数据驱动的锑粗选泡沫图像特征优化设定

针对锑浮选过程中精、尾矿品位难以在线检测, 浮选性能不稳定的问题, 提出一种数据驱动的泡沫图像特征优化设定方法. 该方法根据入矿品位类型对泡沫图像特征进行优化设定, 并针对不同入矿品位类型的样本分布特点,先尝试采用案例推理的方法从历史数据中寻找浮选性能优良的泡沫状态. 若经验知识不足, 则采用基于多中心模糊C均值聚类与概率支持向量回归的区间II 型模糊系统建模方法建立精、尾矿品位指标模型, 并在此基础上利用智能优化方法寻优泡沫图像特征值. 某锑浮选工业实验结果表明了所提出方法的有效性.

Data-driven optimal setting for froth image features of stibium rougher flotation

Due to the difficulties of measuring concentrate and tailing grade online and unstability of the flotation performance, a data-driven optimal setting for froth image features is proposed. The froth image features are optimal set according to the type of feed ore grade. Considering the distribution nature of samples of each feed grade type, it is tried to use case-based reasoning method for obtaining the froth status with optimal flotation performance from history data. When lack of enough experiential knowledge, the interval type II fuzzy system modeling method based on the multi-centers fuzzy C-means clustering and probabilistic support vector regress method is adopted to build the concentrate and tailing grade model. Then intelligent optimization algorithm is applied to search the optimal values for the froth image features. The application to the stibium flotation process shows the effectiveness of the proposed method.