Fast Analysis Of Coal Property Based On Machine Vision

Coal separation efficiency and economic benefit were increased with thescientific and technological development and the increasing automation degree of coalpreparation. However, the automatic controls of domestic coal preparation plants werestill stayed as several feedback control phases, such as automatic launching andstopping machine, liquid level control of medium density barrels, automatic dosingcontrol, real-time ash monitoring. The real-time monitoring and controlling of thewhole production processes has not been realized. The main reason is the afunction ofreal-time monitoring of raw coal and products quality. Hence, this paper proposed themethods of fast analysis of coal property based on machine vision, mainly includingsize distribution analysis, density distribution analysis and ash content analysis.Tai-xi anthracite was taken as research object, and a fast analysis system of coalproperty were built for experiments. According to the need of fast analysis of coalproperty, three image segmentation methods of coal particles were proposed,including segmentation method of non-touching coal particle image,local-segmentation method of coal pile image and whole-segmentation method of coalpile image. Segmentation method of non-touching coal particle image mainly aimedto backlit images of non-touching coal particles, and two-peaks method, areathreshold, hole-filling method were used to segment coal particles accurately.Local-segmentation method of coal pile image is a semi-automatic segmentationmethod, combined with drawing the outline of the target region by manual and colorimage segmentation method. Related algorithms also include morphologicalprocessing, watershed edge processing, area threshold method and minimumcircumscribed rectangle interception. Whole-segmentation method of coal pile imageused CLAHE, minimum and maximum filter algorithms to enhance image.Multi-scale linear filter by Hessian matrix and Gaussian function was used to detectthe coal particle edges, and the effect is better than traditional edge detectionalgorithms. Finally double-threshold edge connection and marked watershedalgorithm were taken to identify the coal particle region. The first two imagesegmentation methods were mainly used to establish the estimated models accurately,and the third image segmentation method was used for fast analysis of coal property.Meanwhile, a segmentation effect quantitative method of coal pile images wasproposed, and the error percentage of the above segmentation method is12.76%.In order to estimate the3D information of coal particle from its2D information, a mass estimation model for coal particles was established in this paper. Actual sizeand measured size by image processing were contrasted and analyzed, showing theimage measuring method is accurate. The exponential relationships between area andperimeter, minimum circumscribed rectangle length and breadth were found and threeexponential models were established by least square method. Thickness estimationmodel of coal particles was established and improved by multiple linear regressionmethod, and then mass estimation model of coal particles were proposed with areaand density of coal particles. Test results indicated the absolute errors of estimatedmass of coal samples are less than6%.A fast analysis method of size distribution of coal piles by machine vision wasproposed. Ten size features were contrasted and analyzed, and then the breadth ofminimum bounding rectangle were determined as the best particle sizecharacterization of coal particles. Through establishing the size-fraction probabilitymodel of surface coal particles, a surface overlapping error correction method wasproposed. R-R granularity characteristic equation was used to explore the innerrelationship between equation parameters of the whole coal pile and the surface, andthen a granular segregation error correction method was proposed. Combined withabove researches, an analysis method of size distribution prediction of coal piles wasproposed, and the results indicated the more test times, the smaller prediction errors.The above two correction methods were useful to reduce the prediction errors. Thehighest error of the first twenty estimated results of coal pile size distribution is3.79%,and the lowest error is0.03%.A fast analysis method of density distribution of coal piles by machine visionwas proposed. Fifty color, luster and texture features were extracted and processed byoutlier detection and standardized treatment. Box-plots were used to analyze thevariation tendency of all features with the increasing of size fractions and densityfractions, and then selecting all the features initially. KPCA and GA were used tooptimize the left features, and results indicated GA is more suitable for featureselection. SVM is better than BP, RBF and PNN to predict the density fraction of coalparticles. The prediction accuracy of narrow size fractions is much higher than thewhole size fraction, and the bigger size fractions, the higher prediction accuracy.Combined with above researches, an analysis method of density distributionprediction of coal piles by each narrow size fraction model was proposed, and theresults indicated the more test times, the smaller prediction errors. The highest error of the first twenty estimated results of coal pile density distribution is8.03%, and thelowest error is0.87%.A fast analysis method of total ash content and ash content of each densityfraction of coal piles by machine vision was proposed. Through establishing thequadratic polynomial model of ash content and density of coal particles, the variationtendency of features with the increasing of ash content should be consistent with thatof features with the increasing of density. GA method was used to select the features,and SVM was used to establish the prediction model of ash content. Results indicatedthe prediction accuracy of narrow size fractions is higher than the whole size fraction,and the bigger size fractions, the higher prediction accuracy. SVM model is betterthan BP and RBF models in ash content prediction. Combined with above researches,an analysis method of total ash content and ash content of each density fraction ofcoal piles by each narrow size fraction model was proposed, and the results indicatedthe more test times, the smaller prediction errors. The highest error of the first twentyestimated results of coal pile density distribution is3.39%, the lo west error is0.22%,and the average error of total ash content is0.73%.Pilot-scale tests of fast analysis of coal property by machine vision were carriedout in a preliminary attempt. An online washability prediction system of raw coal andan online ash content prediction system of ultra-pure coal were designed andestablished in ShenHua Ningmei Taixi coal preparation plant. Test results indicatedthe fast analysis methods of coal property are feasible and the above two systems areable to satisfy the prediction requirements of coal preparation plant basically. Thereal-time prediction absolute errors of size distribution, density distribution and ashcontents are all less than10%and the washability curves is able to show in real time.The real-time prediction absolute error of ultra-pure coal ash contents is0.12%. Theprimary applications show the availability of analysis methods proposed in this paper.System stability and prediction accuracy should be improved and enhanced in futureresearch.