Transforming Prediction into Decision: Leveraging Transformer-Long Short-Term Memory Networks and Automatic Control...

Area of Science:

• Environmental Engineering
• Wastewater Treatment Technology
• Artificial Intelligence in Environmental Science

Background:

• Accurate prediction of ammonia nitrogen (NH3-N) concentration is crucial for assessing wastewater treatment efficiency and environmental impact.
• Complex dynamics and limited measurements in sequencing batch reactor (SBR) systems present challenges for precise NH3-N prediction.
• Existing methods often struggle with the intricate temporal dependencies and multivariate nature of NH3-N dynamics.

Purpose of the Study:

• To develop an advanced predictive model for NH3-N concentration in SBR systems.
• To improve the accuracy and reliability of NH3-N prediction by incorporating novel input features and hybrid AI architectures.
• To reduce energy and time consumption in wastewater treatment processes through intelligent automation.

Main Methods:

• Proposed an innovative Transformer-long short-term memory (Transformer-LSTM) network model, integrating Transformer's long-range dependency capture with LSTM's sequential pattern modeling.
• Incorporated dissolved oxygen (DO), electrical conductivity (EC), and oxidation-reduction potential (ORP), along with their rates of change and cumulative values, as input variables.
• Validated the model using experimental NH3-N datasets from an SBR system, comparing performance against existing advanced methods.

Main Results:

• The Transformer-LSTM model significantly outperformed existing methods, demonstrating superior performance in root mean squared error (RMSE), mean absolute error (MAE), and coefficient of determination (R²).
• Integration with real-time sensor data and automatic control led to substantial improvements in water treatment processes.
• Achieved a 26.9% reduction in energy or time consumption compared to traditional fixed processing cycles.

Conclusions:

• The proposed Transformer-LSTM model offers an accurate and reliable tool for predicting NH3-N concentrations in SBR systems.
• This methodology contributes to enhanced sustainability in water treatment by optimizing processes and reducing resource consumption.
• The findings support the effective implementation of AI-driven automatic control for meeting emission standards and improving wastewater management.