A study on Fast Predicting the Washability Curve of Coal

Transcription

1 Avalable onlne at Proceda Envronmental Scences (20) A study on Fast Predctng the Washablty Curve of Coal Zhang Ze-ln, Yang Jan-guo, Wang Yu-lng, Xa Wen-Cheng, Lng Xang-yang Chna Unversty of Mnng & Technology, Key Laboratory of Coal Processng and Effcent Utlzaton, Mnstry of Educaton Xuzhou,22008, Chna zhangzeln380@63.com Abstract A pure new MATLAB-based mage recognton system was developed to compute the coal partcle pcture of same gran class through the dgtal mage processng method, 3 mage feature parameters was selected to be most representatve mage characterstc parameters. Take the above parameters as the nput of RBF neural network, the densty level of coal partcles could be estmated, combned wth the real ash content of each densty level, the washablty curve could be drawed. Experement show,the absolute error of the total ash s 2.375%,whch s Slghtly bg n the Chna standards of coal preparaton (GB/T ); the related coeffcents of each ndcator n both actual and predcted float-and-snk materal are all close to, whle the curves of λ, β, θ and δ are very smlar and the devaton of ξ curve s relatvely large. 20 Publshed by Elsever Ltd. Selecton and/or peer-revew under responsblty of the 20 Publshed by Elsever Ltd. Selecton and/or peer revew under responsblty of [name organzer] Intellgent Informaton Technology Applcaton Research Assocaton. Open access under CC BY-C-D lcense. Keywords:Dgtal coal preparaton; mage analyss; neural network; washablty curve. Introducton In the mneral processng feld, many processes should be analyzed and judged manly by vsual nformaton, such as use the mcroscope to observe and utlze the mage to analyze the physcal dmenson, shape, color, dssocaton degree, ntergrowth, mneral type and content of mneral partcles. Use the "computer vson" replace human vson, enhance the applcaton of dgtal mage technology n mneral processng, and apply the latest contemporary technologcal achevements of dgtal mages to promote the development of mneral processng technology, all of whch have a very mportant gudng sgnfcance on the coal preparaton ndustry.[] Coal s the man part of Chna's energy and n qute a long tme Chna's coal-domnated energy structure wll not change. In nearly 30 years, the coal wll contnue to domnate the leadng poston n the producton and consumpton of prmary energy sources. However, n the use of coal, there stll exsts low effcency of energy and envronmental polluton as well as other ssues, so the only way to resolve ths contradcton s to develop clean coal technology. Coal washng and processng s the preferred soluton recognzed nternatonally to realze the coal effcent and clean use and t s stll one of the man content to develop clean coal technology. The so-called washablty means the complexty of selectng products from raw materals accordng to the requred qualty ndcator. The washablty curve s drawn n accordance wth the results of the float-and-snk test, whch s used to reflect all the densty level or any densty dstrbuton of coals and t s the necessary mean to understand the washablty, evaluate, predct, and optmze the effect of gravty separaton of raw coal. Besdes, t can provde correct way for the coal washng and processng as well as effectve way of supervson and management, so t has a pvotal poston n the coal washng and processng ndustry. The float-and-snk experment should not only cost a lot of manpower and resources, but also take a long tme. For ths, the coal preparaton plant commonly conducts the comprehensve test once a month. In the daly producton, they sometmes carry out a quck float every one hour to gude the producton. For the quck float has an hour lag, so t cannot opportunely gude the producton. Besdes, operators manly operate by experence, whch has large blndness and cannot automatc control accordngly. Therefore, how to rapdly predct the washablty curve of raw coal has become an urgent ssue. In 996, Max Lu started to study the relatonshp between coal ash and t s float-and-snk composton and also establshes the model to predct the float-and-snk composton of the raw coal [2]; In 998, Jng Lu and Max Lu predct the the washablty of the raw coal through the total ash content [3]; n 999, Zhenchong Wang & Max Lu developed a set of onlne system to predct coal washablty curve based on the relatvty between ash and densty of the raw coal from some gven coal preparaton plants. However, ths system only lmted to the specfc coal qualty and densty range and cannot acheve the ndustralzaton [4]. Ths paper uses the dgtal mage processng method to extract the Publshed by Elsever Ltd. Selecton and/or peer-revew under responsblty of the Intellgent Informaton Technology Applcaton Research Assocaton. Open access under CC BY-C-D lcense. do:0.06/j.proenv

