A Study on Prediction of Turbidity after Flocculation from Floc Images by Deep Learning for Water Purification Process Control

The objective of this paper is to predict the turbidity after flocculation from floc images in jar-test deep convolutional neural network (DCNN). Our goal is to develop a system to control the water purification process using the predictive model. In conventional studies, chemical parameters such as pH and alkalinity are generally used to predict turbidity. However, our proposed method does not use chemical parameters. It uses images of floating matter, called “floc”, generated during the flocculation process as input to the DCNN. We performed experiments using DCNN to predict turbidity after flocculation from images of “flocs” generated in jar-tests. We used VGG-16 as the DCNN for our experiments. Furthermore, we conducted experiments to compare the proposed method with the baseline method using chemical parameters.