尾矿
人工智能
色调
RGB颜色模型
泡沫浮选
计算机视觉
软传感器
特征提取
计算机科学
工艺工程
环境科学
过程(计算)
工程类
材料科学
冶金
操作系统
作者
Dingsen Zhang,Xianwen Gao,Wenhai Qi
标识
DOI:10.1177/01423312221096450
摘要
In the iron reverse flotation production process, the amount of flotation agent and the quality of flotation products are usually judged according to the grade of tailings, so it is essential to measure the grade of tailings froth. This research applies computer vision and image feature extraction technology to the soft sensor of tailings froth grade. An adaptive selection method for the image target region is proposed. The relationship between RGB (Red, Green, Blue), HSI (Hue, Saturation, Intensity), and Lab color space and tailings grade of reverse flotation in iron mine has been analyzed. A new image feature is proposed to characterize the degree of froth mineralization. The RGB and HSI dual color space feature values and froth mineralization degree values are determined as input, and the tailing grade soft sensor model is established by the multilayer feedforward perceptrons and VGG-19 neural network. A tailings grade soft sensor system has been developed and applied in a flotation workshop. The results of industrial tests show that this method is efficient and reliable.
科研通智能强力驱动
Strongly Powered by AbleSci AI