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Machine learning framework for intelligent aeration control in wastewater treatment plants: Automatic feature
Yu-Qi Wang1, Hong-Cheng Wang1, Yun-Peng Song1
1State Key Laboratory of Urban Water Resource and Environment, School of Civil and Environmental Engineering, Harbin Institute of Technology Shenzhen, Shenzhen 518055, China.
Water Research
|October 8, 2023
Summary
This study introduces a novel feature engineering framework for intelligent wastewater treatment plant control, significantly improving aeration efficiency and reducing energy use. The developed models achieved a 16.12% reduction in air demand in real-world applications.
Area of Science:
- Environmental Engineering
- Artificial Intelligence in Water Treatment
- Machine Learning Applications
Background:
- Intelligent control of wastewater treatment plants (WWTPs) offers substantial energy savings and reduced greenhouse gas emissions.
- Machine learning (ML) is a powerful tool for managing complex WWTP data, but feature relationships are often unclear, limiting AI adoption.
- Precise control of aeration is crucial for optimizing WWTP efficiency and minimizing operational costs.
Purpose of the Study:
- To develop an automatic feature engineering framework, the variation sliding layer (VSL), for precise air demand control in WWTPs.
- To evaluate the effectiveness of VSL integrated with various machine learning models for enhancing aeration control.
- To demonstrate the practical applicability and impact of VSL-ML models in a full-scale WWTP setting.
Main Methods:
- Development of an automatic feature engineering framework based on the variation sliding layer (VSL).
- Integration of VSL with classic machine learning, deep learning, and ensemble learning algorithms.
- Validation of VSL-ML models using wastewater treatment plant datasets and implementation in a full-scale plant.
Main Results:
- VSL significantly improved the efficiency of aeration intelligent control across different ML approaches.
- Bayesian regression and ensemble learning models incorporating VSL demonstrated the highest accuracy in predicting air demand.
- Implementation in a full-scale WWTP resulted in a 16.12% reduction in air demand compared to conventional control methods.
Conclusions:
- The VSL-based feature engineering framework enhances AI-driven aeration control in WWTPs.
- VSL-ML models show strong potential for accurate air demand prediction and control, leading to significant operational savings.
- The freely accessible Python package 'wwtpai' aims to lower technical barriers for AI implementation in WWTPs.
Keywords:
Feature engineeringIntelligent controlMachine learningVariation sliding layerWastewater treatment plantsMore Related Videos
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