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Reagent Predictive Control Using Joint Froth Image Feature for Antimony Flotation Process

机译：利用联合泡沫图像特征进行锑浮选过程的试剂预测控制

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摘要

The prerequisite for achieving automatic control and optimal operation in flotation process is to extract distinctive froth image features. With the fluctuation of flotation conditions, the morphological features of surface bubbles will change. It is not reliable to impose bubble size distribution as the decisive character to represent the complex flotation conditions. Therefore, a novel joint froth image feature was proposed via analysing bubble size and shape simultaneously. To consist with the observation process of operators in plant and overcome the deficiency of bubble samples, splicing froth images from sequential frames were segmented to obtain the joint distribution and a nonparametric estimation using B-spline functions was introduced to depict the shape of distribution. Then a multi-output least square support vector regressor (MLS-SVR) was implemented to construct the relationship between the weights of the B-spline functions and the reagent dosage. Finally, a joint feature based reagent predictive controller was constructed to validate the proposed method. Actual industrial data experiment results have shown its effectiveness and feasibility.
机译：浮选过程中实现自动控制和最佳操作的前提是提取独特的泡沫图像特征。随着浮选条件的变化，表面气泡的形态特征将发生变化。将气泡大小分布作为代表复杂浮选条件的决定性特征是不可靠的。因此，通过同时分析气泡的大小和形状，提出了一种新颖的关节泡沫图像特征。为了适应操作员在工厂的观察过程，克服气泡样本的不足，对连续帧拼接的泡沫图像进行分割，得到联合分布，并采用B样条函数进行非参数估计，以描述分布的形状。然后，采用多输出最小二乘支持向量回归器（MLS-SVR）来构建B样条函数的权重与试剂用量之间的关系。最后，构建了基于联合特征的试剂预测控制器来验证所提出的方法。实际的工业数据实验结果表明了其有效性和可行性。

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