Optimization of industrial boiler combustion control system based on genetic algorithm
| Autor: | Weimin Zhong, Hongguang Pan, Zaiying Wang, Guoxin Wang |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
General Computer Science
Computer science Boiler (power generation) Steam pressure 02 engineering and technology Combustion 020401 chemical engineering Control and Systems Engineering Control theory Combustion process Control system 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing 0204 chemical engineering Electrical and Electronic Engineering Control methods |
| Zdroj: | Computers & Electrical Engineering. 70:987-997 |
| ISSN: | 0045-7906 |
| DOI: | 10.1016/j.compeleceng.2018.03.003 |
| Popis: | In the combustion process, the traditional furnace combustion control method cannot meet the control requirements with frequent variable loads. Firstly, for the combustion control system with the varying variable bias and bi-double crossing limit, the ranges of bias coefficients are analyzed gradually. Secondly, the objective function is designed based on the excess air coefficient and the main steam pressure deviation signal. Finally, the genetic algorithm is adopted to optimize the bias coefficients to achieve better control performance. The simulation results show that the presented method can effectively improve the response speed and keep the excess air coefficient in the optimal combustion interval. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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