基于BP人工神经网络的球磨机钢球配比预测模型 |
| |
引用本文: | 张胜东,,,童 雄,,,张 翼,蔡兵兵,谢 贤,,.基于BP人工神经网络的球磨机钢球配比预测模型[J].武汉工程大学学报,2016,38(3):299-307. |
| |
作者姓名: | 张胜东 童 雄 张 翼 蔡兵兵 谢 贤 |
| |
作者单位: | 1. 复杂有色金属资源清洁利用国家重点实验室,云南 昆明 650093;2. 昆明理工大学国土资源工程学院,云南 昆明 650093;3. 云南省金属矿尾矿资源二次利用工程研究中心,云南 昆明 650093;4. 武汉工程大学资源与土木工程学院,湖北 武汉 430074 |
| |
摘 要: | 采用BP神经网络对实验室磷矿球磨机磨矿中的钢球配比与磨矿产品粒级分布的关系进行建模, 解决选矿厂磨机生产中钢球配比的计算问题. 建立的BP神经网络预测模型通过磨矿产品粒级分布来预测对应的球磨机内钢球配比,预测绝对误差控制在3%以内,但预测相对误差较大且不稳定,说明在钢球配比与磨矿产品粒级分布的关系建模中该建模方法具有一定研究价值,该模型进一步优化后可具有工业应用价值.
|
关 键 词: | 磨矿 钢球配比 粒级分布 BP神经网络预测模型 |
Prediction Model on Matching Steel Ball in Mill Based on BP Artificial Neural Network |
| |
Authors: | ZHANG Shengdong TONG Xiong ZHANG Yi CAI Bingbing XIE Xian |
| |
Affiliation: | 1. State Key Laboratory of Complex Nonferrous Metal Resources Clean Utilization, Kunming 650093, China;2. Faculty of Land Resource Engineering, Kunming University of Science and Technology, Kunming 650093, China;3. Yunnan Province Engineering Research Center for Reutilization of Metal Tailings Resources, Kunming 650093, China; 4. School of Resources and Civil Engineering ,Wuhan Institute of Technology, Wuhan 430074, China |
| |
Abstract: | The error back prorogation (BP) artificial neural network was applied to establish the prediction model about the relationship between the particle size distribution of grinding product and the proportion of matching steel balls of different sizes in the ball mill of phosphate ore in the laboratory, which can predict the proportion of different size balls in the ball mill through the particle size distribution of grinding product. The mean absolute percentage error of the prediction can be controlled in 3%, but the mean relative percentage error of prediction is unacceptable, which illustrates that the modeling method has some research values, but it should be studied in-depth to reduce the error of model for application in the factory. |
| |
Keywords: | grinding proportion of matching steel balls of different sizes particle size distribution predicted model based on back prorogation artificial neural network |
本文献已被 CNKI 等数据库收录! |
| 点击此处可从《武汉工程大学学报》浏览原始摘要信息 |
|
点击此处可从《武汉工程大学学报》下载全文 |
|