發(fā)布時間：2018-09-13 09:15

【摘要】：礦石粒度是礦物加工工藝的一項重要指標(biāo),磨礦作業(yè)需要對礦石粒度進行檢測,根據(jù)檢測結(jié)果調(diào)整相應(yīng)工藝參數(shù)。目前常采用篩分、沉降等常規(guī)方法進行粒度檢測,這些檢測方法耗時長、效率低,而且受檢測人員的主觀影響較大。針對以往檢測方法的不足,本論文采用基于數(shù)字圖像處理的檢測方法,經(jīng)過實踐證明,這種檢測方法能夠快速、準(zhǔn)確的對礦石粒度進行分析測量。本論文研究的主要內(nèi)容有:1.對礦樣原始圖像進行預(yù)處理,首先對原始圖像進行灰度化,接著對于目標(biāo)礦粒與背景不易區(qū)分的現(xiàn)象進行了對比度調(diào)節(jié),之后分析對比了兩種典型的濾波算法,選取中值濾波算法濾除了圖像噪聲。2.對礦樣圖像進行分割。對不同的分割算法進行了分析與對比,最后選用了基于閾值的圖像分割方法,成功的將目標(biāo)礦粒與背景分離。3.礦樣圖像的形態(tài)學(xué)處理。對分割后的圖像進行形態(tài)學(xué)處理,平滑圖像噪聲,填充由于礦粒反光而形成的孔洞。4.粘連礦粒的分割。在傳統(tǒng)分水嶺算法的基礎(chǔ)上,分析對比了幾種改進的分水嶺算法分割效果,最后采用了基于標(biāo)記符控制的分水嶺分割算法,成功將粘連礦粒分離開。5.對處理后圖像中的連通區(qū)域進行標(biāo)記,計算每個連通域中的像素個數(shù),通過比例換算,得到礦粒的實際粒度。6.完成軟件編譯,輸出粒度分布曲線,將軟件分析結(jié)果與篩分結(jié)果進行對比。通過實驗證明,本論文的檢測方法成功、有效的統(tǒng)計出了礦石的粒度分布,與傳統(tǒng)方法相比,基于圖像處理的檢測方法具有操作簡便、準(zhǔn)確高效的優(yōu)點,同時,對礦石粒度的在線檢測也取得了很好的效果,該檢測方法對提高磨礦作業(yè)效率以及推動選礦自動化的發(fā)展都具有重要意義。
[Abstract]:Ore particle size is an important index of mineral processing technology. Grinding operation needs to detect ore particle size and adjust the corresponding process parameters according to the test results. At present, conventional methods such as sieving and settling are often used to detect particle size. These methods are time-consuming, inefficient and subject to the subjective influence of the examiners. In view of the shortcomings of the previous detection methods, this paper adopts the detection method based on digital image processing. It has been proved by practice that this detection method can analyze and measure the ore particle size quickly and accurately. The main content of this thesis is 1: 1. Preprocessing the original image, first graying the original image, then adjusting the contrast between the target ore particles and the background, then analyzing and comparing two typical filtering algorithms. Select median filter algorithm to filter image noise. 2. The mineral image is segmented. The different segmentation algorithms are analyzed and compared. Finally, the threshold-based image segmentation method is used to separate the target ore particles from the background successfully. Morphological processing of mineral image. The segmented image is processed by morphology to smooth the noise of the image and fill the hole. 4. The division of mineral particles. Based on the traditional watershed algorithm, this paper analyzes and compares the segmentation effects of several improved watershed algorithms. Finally, the watershed segmentation algorithm based on marker control is used to separate the adhesion particles successfully. The connected region of the processed image is marked, the number of pixels in each connected domain is calculated, and the actual particle size of the ore is obtained by the scale conversion. The software is compiled, the granularity distribution curve is outputted, and the results of software analysis and screening are compared. It is proved by experiments that the detection method in this paper is successful, and the particle size distribution of ore is calculated effectively. Compared with the traditional method, the method based on image processing has the advantages of simple operation, accuracy and high efficiency, at the same time, The on-line detection of ore size has also achieved good results. This method is of great significance to improve the grinding efficiency and promote the development of mineral processing automation.
【學(xué)位授予單位】：昆明理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TD91;TP391.41

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本文編號：2240721