Time-aware attention network for multi-step future prediction in wastewater treatment

Area of Science:

• Environmental Engineering
• Artificial Intelligence
• Water Treatment Technology

Background:

• Modern wastewater treatment plants (WWTPs) face challenges including increased loads, strict regulations, and operational volatility.
• Existing machine learning models struggle with multivariate, multi-step dynamics and underutilize heterogeneous sensor data.
• Current models often rely on single-point predictions, failing to capture complex temporal patterns.

Purpose of the Study:

• To develop a deep learning framework for accurate multi-step forecasting of wastewater treatment parameters.
• To overcome limitations of existing models in capturing complex temporal dynamics and utilizing diverse sensor data.
• To enhance operational decision-making and energy efficiency in WWTPs through advanced predictive capabilities.

Main Methods:

• Developed the Time-Aware Attention Net (TAANet), a deep learning framework for time-ahead forecasting.
• Incorporated an attention-based fusion module for dynamic capture of interdependencies among variables.
• Utilized multi-component temporal embedding to represent nonstationary system behavior and periodic structures.

Main Results:

• TAANet demonstrated state-of-the-art accuracy and resilience in predicting wastewater treatment parameters, even under extreme conditions.
• Achieved significant improvements over baseline models (Informer, PatchTST, iTransformer, LSTM) in predictive performance.
• For chemical oxygen demand removal efficiency, R² improved from 0.19-0.30 to 0.46, and MSE was reduced by up to 32.8%.

Conclusions:

• TAANet shows strong capability for multi-step forecasting in wastewater treatment processes.
• The framework supports future-state awareness and operational decision-making in WWTPs.
• Potential to contribute to more proactive process management and energy-efficient system operation.