Journal
PARTICULATE SCIENCE AND TECHNOLOGY
Volume -, Issue -, Pages -Publisher
TAYLOR & FRANCIS INC
DOI: 10.1080/02726351.2023.2283582
Keywords
Artificial neural network; curvature radius; pneumatic conveying; power dissipation; pressure drop
Categories
Ask authors/readers for more resources
This paper investigates the distribution of system pressure drop when conveying particles using pipes with different curvature radius for pneumatic conveying system. Artificial neural network technique is used to predict the pressure drop. The results show that the pressure drop of the system is lower when using the pipe with R/D = 6.25 for conveying particles.
To investigate the system pressure drop distribution when conveying particle using different curvature radius pipes for the pneumatic conveying system, this paper measured the particle velocity distribution, particle-particle collision characteristics, collision energy loss, minimum pressure drop gas velocity, system pressure drop distribution, and power dissipation for R/D = 3.75, R/D = 5, and R/D = 6.25 pipes. Subsequently, the artificial neural network technique is used to predict the pressure drop of the pneumatic conveying system. It is found that the pressure drop of the system is lower when using the pipe with R/D = 6.25 for conveying particles. Compared to the pipe with R/D = 3.75, the reduction in power dissipation is 3.18 and 5.27% for conveying pellets when using R/D = 5 and R/D = 6.25 pipes, respectively. In addition, the energy loss of the system can be effectively reduced when using the pipe with R/D = 6.25 for conveying particles, which is more beneficial for the particles move in the pipe. The pressure drop model built with artificial neural network can predict the pressure drop value of the system more accurately within +/- 1.5%.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available