Coal slime flotation clean coal ash content prediction method based on deep learning

Abstract

The invention discloses a coal slime flotation clean coal ash content prediction method based on deep learning. The method comprises the following steps: assembling an image acquisition hardware system; collecting a coal slime flotation froth image and corresponding ash content data; dividing the data set into nine types according to the + / -0.5 interval of the gray unit digit, and performing dataenhancement; adopting a resnet50 network to extract foam surface features, adopting a random gradient descent process to update parameters and softmax function classification, obtaining a high accuracy rate through multiple times of iterative training of a model, and finally making suggestions for on-site working conditions according to prediction results. Compared with manual subjective observation operation, the method has the following advantages: representative high-order abstract detail features can be automatically extracted along with continuous optimization of the model; and in addition, compared with a traditional method, the method has the following advantages: the modeling time is greatly shortened, high-order abstract features are extracted through a convolutional network, a training sample input into the model is more real, an obtained prediction result is more accurate, and the method has an important guiding effect on flotation field production.

Description

technical field [0001] The invention relates to a monitoring of ash content of flotation clean coal, in particular to a method for predicting ash content of slime flotation clean coal based on deep learning, and belongs to the technical fields of foam flotation, deep learning and machine vision. Background technique [0002] Foam flotation is the preferred method for fine-grained coal slime separation in today's coal preparation plants. It is a process in which clean coal and gangue minerals are separated by means of the buoyancy of air bubbles in the slurry according to the differences in physical and chemical properties on the surface of mineral particles. A very important part of the process. Due to the quality of coal and the different working conditions, the ash in the flotation product fluctuates, so it is necessary to detect the ash in time and adjust the process parameters. However, there are many processes in the flotation workshop at present, and the adjustment la...

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Problems solved by technology

[0005] In order to solve the technical problems of difficult and low-precision online automatic monitoring of flotation clean coal ash in coal preparation plants, the present invention provides a prediction method for coal slime flotation clean coal ash based on deep learning

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