小型凝集装置を用いた深層学習による凝集後濁度予測手法の検討

抄録

The objective of this paper is to predict the turbidity after flocculation from floc images using deep convolutional neural network(DCNN) with small flocculation plant. Our goal is to develop a system to control the water purification process using the predictive model. The following results were recognize from experiments using floc images from a small flocculation plant: 1) the DCNN model is able to recognize the image characteristics of the flock; 2) the prediction accuracy is around 0.10 for the teacher data and around 0.31 for the test data.