Deep learning-based flocculation sensor for automatic control of flocculant dose in sludge dewatering processes during wastewater treatment

In sludge dewatering of most wastewater treatment plants (WWTPs), the dose of polymer flocculant is manually adjusted through direct visual inspection of the flocs without the aid of any instruments. Although there is a demand for the development of automatic control of flocculant dosing, this has been challenging owing to the lack of a reliable flocculation sensor. To address this issue, this study developed a novel image sensor for measuring the degree of flocculation (DF) based on deep learning. Two types of sludge samples were used in the laboratory-scale flocculation tests: excess sludge and mixtures of excess sludge and raw wastewater. To search for a deep learning regression model suitable for DF inference, ten models, including convolutional neural networks, vision transformers, and a multilayer perceptron MLP mixer, were comparatively analysed. The ConvNeXt and MLP mixer models showed the highest accuracies with coefficients of determination (R2) greater than 0.9. The region contributing to the DF inference in the flocculation images was visualised using a modulus-weighted, gradient-weighted regression activation map. A prototype of the flocculation sensor was constructed using an inexpensive EdgeAI device. This device comprises a single-board computer and an integrated graphical processing unit (GPU) and is equipped with a quantised ConvNeXt model. The maximum inference speed of the sensor was 12.8 frames per second (FPS). The flocculation control tests showed that the sensor could control the DF to the target value by adjusting the polymer flocculant dose. Therefore, the flocculation sensor can provide a data-driven approach to the management of the flocculation process, thereby facilitating the automation of WWTPs.