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中文摘要：
浮选生产过程中浮选槽液位通常根据经验人工设定，具有主观随意性、液位波动大，使精／尾矿品位不满足要求．为此，提出一种基于浮选泡沫图像多特征的浮选槽液位智能优化设定的方法．在浮选槽工作原理以及液位与泡沫图像特征问关系的分析基础上，将基于案例推理的浮选槽液位预设定、基于多泡沫图像特征的改进LS-SVM（Least squares support vector machine）品位预测及基于BP神经网络的自学习模糊推理智能补偿等模型有机集成，提出了充分利用泡沫图像特征的液位智能优化设定方法．将该方法在某铝土矿浮选生产过程进行应用验证，可使粗选槽液位波动减小，提高了粗选精／尾矿品位合格率、总精矿品位合格率及回收率．
英文摘要：
In the flotation production process, the liquid level of flotation cells is usually set by on experiences. The liquid level can fluctuate in a large range such that the concentrate grade and tailings grade may not meet the requirement. In this paper, an intelligent optimal setting approach based on forth image features is proposed. On the basis of analysis of flotation cells/ working principle and relationship between level and froth image features, the pre-setting model based on CBR, the improved least squares support vector machine （LS-SVM） grade prediction model based on multiple froth features, and the self-learning fuzzy reasoning intelligent compensation model based on BP neural network are integrated together. This method is tested on a bauxite flotation process. The level fluctuation of rougher flotation cells decreases. The pass rates of concentrate and tailings grade in rougher flotation and the pass rate of the concentrate grade and recovery of the overall flotation increase.
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