Convolutional Neural Network Model for Estimating Clogging of Ash-Mixed Sand Filters

Abstract

A deep learning model was developed to estimate the clogging of voids in bottom-ash-mixed sand filters. Filter clogging is characterized by a decrease in the infiltration ratio with an increase in the outflow. The estimation of clogging for time-series forecasting is a regression problem. Various algorithms for regression problems have been applied through machine learning and deep learning. Despite the various algorithm applications, an application for clogging appears to be emerging. Studies are required to develop dependable models for precisely predicting clogging. A convolutional neural network (CNN) clogging model was developed in this study to estimate the relationship between the infiltration ratio and outflow quantity using features defined by the mixing ratio between sand and ash. Significant clogging data required for the effective maintenance and stable operation of stormwater infiltration filters were obtained using the proposed model. The CNN clogging model is recommended as a supplement to conventional theoretical models and expensive experimental models.