Advanced temporal deep learning framework for enhanced predictive modeling in industrial treatment systems

Abstract

• Developed a hybrid model for interval prediction to forecast industrial STP water quality. • Used VAEs to address data scarcity by generating realistic synthetic datasets. • Combined Autoencoders with SOM for anomaly detection and feature selection. • Integrated TCNs with BiLSTM and BiGRU for advanced forecasting frameworks. • Enhanced TCN_BiLSTM with pre/post-soft attention and Multi-Head Attention layer. This research introduces an innovative hybrid modeling framework tailored for interval prediction, aimed at forecasting water quality parameters in industrial sewage treatment plants (STPs). It tackles key challenges in the field, including limited data availability, detecting anomalies, and selecting relevant features with precision. By addressing these critical gaps, the study advances predictive analytics for wastewater treatment systems. The main goal was to create a scalable and resilient model that consistently provides accurate forecasts for essential water quality indicators. To accomplish this, Variational Autoencoders (VAEs) were employed to generate synthetic datasets that mimic real-world patterns, improving data availability and enhancing the model's generalization capabilities. Autoencoder paired with a Self-Organizing Map (SOM) was leveraged for anomaly detection and efficient feature selection. The study evaluated advanced architectures, including a Temporal Convolutional Network (TCN), TCN integrated with bidirectional Long Short-Term Memory (BiLSTM), and refined TCN_BiLSTM models featuring pre- and post-soft attention layers. The final model incorporated Multi-Head Attention mechanisms in both pre- and post-processing stages (TCN_BiLSTM_MultiHead_Attention), delivering a substantial performance improvement. The TCN_BiLSTM_MultiHead_Attention model proved to be the top performer, delivering state-of-the-art results with R² scores of 0.9732, 0.9567, and 0.9638 for the training, validation, and test datasets, respectively. On the test set, it achieved an impressive MSE of 0.0008 and an MAE of 0.0198. These results underscore the model's exceptional accuracy in predicting key parameters, including BOD, COD, and AmmoniaNitrogen. The results highlight the significant potential of hybrid deep learning frameworks in capturing temporal patterns and complex dynamics within STP data. By integrating temporal pattern recognition, long-term dependency modeling, and sophisticated attention mechanisms, this study offers actionable insights for practitioners and establishes a strong foundation for future research in predictive analytics for environmental systems.

Topics & Concepts