Evaluating Machine Learning-Based Soft Sensors for Effluent Quality Prediction in Wastewater Treatment Under Variable Weather Conditions
Maintaining effluent quality in wastewater treatment plants (WWTPs) comes with significant challenges under variable weather conditions, where sudden changes in flow rate and increased pollutant loads can affect treatment performance. Traditional physical sensors became both expensive and susceptibl...
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MDPI AG
2025-03-01
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| author | Daniel Voipan Andreea Elena Voipan Marian Barbu |
| author_facet | Daniel Voipan Andreea Elena Voipan Marian Barbu |
| author_sort | Daniel Voipan |
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| description | Maintaining effluent quality in wastewater treatment plants (WWTPs) comes with significant challenges under variable weather conditions, where sudden changes in flow rate and increased pollutant loads can affect treatment performance. Traditional physical sensors became both expensive and susceptible to failure under extreme conditions. In this study, we evaluate the performance of soft sensors based on artificial intelligence (AI) to predict the components underlying the calculation of the effluent quality index (EQI). We thus focus our study on three ML models: Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU) and Transformer. Using the Benchmark Simulation Model no. 2 (BSM2) as the WWTP, we were able to obtain datasets for training the ML models and to evaluate their performance in dry weather scenarios, rainy episodes, and storm events. To improve the classification of networks according to the type of weather, we developed a Random Forest (RF)-based meta-classifier. The results indicate that for dry weather conditions the Transformer network achieved the best performance, while for rain episodes and storm scenarios the GRU was able to capture sudden variations with the highest accuracy. LSTM performed normally in stable conditions but struggled with rapid fluctuations. These results support the decision to integrate AI-based predictive models in WWTPs, highlighting the top performances of both a recurrent network (GRU) and a feed-forward network (Transformer) in obtaining effluent quality predictions under different weather conditions. |
| format | Article |
| id | doaj-art-1eac7859531b4c2ead4a2a4041cbf5dc |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
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| series | Sensors |
| spelling | doaj-art-1eac7859531b4c2ead4a2a4041cbf5dc2025-08-20T03:43:54ZengMDPI AGSensors1424-82202025-03-01256169210.3390/s25061692Evaluating Machine Learning-Based Soft Sensors for Effluent Quality Prediction in Wastewater Treatment Under Variable Weather ConditionsDaniel Voipan0Andreea Elena Voipan1Marian Barbu2Department of Computer Science and Information Technology, ‘Dunarea de Jos’ University of Galati, 800008 Galati, RomaniaDepartment of Automation, ‘Dunarea de Jos’ University of Galati, 800008 Galati, RomaniaDepartment of Automation, ‘Dunarea de Jos’ University of Galati, 800008 Galati, RomaniaMaintaining effluent quality in wastewater treatment plants (WWTPs) comes with significant challenges under variable weather conditions, where sudden changes in flow rate and increased pollutant loads can affect treatment performance. Traditional physical sensors became both expensive and susceptible to failure under extreme conditions. In this study, we evaluate the performance of soft sensors based on artificial intelligence (AI) to predict the components underlying the calculation of the effluent quality index (EQI). We thus focus our study on three ML models: Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU) and Transformer. Using the Benchmark Simulation Model no. 2 (BSM2) as the WWTP, we were able to obtain datasets for training the ML models and to evaluate their performance in dry weather scenarios, rainy episodes, and storm events. To improve the classification of networks according to the type of weather, we developed a Random Forest (RF)-based meta-classifier. The results indicate that for dry weather conditions the Transformer network achieved the best performance, while for rain episodes and storm scenarios the GRU was able to capture sudden variations with the highest accuracy. LSTM performed normally in stable conditions but struggled with rapid fluctuations. These results support the decision to integrate AI-based predictive models in WWTPs, highlighting the top performances of both a recurrent network (GRU) and a feed-forward network (Transformer) in obtaining effluent quality predictions under different weather conditions.https://www.mdpi.com/1424-8220/25/6/1692soft sensorsartificial intelligencewastewater treatmenteffluent qualityGRUtransformer |
| spellingShingle | Daniel Voipan Andreea Elena Voipan Marian Barbu Evaluating Machine Learning-Based Soft Sensors for Effluent Quality Prediction in Wastewater Treatment Under Variable Weather Conditions Sensors soft sensors artificial intelligence wastewater treatment effluent quality GRU transformer |
| title | Evaluating Machine Learning-Based Soft Sensors for Effluent Quality Prediction in Wastewater Treatment Under Variable Weather Conditions |
| title_full | Evaluating Machine Learning-Based Soft Sensors for Effluent Quality Prediction in Wastewater Treatment Under Variable Weather Conditions |
| title_fullStr | Evaluating Machine Learning-Based Soft Sensors for Effluent Quality Prediction in Wastewater Treatment Under Variable Weather Conditions |
| title_full_unstemmed | Evaluating Machine Learning-Based Soft Sensors for Effluent Quality Prediction in Wastewater Treatment Under Variable Weather Conditions |
| title_short | Evaluating Machine Learning-Based Soft Sensors for Effluent Quality Prediction in Wastewater Treatment Under Variable Weather Conditions |
| title_sort | evaluating machine learning based soft sensors for effluent quality prediction in wastewater treatment under variable weather conditions |
| topic | soft sensors artificial intelligence wastewater treatment effluent quality GRU transformer |
| url | https://www.mdpi.com/1424-8220/25/6/1692 |
| work_keys_str_mv | AT danielvoipan evaluatingmachinelearningbasedsoftsensorsforeffluentqualitypredictioninwastewatertreatmentundervariableweatherconditions AT andreeaelenavoipan evaluatingmachinelearningbasedsoftsensorsforeffluentqualitypredictioninwastewatertreatmentundervariableweatherconditions AT marianbarbu evaluatingmachinelearningbasedsoftsensorsforeffluentqualitypredictioninwastewatertreatmentundervariableweatherconditions |