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 (R

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Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.