2 Zhang Ze-ln et al. / Proceda Envronmental Scences (20) surface nformaton of coal partcles n each densty level, and then utlzes neural network to predct washablty curve. Ths method doesn t lmt to coal qualty and densty range, so t can acheve the purpose of automatc control and gudng producton quckly. 2. Coal qualty characters of expermental coal The coal used n ths study s the coal partcle wth gran sze of 3mm-25mm n the Tax Coal Preparaton Plant of Chna. When conductng the screen analyss on the raw coal of Tax, we also carry out the lthotype proxmate analyss on the raw coal n varous densty levels. Table. accordng to ts geness, lthotypes of coal dstngushed from chemcal nature and rock property and ts vsual characterstcs Composton of coal vsual characterstcs vtran glance coal dull coal fust brght,black,generally very brttle and often wth cracks Sem-brght,black,thn layer dm,black or gray-black,hard,rough surface slk gloss,black,nemalne,soft,very frable After the float-and-snk analyss on the expermental coal, we dvde t nto 0 densty levels: and >.90 Through the observaton on the varous components of float-and-snk experment, t can be seen that the vtran s manly concentrated n the densty level of.35 ~.40 Kg / L; dull coal s manly concentrated n the densty level of.40 ~.50 Kg / L; fusan s manly concentrated n the densty level of.40 ~.60 Kg / L; mneral-rch coal s manly concentrated n the densty level of.60 ~.80 Kg / L; ash mnerals (gangue) are manly concentrated n the densty level of +.80 Kg / L. By Table and the macro-components of denstes, whch can be seen that there exst large dfferences for expermental coal n 3mm-25mm and varous densty levels, besdes varous macro-compostons are also dfferent n vsual characterstcs, so usng the mage analyss method can predct the densty level of coal partcles n the same gran szes. 3. Identfcaton system of coal partcle mage Use self-developed MATLAB-based mage recognton system of coal partcles to extract the characterstc parameters of the coal partcle mage, whose functons are as follows: ) Image Acquston: t can be used to easly ntercept and load mages. 2) Image preprocessng: conduct the gray-scale processng on the coal partcle mage, contrast enhancement, threshold bnaryzaton, dlaton and eroson preprocessng; use the prncple of color segmentaton to dentfy and separate the coal partcle mage, and calculate cross-sectonal area of the coal partcles. 3) Image analyss and operaton: extract 29 characterstc parameters of coal mages - n the color mages, extract the frst-order, second-order and thrd-order (e mean, standard devaton and gradent) of color component hue saturaton and value by two reference systems---rgb and HSV; obtan the frst, second and thrd-order moment of gray scale from the gray mage, the contrast, correlaton, energy, homogenety, entropy, coarseness, Tamura-contrast and drectonalty of texture parameters. 4) Data storage and processng: save the feature parameters of coal partcle mage nto Excel tables, and then conduct flter the characterstc parameters and re-storage through the analyss of statstcs and graphs. 5) RBF neural network predcton: regardng the feature parameters of fltered mage as nput, the average densty level of the coal partcles as the tranng objectve, we can tran the network to predct the densty level of coal partcle, and then combned wth the real ash content of varous densty levels to predct the washablty curve. 4. Predctng the washablty curve After flter the characterstc parameters, the most representatve mage feature parameters of 3mm-25mm coal partcles n the Tax Coal Preparaton Plant are the frst-order of grayscale, second-order of grayscale, thrd-order of grayscale, frst-order of hue, second-order of hue, thrd-order of hue, second-order of saturaton, thrd-order of saturaton, contrast, energy, homogenety, entropy and drectonalty. Randomly select 0 groups of coal partcles n 3mm-25mm from the raw coal n Tax Coal Preparaton Plant. Select at random 5 coal partcles from each group, that s, each densty level, extract the fltered characterstc parameters of coal partcles, and then regard the normalzed average value of characterstc parameter of each coal partcle n each densty level

