Estimation of floc condition in a dewatering process by image analysis using machine learning

Abstract

Dewatering is a crucial process in sludge treatment plants, and appropriate mixing of polymer and sludge is an important factor in achieving efficient dewatering. This study focused on the condition of flocs produced by mixing sludge and polymer, and estimated the floc condition through visual analysis of images. In this study, the estimation of floc condition was assumed to be a classification problem of mixer speed, and validation was conducted to classify the appropriate speed based on the images. The proposed methodology involved the development of a machine learning model characterized by high accuracy and transparency. This model was formulated using two features extracted from the images, i.e., the gaps between flocs and their texture, which are the parameters used by human operators to estimate floc condition. Explainable Boosting Machine was used as the machine learning model, which allows interpretation of the model’s contents and can be applied easily. The classification accuracy of this model was validated using both interpolated and extrapolated data, yielding accuracies exceeding 95% in both scenarios. Furthermore, comparative analysis was performed between the proposed transparent box model and a conventional Convolutional Neural Network (CNN) model. Despite its transparent box nature, the proposed approach demonstrated a comparable level of accuracy to the CNN model in this comparative study.