Flotation dosing abnormity detection method based on NSST morphological characteristics and depth KELM

Abstract

The invention relates to a flotation dosing abnormity detection method based on NSST morphological characteristics and depth KELM. The method comprises the following steps: firstly, acquiring a bubble image on the surface of a flotation tank in real time, and decomposing the image NSST to obtain a low-frequency sub-band image and a multi-scale high-frequency sub-band; secondly, binarizing the low-frequency image to extract bubble bright spots, calculating the number, the area, the standard deviation and the ellipticity of the bright spots, and calculating the fractal dimension, the mean value and the variance of high-frequency sub-band coefficients of all scales to form multi-scale morphological characteristics of the bubble image; then, on the basis of the KELM algorithm, a deep KELM is constructed by referring to a deep learning idea, quantum calculation is introduced into optimization of a genetic algorithm and used for optimizing parameters of the deep KELM, and an adaptive deep KELM is constructed; and finally, establishing a flotation dosing abnormity detection model through the multi-scale morphological characteristics and the self-adaptive depth KELM. The average recognition rate and the operation efficiency are obviously higher than those of an existing detection method, the requirement for flotation production on-line detection is better met, and a foundation is laid for follow-up dosing automatic control.

Description

technical field [0001] The invention relates to a flotation dosing abnormality detection method based on NSST morphological features and depth KELM. Background technique [0002] In the mineral flotation process, the flotation agent is one of the most critical control quantities. The quality of the dosage directly affects the mineral processing production indicators. relevant. When the dose is normal, the size of the bubbles is moderate, the size distribution is uniform, and the circularity of the bubbles is high; when the dose is over, the bubbles are severely hydrated and have strong fluidity, mainly small-sized bubbles; Higher, the circularity of the bubbles is low, and a large number of bubbles merge. At present, the concentrator mainly adopts artificial naked eyes to observe the changes of the characteristics of the air bubbles on the surface of the flotation tank to adjust the dosage, and the judgment and control lag, and the subjective randomness is large. [0003]...

AI Technical Summary

Problems solved by technology

However, the input weights and hidden layer bias of ELM are randomly selected, the number of hidden layer nodes is difficult to determine, and problems such as overfitting will directly affect its stability and generalization ability.

Therefore, Huang et al. introduced the kernel function into the ELM algorithm and proposed the kernel extreme learning machine (KELM), which enhanced the generalization performance of the algorithm, but at the same time its performance was easily affected by the penalty coefficient C and the kernel function σ

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