| 基于IPSO-BP神经网络的富氧底吹铜熔炼炉喷枪寿命预测模型 |
| |
| 引用本文： | 武龙飞,张晓龙,胡建杭,徐建新,宋进,黄旷,刘杰.基于IPSO-BP神经网络的富氧底吹铜熔炼炉喷枪寿命预测模型[J].有色金属(冶炼部分),2023(12):18-23. |
| |
| 作者姓名： | 武龙飞 张晓龙 胡建杭 徐建新 宋进 黄旷 刘杰 |
| |
| 作者单位： | 昆明理工大学,昆明理工大学,昆明理工大学,昆明理工大学,昆明理工大学,昆明理工大学,昆明理工大学 |
| |
| 基金项目： | 国家自然科学基金联合基金资助项目（U2102213）；云南省科技厅重大专项课题（202202AG050002） |
| |
| 摘 要： | 富氧底吹铜熔炼炉喷枪是整个熔炼炉中最重要的部件，并且造价高，易损坏，工作环境恶劣复杂，对其进行准确的寿命预测比较困难。提出了一种基于IPSO-BP神经网络的寿命预测模型，粒子群优化算法解决了BP神经网络容易陷入局部极小值和训练速度慢的问题，优化的粒子群算法优化了惯性权重和学习因子，进一步加快了训练速度和搜索速度，提高了BP神经网络跳出局部极小值的能力。以工作环境中容易对喷枪寿命造成影响的因素作为输入，喷枪寿命作为输出，通过实际生产采集的数据做验证，并与BP神经网络和PSO-BP神经网络预测模型作对比。结果表明，本文构建的寿命预测模型预测效果比BP神经网络和PSO-BP神经网络的预测更加准确，精度更高，该预测模型为富氧底吹铜熔炼的喷枪寿命预测提供了一种方法借鉴。
|
| 关 键 词： | 改进粒子群算法 BP神经网络 寿命预测 喷枪 富氧底吹 铜 |
| 收稿时间： | 2023/7/16 0:00:00 |
| 修稿时间： | 2023/7/27 0:00:00 |
Life Prediction Model of Spray Gun in Oxygen-Enriched Bottom Blown Copper Smelting Furnace Based on IPSO-BP Neural Network |
| |
| WU Longfei,ZHANG Xiaolong,HU Jianhang,XU Jianxin,SONG Jin,HUANG Kuang and LIU Jie.Life Prediction Model of Spray Gun in Oxygen-Enriched Bottom Blown Copper Smelting Furnace Based on IPSO-BP Neural Network[J].Nonferrous Metals(Extractive Metallurgy),2023(12):18-23. |
| |
| Authors: | WU Longfei ZHANG Xiaolong HU Jianhang XU Jianxin SONG Jin HUANG Kuang and LIU Jie |
| |
| Affiliation: | a. school of Mechanical and Electrical Engineering,b. School of Metallurgy and Energy Engineering,Kunming University of Science and Technology,,,,,, |
| |
| 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. |
| |
| Keywords: | improved particle swarm optimization BP neural network life prediction spray gun oxygen enriched bottom blown copper |
|
| 点击此处可从《有色金属(冶炼部分)》浏览原始摘要信息 |
|
点击此处可从《有色金属(冶炼部分)》下载全文 |
|
