹, Ignacio Santín², Jose Lopez Vicario³

¹Department of Telecommunications and Systems Engineering, Escola d'Enginyeria, Universitat Autònoma de Barcelona, 08193 Bellaterra, Spain. ivan.pisa@uab.cat.

Sensors (Basel, Switzerland)

|March 16, 2019

Summary

This study introduces an artificial neural network (ANN) soft sensor using Long-Short Term Memory (LSTM) to predict nitrogen pollutants in wastewater. The system accurately forecasts violations, aiding in environmental protection and regulatory compliance.

Keywords:

artificial neural networks long-short term memory cells soft sensors wastewater treatment plants

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Published on: March 24, 2023

Area of Science:

Environmental Engineering
Artificial Intelligence
Water Quality Management

Background:

Wastewater treatment plants (WWTPs) face challenges in meeting strict effluent regulations for pollutants like nitrogen.
Existing control strategies sometimes fail to prevent violations, leading to environmental damage such as eutrophication and aquatic toxicity.
Accurate prediction of nitrogen-derived components is crucial for proactive control and compliance.

Purpose of the Study:

To develop and evaluate an artificial neural network (ANN)-based soft sensor for predicting ammonium (SNH) and total nitrogen (SNtot) in wastewater effluent.
To integrate the soft sensor with existing control strategies to enhance their effectiveness in preventing regulatory violations.
To assess the prediction accuracy and violation detection capabilities of the proposed soft sensor.

Main Methods:

Implementation of a Long-Short Term Memory (LSTM) network as a soft sensor within the Benchmark Simulation Model N.2 (BSM2).
Testing the soft sensor's performance with three control strategies of varying complexity.
Evaluating the accuracy of nitrogen-derived component predictions and the probability of detecting effluent limit violations.

Main Results:

The LSTM-based soft sensor demonstrated high accuracy in predicting nitrogen-derived products.
The system achieved an 86%–94% probability of detecting violations of BSM2 effluent limits.
Calibration of the soft sensor can further improve prediction accuracy, potentially achieving perfect violation prediction at the cost of increased false positives.

Conclusions:

The proposed ANN-based soft sensor effectively predicts key nitrogen parameters in wastewater.
The predictive capabilities enhance the performance of WWTP control strategies, reducing the likelihood of effluent violations.
This technology offers a valuable tool for improving water quality management and ensuring environmental compliance.