Integrated prediction model of bauxite concentrate grade based on distributed machine vision
Integrated prediction model of bauxite concentrate grade based on distributed machine vision
复制标题
基于分布式机器视觉的铝土矿精矿品位综合预测模型
DOI：
10.1016/j.mineng.2013.07.003
复制
发表时间：
2013-11
影响因子：
4.8
通讯作者：
Yang Chunhua
中科院分区：
文献类型：
--
作者：
Xie Yongfang;Gui Weihua;Wei Lijun;Yang Chunhua
铝土矿浮选精矿品位是一项重要的工艺指标，直接影响铝的质量。针对现有基于机器视觉的精矿品位预测方法的单一性、局部性和不准确性，建立了铝土矿浮选过程的分布式机器视觉系统，并在此基础上提出了精矿品位的集成预测模型。首先，我们用实验的方法分析了不同浮选阶段的图像数据，以及评论其整体趋势和局部趋势之间的关系。然后利用多核最小二乘支持向量机和小波极值学习机分别建立精矿品位预测模型和残差补偿模型，通过集成对精矿品位进行预测。验证和工业应用表明，基于分布式机器视觉的集成预测模型具有良好的泛化能力，能够实现精矿品位的良好预测精度，相对误差小于6%，为浮选过程基于矿物品位的优化控制奠定了基础。
Concentrate grade of bauxite flotation is an important technology indicator, which has a direct effect on aluminum quality. Considering the unity, locality and inaccuracy of existing prediction methods of concentrate grade based on machine vision, a distributed machine vision system of bauxite flotation process is built in this paper, from which an integrated prediction model of concentrate grade is presented. At first, we use experimental methods to analyse image data from different flotation stages, as well as comment on the relationship between its global trends and local trends. Then taking advantage of the multiple kernels least squares support vector machine and wavelet extreme learning machine, models for prediction of concentrate grade and its residual compensation are established respectively to predict the concentrate grade through integration. Finally, validation and industrial applications show that the integrated prediction model based on distributed machine vision has a good generalization capability, which can achieve a good prediction accuracy of concentrate grade, with a relative error of less than 6%, thus laying a foundation for optimal control based on mineral grade in flotation process.
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DOI：
10.1016/s0167-4528(00)80066-6
发表时间：
2000
期刊：
Developments in mineral processing
影响因子：
--
作者：
G. Bonifazi;V. Giancontieri;A. Meloni;S. Serranti;F. Volpe;R. Zuco;Heikki Koivo;J. Hätönen;H. Hyötyniemi;A. Niemi;P. Sipari;H. Kuopanporrti;R. Ylinen;I. Heikkila;S. Lahteenmaki;J. Miettunen;O. Stephasson;Wen Wang;L. Carlsson
通讯作者：
G. Bonifazi;V. Giancontieri;A. Meloni;S. Serranti;F. Volpe;R. Zuco;Heikki Koivo;J. Hätönen;H. Hyötyniemi;A. Niemi;P. Sipari;H. Kuopanporrti;R. Ylinen;I. Heikkila;S. Lahteenmaki;J. Miettunen;O. Stephasson;Wen Wang;L. Carlsson
影响因子：
6
作者：
Huang, Guang-Bin;Zhu, Qin-Yu;Siew, Chee-Kheong
通讯作者：
Siew, Chee-Kheong
影响因子：
--
作者：
Haavisto, Olli;Kaartinen, Jani
通讯作者：
Kaartinen, Jani
影响因子：
14.3
作者：
Zhang, Jingqiao;Sanderson, Arthur C.
通讯作者：
Sanderson, Arthur C.
影响因子：
4.8
作者：
Hargrave, JM;Hall, ST
通讯作者：
Hall, ST