Voipan Daniel, Voipan Andreea Elena, Barbu Marian
Department of Computer Science and Information Technology, 'Dunarea de Jos' University of Galati, 800008 Galati, Romania.
Department of Automation, 'Dunarea de Jos' University of Galati, 800008 Galati, Romania.
Sensors (Basel). 2025 Mar 8;25(6):1692. doi: 10.3390/s25061692.
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.
在污水处理厂（WWTPs）中，要在天气条件多变的情况下维持出水水质面临着重大挑战，流量的突然变化和污染物负荷的增加会影响处理性能。传统的物理传感器在极端条件下既昂贵又容易出现故障。在本研究中，我们评估了基于人工智能（AI）的软传感器预测出水水质指数（EQI）计算基础成分的性能。因此，我们将研究重点放在三种机器学习模型上：长短期记忆（LSTM）、门控循环单元（GRU）和Transformer。以基准模拟模型2（BSM2）作为污水处理厂，我们能够获得用于训练机器学习模型的数据集，并评估它们在干燥天气、降雨时段和暴雨事件中的性能。为了根据天气类型改进网络分类，我们开发了一种基于随机森林（RF）的元分类器。结果表明，在干燥天气条件下，Transformer网络表现最佳，而在降雨时段和暴雨场景中，GRU能够以最高的准确率捕捉突然变化。LSTM在稳定条件下表现正常，但在快速波动时表现不佳。这些结果支持了在污水处理厂中集成基于人工智能的预测模型的决定，突出了循环网络（GRU）和前馈网络（Transformer）在不同天气条件下获得出水水质预测方面的卓越性能。
