Name
Title | ||
|---|---|---|
A data-driven soft sensor modeling for furnace temperature of Opposed Multi-Burner gasifier. |
Abstract | ||
|---|---|---|
The Opposed Multi-Burner (OMB) Coal-Water Slurry (CWS) gasification is a new large-scale coal gasification technology with higher product yield, lower oxygen and coal consumption than that of Texaco CWS gasification technology. However, current furnace temperature measurements of OMB and other gaisifiers are unstable and even short-life due to the extreme internal environment: high temperature, strong corrosion, etc. Therefore a new data-driven soft sensor modeling technique for furnace temperature of OMB gasifier is proposed and the selection of secondary variables and model structure of BP neural network is studied in this paper. Results indicate that, the furnace temperature predictive model integrating Principal Component Analysis (PCA) and BP neural network has a promising performance with good predictive precision. © 2011 IEEE. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/ICNC.2011.6022141 | ICNC |
Keywords | Field | DocType |
bp neural network,coal-water slurry gasification,opposed multi-burner,principal component analysis,soft sensor modeling,neural nets,slurries,coal,backpropagation,coal gasification,prediction model,neural network,temperature measurement | Process engineering,Mathematical optimization,Combustor,Soft sensor,Computer science,Coal,Wood gas generator,Temperature measurement,Principal component analysis,Coal gasification,Slurry | Conference |
Volume | Issue | Citations |
2 | null | 0 |
PageRank | References | Authors |
0.34 | 2 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jie Li | 1 | 0 | 0.34 |
| Weimin Zhong | 2 | 79 | 14.18 |
| Hui Cheng | 3 | 0 | 0.68 |
| Xiangdong Kong | 4 | 5 | 1.20 |
| Feng Qian | 5 | 0 | 1.01 |