摘要
鉴于精煤灰分的测量对煤泥浮选自动化的生产有着重要的意义,然而传统的燃烧法测量灰分的办法无法满足自动化生产的需求,设计了一种基于泡沫图像的浮选精煤灰分预测模型。首先对泡沫图像进行去噪声处理,然后用MIV值评价法对影响精煤灰分的特征进行筛选,最后建立基于径向基神经网络的精煤灰分预测模型,并通过与BP神经网络预测结果的对比,说明该网络在预测精煤灰分的优越性。
Determination of ash of flotation concentrate is of vital importance to automated find coal flota- tion process. However, the traditional method of rapid determination of ash through burning cannot meet the requirement of such a process. A flotation concentrate ash prediction model based on foam image is, therefore, designed through first denoising processing and then sieving of the characteristic factors affect- ing the concentrate ash using MIV value evaluation method. Following these processes, a RBF neural net- work-based ash prediction model is finally developed. As evidenced by comparison with the BP neutral network-based method, the RBF network-based model can produce a far better concentrate ash prediction result.
出处
《选煤技术》
CAS
2017年第5期69-72,共4页
Coal Preparation Technology
关键词
精煤灰分
泡沫图像
RBF神经网络
MIV值评价法
灰分预测模型
flotation concentrate ash
foam image
RBF neutral network, MIV value evaluation method
ash prediction model
作者简介
郭西进（1962-），男，安徽省固镇县人，博士，教授，硕士生导师，从事计算机集成制造技术、智能控制理论、自适应控制、网络控制的研究工作。E-mail：cumtcxt@163．com Tel：15152461868
