Abstract
An artificial neural network (ANN) approach is investigated to model and study the phase holdup distributions of a liquid-solid circulating fluidized bed (LSCFB) system. The ANN model is developed based on different operating parameters of the LSCFB including primary and auxiliary liquid velocities, and superficial solids velocity. The competency of the model is examined by comparing the model predicted and the experimental phase holdup of the LSCFB riser reactor. It is also found that the ANN model successfully predicted the radial non-uniformity of phase holdup that is observed in the experimental runs of the riser. When compared, the model predicted output and trend of radial flow structure for solids holdup are in well agreement with the experiments. The mean absolute percentage error is around 6% and the correlation coefficient value of the predicted output and the experimental data is 0.992.
| Original language | English |
|---|---|
| Pages (from-to) | 71-77 |
| Number of pages | 7 |
| Journal | Powder Technology |
| Volume | 229 |
| DOIs | |
| State | Published - Oct 2012 |
Bibliographical note
Funding Information:The authors would like to acknowledge the support provided by Deanship of Scientific Research, King Fahd University of Petroleum and Minerals under research grant JF 101006 . The authors also gratefully acknowledge the contributions of PTRC, UWO for the experimental part of this work.
Keywords
- ANN modeling
- CFB
- Fluidization
- Hydrodynamics
- LSCFB
- Phase holdup
ASJC Scopus subject areas
- Chemical Engineering (all)