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| Life Prediction Model of Spray Gun in Oxygen-Enriched Bottom Blown Copper Smelting Furnace Based on IPSO-BP Neural Network |
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Received:July 16, 2023
Revised:July 27, 2023
Accepted:July 28, 2023
Published Online:October 27, 2023
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| DOI：doi：10.3969/j.issn.1007-7545.2023.12.003 |
| KeyWord:improved particle swarm optimization; BP neural network; life prediction; spray gun; oxygen enriched bottom blown; copper |
| Author | Institution |
| WU Longfei |
昆明理工大学 |
| ZHANG Xiaolong |
昆明理工大学 |
| HU Jianhang |
昆明理工大学 |
| XU Jianxin |
昆明理工大学 |
| SONG Jin |
昆明理工大学 |
| HUANG Kuang |
昆明理工大学 |
| LIU Jie |
昆明理工大学 |
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| Abstract: |
| The lance of oxygen-enriched bottom-blown copper smelting furnace is the most important part in the whole smelting furnace, and its cost is high, it is easy to be damaged, and its working environment is harsh and complicated, so it is difficult to predict its life accurately. A life prediction model based on IPSO-BP neural network was put forward, in which, the particle swarm optimization algorithm solves the problems that BP neural network is easy to fall into local minimum and the training speed is slow, the optimized particle swarm optimization algorithm optimizes the inertia weight and learning factor, and further accelerates the training speed and search speed. Taking the factors that easily affect the service life of the spray gun in the working environment as input and the service life of the spray gun as output, it is verified by the data collected in actual production, and compared with BP neural network and PSO-BP neural network prediction model. The results show that the prediction effect of the life prediction model constructed in this paper is more accurate and precise than that of BP neural network and PSO-BP neural network. This prediction model provides a method for the life prediction of lance in oxygen-enriched bottom blowing copper smelting. |
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