Name
Abstract | ||
|---|---|---|
The temperature of coke oven is an important process parameter, but it is difficult to obtain the temperature of the vertical flue in real-time. The establishment based on the case-based reasoning (CBR) and radial basis function neural network (RBFNN) of coke oven flue temperature intelligent prediction model, realise the real-time prediction of the temperature, and help to realise the coke oven production process of intelligent optimisation control. The real-time forecast under different conditions is realised by the selective intelligent forecasting model of the coke oven, and the forecasting performance of system model is simulated. The results show that the forecasting model is faster and more reliable than the traditional artificial forecast. Finally, combining with the actual data of a steel enterprise to verify, the results show that the model meet the actual working condition, it can provide relevant processing methods for the soft measurement of complex industrial production control process, and it has some practical significance for intelligent optimisation control. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1504/IJCSM.2018.10015908 | INTERNATIONAL JOURNAL OF COMPUTING SCIENCE AND MATHEMATICS |
Keywords | Field | DocType |
coke oven, temperature measurement, intelligent forecast, neural network, case-based reasoning, CBR | Process engineering,Flue,Industrial production,Mathematical optimization,Scheduling (production processes),Process variable,Coke,Artificial neural network,Case-based reasoning,Mathematics,System model | Journal |
Volume | Issue | ISSN |
9 | 4 | 1752-5055 |
Citations | PageRank | References |
2 | 0.36 | 0 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yang He | 1 | 14 | 3.56 |
| Gongfa Li | 2 | 239 | 43.45 |
| Ying Sun | 3 | 291 | 40.03 |
| Guozhang Jiang | 4 | 172 | 27.25 |
| Jian-yi Kong | 5 | 11 | 3.65 |
| Du Jiang | 6 | 97 | 14.40 |
| Honghai Liu | 7 | 1974 | 178.69 |