Abstract
An important parameter in silo discharging is residence time distribution (RTD), which describes the time each pebble stays within the bed. RTD can be applied as a key indicator to estimate reaction processes, and a key analytical metric to assess the inherent safety of reactors. However, due to the difficulty in obtaining pebble trajectories, few studies on discharging processes have been carried out. In this research, a pilot experimental study is conducted to get quantitative data and prediction model on RTD with precise predictions. The feasibility of applying neural network methods to RTD prediction is also checked. The accuracy of neural network prediction is confirmed by comparing the error between predicted and experimental results. It is discovered that RTD isoline changes in different regions and characterized by intuitive physical models. Compared to DEM, the neural network has a significant advantage in computing speed while retaining acceptable prediction accuracy.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
