A convolutional neural networks approach using X-Ray absorption images for studying granular activated carbon

Abstract

X-ray methods have proven to be reliable, accurate and sensitive techniques to study activated carbons. The studying of granular activated carbon (GAC) samples through X-ray digital radiographic images using Deep Learning, more specifically convolutional neural networks (CNN) class of model, has been explored. Results were compared to hand-engineered characterization using X-Ray absorption method (XRA). It was proved that CNNs represent a fast and reliable analytical tool for indirect information on the chemical and physical characteristics of GACs. The proposed method opens possibilities for the application of Deep Learning based models on radiographic images for the characterization and comparison of exhausted and virgin porous materials.