Effluent quality soft sensor for wastewater treatment plant with ensemble sparse learning-based online next...

Area of Science:

• Environmental Engineering
• Artificial Intelligence
• Machine Learning

Background:

• Real-time monitoring of wastewater quality is vital for safe and stable operations of wastewater treatment plants (WWTPs).
• Next-generation reservoir computing (NG-RC) shows promise for predicting quality variables like COD and BOD, acting as a data-driven soft sensor.
• Traditional NG-RC models trained offline struggle with dynamic scenarios, leading to performance degradation.

Purpose of the Study:

• To propose a sparse online NG-RC approach for real-time quality prediction in WWTPs.
• To address model degradation in dynamic environments and mitigate measurement noise.
• To develop an effective soft sensor for continuous quality indicator monitoring.

Main Methods:

• An incremental strategy inspired by the Woodbury matrix identity was developed for online learning of NG-RC output weights.
• An ensemble sparse strategy was integrated to reduce overfitting in the prediction model.
• A soft sensor was implemented using the ensemble sparse online NG-RC for real-time wastewater quality prediction.

Main Results:

• The proposed sparse online NG-RC approach demonstrated effectiveness in real-time quality prediction for WWTPs.
• The method showed improved robustness against dynamic changes and measurement noise compared to traditional approaches.
• Validation using datasets from two actual WWTPs confirmed the model's practical applicability and performance.

Conclusions:

• The developed ensemble sparse online NG-RC provides a robust and accurate solution for real-time quality monitoring in WWTPs.
• This data-driven soft sensor approach enhances operational stability and safety by enabling timely quality predictions.
• The findings offer a significant advancement in applying advanced machine learning techniques to environmental engineering challenges.