Hybrid Intelligence Model Based on Image Features for the Prediction of Flotation Concentrate Grade
Hybrid Intelligence Model Based on Image Features for the Prediction of Flotation Concentrate Grade
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DOI：
10.1155/2014/401380
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发表时间：
2014-06
影响因子：
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通讯作者：
Yalin Wang;Xiaofang Chen;XiaoLing Zhou;W. Gui;L. Caccetta;Honglei Xu
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文献类型：
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作者：
Yalin Wang;Xiaofang Chen;XiaoLing Zhou;W. Gui;L. Caccetta;Honglei Xu
在浮选过程中，精矿品位是关键的生产指标，但很难在线测量。机理模型反映了精矿品位变化的基本趋势，但不能提供足够的预测精度。基于泡沫图像特征的数据驱动模型可以在良好采样的空间内提供准确的预测，但严重依赖于泛化能力较低的样本数据。因此，本文提出了一种结合两种模型的混合智能模型。由于图像特征信息量巨大，且图像特征与精矿品位之间的关系是非线性的，因此采用B样条偏最小二乘法（BS-PLS）构建数据驱动的精矿品位预测模型。为了获得更好的泛化能力和预测精度，引入信息熵将机理模型和BS-PLS模型集成在一起，并通过输出偏差补偿策略在线修正模型输出。此外，采用滑动窗口方案更新混合模型以提高其适应性。工业实际数据测试结果表明，混合模型的性能优于两种单一模型，满足工业应用中的精度和稳定性要求。
In flotation processes, concentrate grade is the key production index but is difficult to be measured online. The mechanism models reflect the basic tendency of concentrate grade changes but cannot provide adequate prediction precision. The data-driven models based on froth image features provide accurate prediction within well-sampled space but rely heavily on sample data with less generalization capability. So, a hybrid intelligent model combining the two kinds of model is proposed in this paper. Since the information of image features is enormous, and the relationship between image features and concentrate grade is nonlinear, a B-spline partial least squares (BS-PLS) method is adopted to construct the data-driven model for concentrate grade prediction. In order to gain better generalization capability and prediction accuracy, information entropy is introduced to integrate the mechanism model and the BS-PLS model together and modify the model output online through an output deviation compensation strategy. Moreover, a slide window scheme is employed to update the hybrid model in order to improve its adaptability. The industrial practical data testing results show that the performance of the hybrid model is better than either of the two single models and it satisfies the accuracy and stability requirements in industrial applications.