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Evaluating Machine Learning-Based Soft Sensors for Effluent Quality Prediction in Wastewater Treatment Under Variable

Daniel Voipan1, Andreea Elena Voipan2, Marian Barbu2

  • 1Department of Computer Science and Information Technology, 'Dunarea de Jos' University of Galati, 800008 Galati, Romania.

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|April 28, 2025
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Summary

Artificial intelligence (AI) models predict wastewater effluent quality under varying weather. AI soft sensors, including Transformer and Gated Recurrent Unit (GRU) networks, offer accurate predictions, outperforming traditional sensors.

Keywords:
BSM2GRULSTMRF classificationartificial intelligenceeffluent qualitysoft sensorstransformerwastewater treatmentweather conditions

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Area of Science:

  • Environmental Engineering
  • Water Treatment Technologies
  • Artificial Intelligence in Environmental Monitoring

Background:

  • Wastewater treatment plants (WWTPs) face challenges maintaining effluent quality due to variable weather conditions impacting flow rates and pollutant loads.
  • Traditional physical sensors are expensive and prone to failure during extreme weather events.
  • Developing robust predictive models is crucial for consistent wastewater treatment performance.

Purpose of the Study:

  • To evaluate the performance of artificial intelligence (AI) based soft sensors for predicting effluent quality index (EQI) components in WWTPs.
  • To compare the effectiveness of Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Transformer models under diverse weather scenarios.
  • To enhance weather condition classification using a Random Forest (RF) meta-classifier.

Main Methods:

  • Utilized the Benchmark Simulation Model no. 2 (BSM2) to generate training and testing datasets for WWTP operations.
  • Trained and evaluated LSTM, GRU, and Transformer machine learning models on simulated dry weather, rain, and storm event data.
  • Implemented a Random Forest (RF) meta-classifier to improve model selection based on weather type.

Main Results:

  • Transformer models demonstrated superior performance in predicting effluent quality during dry weather conditions.
  • GRU networks exhibited the highest accuracy in capturing rapid variations during rain episodes and storm events.
  • LSTM models performed adequately in stable conditions but showed limitations in handling rapid fluctuations.

Conclusions:

  • AI-based soft sensors show significant promise for real-time effluent quality prediction in WWTPs.
  • GRU and Transformer models offer distinct advantages for different weather conditions, supporting their integration into WWTP operations.
  • The study validates the use of AI for robust wastewater quality monitoring, especially under challenging environmental variability.