3 582 Zhang Ze-ln et al. / Proceda Envronmental Scences (20) (as shown n Table 2) as the nput of RBF neural network, whch can effectvely reduce the error caused by the dfference n the coal surface. Table 2. the normalzed average value of characterstc parameter n each densty level densty parameter >.90 G frst order G second-order G thrd-order V frst -order V second-order V thrd-order S second -order S thrd -order contrast entropy energy homogenety drectonalty In Table 2, regard the normalzed characterstc average value as the nput of RBF neural network and the average densty of each densty level as the tranng objectve, and then enter the characterstc parameters of 40 coal partcles n dfferent gran szes to conduct predcton. Assume the coal grans physcal volume V of the same sze s constant,the ash content A of coal partcles n each densty level s constant, the densty level s, the number of coal partcles n each densty level s j, the productve rate γ of Ith densty level s: V( V( j = j= ρ j) ( = j ρ )( j = j= ρ ) j j= j= = 0 0 j γ j ρ ) j Related coeffcent, also known as the Pttler product-moment related coeffcent, ndcates that the statstcal analyss ndcators of the ntensty of the correlatvty between two phenomenon. Related coeffcent of the sample s shown as r, the greater the value of r s, the smaller the error Q wll be, and also the hgher the lnear correlaton degree of varable X, Y wll be; when the value of r s closer to 0, Q s greater, and the lnear correlaton degree between varables X, Y s lower. s the data dmenson of varable X, Y. X Y XY r = 2 2 ( ) ( ) 2 X 2 Y ( X )( Y ) Table 3. Actual and predcted washablty curve data Curve Classfy Actual Characterstc ash Cumulatve floats Cumulatve snks curve λ curve β curve θ Densmetrc curve δ δ+/-0. curve ε Productve Productve Productve Productve

4 Zhang Ze-ln et al. / Proceda Envronmental Scences (20) Predcted Related coeffcent r Accordng to the natonal standards of coal preparaton n Chna--GB / T [5], when the ash content s larger or equal to 20%, the absolute error of the fltered gross sample ash content and weghted average of ash content of each gran sze can not exceed 2%. It can be seen from Table 3, when the ash content A > 20%, the absolute error between actual ash content and predcted ash content s 2.375%, although bgger n naton standard, have already neared to very. In addton to the low correlaton coeffcent of the productve rate n each densty level of ε curve, the correlaton coeffcent of the actual and predcted washablty curve data s close to, ndcatng that they have a hgh degree smlarty. Fgure. the actual washablty curve and the predcted washablty curve In fgure, the smlarty of actual and predcted curves s consstent wth the data shown n Table 3. As there s a certan msmatch rate for the predcton of densty level of coal partcle, the predcton of the productve rate for each densty level wll defntely have a certan devaton. However, the experment proves to have less nfluence on λ, β, θ and δ curve, but a greater mpact on the ξ curve. 5. Conclusons As can be seen from the above study, usng the dgtal mage processng method to forecast the washablty curve s feasble and the predcton on the total ash have already neared to the Chnese natonal standard as well as actual and predcted washablty curve have a hgh degree smlarty. Ths research wll provde a knd of all new way of thnkng and method for real-tme control and dgtal coal preparaton, then more effcently realze the polcy of coal effcent and clean use. 6. Acknowledgements Thank and acknowledge the support under The Creatve Research Groups Scence Fund of the atonal atural Scence Fund Commsson( ).

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