Research on the Denitrification Efficiency of Anammox Sludge Based on Machine Vision and Machine Learning

Yiming Hu, Dongdong Xu, Meng Zhang, Shihao Ge, Dongyu Shi, Yunjie Ruan

This study combines machine vision technology and deep learning models to rapidly assess the activity of anaerobic ammonium oxidation (Anammox) granular sludge. As a highly efficient nitrogen removal technology for wastewater treatment, the Anammox process has been widely applied globally due to its energy-saving and environmentally friendly features. However, existing sludge activity monitoring methods are inefficient, costly, and difficult to implement in real-time. In this study, we collected and enhanced 1000 images of Anammox granular sludge, extracted color features, and used machine learning and deep learning training methods such as XGBoost and the ResNet50d neural network to construct multiple models of sludge image color and sludge denitrification efficiency. The experimental results show that the ResNet50d-based neural network model performed the best, with a coefficient of determination (R2) of 0.984 and a mean squared error (MSE) of 523.38, significantly better than traditional machine learning models (with R2 up to 0.952). Additionally, the experiment demonstrated that under a nitrogen load of 2.22 kg-N/(m3·d), the specific activity of Anammox granular sludge reached its highest value of 470.1 mg-N/(g-VSS·d), with further increases in nitrogen load inhibiting sludge activity. This research provides an efficient and cost-effective solution for online monitoring of the Anammox process and has the potential to drive the digital transformation of the wastewater treatment industry.