ABSTRACT

Froth flotation is widely used in the resource industry as a particle separation process. The performance of the flotation process is significantly affected by the mobility of the froth phase. Despite its importance, little work has been done to develop a simple and robust method for indicating froth mobility. In the present study, a simple method to monitor froth mobility was developed using a readily available web-camera to take images and a convolutional neural network (CNN) model classifying the images mainly based on the degree of motion blur. The CNN model was trained with a newly built froth image dataset, comprising froth images taken near the overflowing lip of an industrial flotation cell at a wide range of operating conditions using the web-camera. It was found that the trained model could correctly classify 98% of the froth images into one of three categories: low, medium, and high mobility. The froth mobility determined by the trained CNN model was in good agreement with the one analyzed with a commercial software. A potential application of the present method for indicating flotation performance was illustrated.

摘要

泡沫浮选作为一种颗粒分离工艺广泛应用于资源行业。浮选过程的性能受泡沫相流动性的显着影响。尽管它很重要，但很少有人开发一种简单而可靠的方法来指示泡沫流动性。在本研究中，开发了一种监测泡沫流动性的简单方法，使用现成的网络摄像头拍摄图像，并使用卷积神经网络 (CNN) 模型主要根据运动模糊程度对图像进行分类。CNN 模型使用新建的泡沫图像数据集进行训练，该数据集包括使用网络摄像头在各种操作条件下在工业浮选槽溢出边缘附近拍摄的泡沫图像。结果发现，经过训练的模型可以将 98% 的泡沫图像正确分类为三类之一：低、中和高流动性。由经过训练的 CNN 模型确定的泡沫流动性与使用商业软件分析的泡沫流动性非常一致。说明了本方法在指示浮选性能方面的潜在应用。