Egeerg Letters, 23:3, EL_23_3_08 Oxyge Supply Predcto Model Based o IWO-SVR Bo-oxdato Pretreatmet Ca, -yua Na, B-peg Gao Abstract Oxyge supply s a key parameter mportat for oxdzato rate of pulp ad the eergy cosumpto. Therefore, how to crease the use rato of oxyge ad reduce eergy cosumpto has become a mportat ssue the metallurgcal dustry. A IWO-SVR predcto model s proposed to overcome the metoed problem by takg advatages of both Ivasve weed optmzato (IWO) ad support vector regresso mache (SVR). IWO s appled to optmze the model s parameters. The, the sample data s classfed by dscrmat fuctos to predct oxyge supply. The expermetal results show the IWO-SVR approach obtas better predcto accuracy tha the stadard SVR approach. So, the proposed model s well suted to predct the oxyge supply the process of bo-oxdato pretreatmet. Idex Terms oxyge supply, support vector regresso, Ivasve weed optmzato, Predcto P I. INTRODUCTION ROCESS for bo-oxdato of gold s curretly oe of the most mportat pretreatmet o refractory gold ores wth strog compettve power ad brght prospect. I metallurgcal dustres ad mes, power cosumpto of ar compressor system has reached about 0%~20% of the total cosumpto[], eve to 20% bologcal oxdato pretreatmet[2]. The process of bologcal oxdato pretreatmet s to make gold exposed through bactera oxdzg ad decomposg pulp wth eough oxyge for further extracto of gold. Based o the experece, oxyge supply of each oxdato tak s adusted maually by reaso of complex bologcal oxdato process ad varable composto the tak. I ths way, t leads to low dssolved oxyge ad hgh eergy cosumpto of the system. So, how to crease the use rato of oxyge ad reduce eergy cosumpto has become a mportat ssue. To overcome ths problem, a effcet way s proposed to predct the oxyge supply of every tak usg process parameters data of the pretreatmet process. However, the complexty of the bochemcal processes has bee a great barrer to modelg the bo-oxdato pretreatmet accurately. Sce the study o bo-oxdato pretreatmet s relatvely less, oly the experece of lterature from relatve dustry Mauscrpt receved Jauary 3, 205; revsed March, 205. Ca s the School of Electrcal Egeerg, ag Uversty, Urumq, CO 83007 Cha (phoe: +86 573957733; e-mal: mystery.la@63.com). -yua Na s wth the School of Electrcal Egeerg, ag Uversty, Urumq, CO 83007 Cha (e-mal: xya@xu.edu.c ). B-peg Gao s wth the School of Electrcal Egeerg, ag Uversty, Urumq, CO 83007 Cha (e-mal: gbp_xd@sa.com ). ca be referred. Least squares support vector mache (LSSVM) was used to predct gas emssos wth ay chage geologcal codtos, producto processes or the evromet of the workg face[3]. I[], support vector mache based K-meas cluster was appled to predct temperature of molte ro blast furace. Compared wth mechasm model cocered slco cotet molte ro, the proposed method had a hgher accuracy. For multple correlatos ad varablty by tme amog aluma evaporato process parameters, Yag [5] used LSSVM wth adaptve weght for the MIMO system to predct export materal lqud desty ole. L[6] proposed the model of LSSVM mproved by artfcal bee coloy to predct oxdato reducto potetal the bologcal oxdato pretreatmet process, ad receved a better accuracy predcto. All of above researches lay the foudato for settg up the tellget predcto model of oxyge taks. Ivasve weed optmzato (IWO) [7]s a ovel ecologcally spred algorthm, proposed by Mehraba, whch mmcs the process of weeds colozato ad dstrbuto, ad has very strog robustess ad adaptablty. The algorthm s smple, easy to mplemet, ad ca effectvely coverge to the optmum soluto of problems. Sce ts cepto 2006, IWO has foud successful applcatos lke broadbad matchg etwork desg[8], ut commtmet problem[9], stock prce predcto[0], fault dagoss of aalog crcuts [] ad so o. I ths paper, a combato method of IWO ad SVR s preseted ad used for oxyge supply predcto. The remader of ths paper s orgazed as follows: Secto II gves the basc prcple ad mathematcal formulato of SVR. Secto III dscusses the theory ad method of IWO. The the IWO-SVR approach for oxyge supply predcto s proposed secto IV. Fally, coclusos are draw Secto V. II. THEORETICAL BACKGROUND A. Support Vector Regresso Support Vector Regresso (SVR)[2] s the exteso of SVM to solve regresso ad predcto problems. The regresso problem s to fer output y accordg to the gve ew put sample x. A smple descrpto of the SVR algorthm s provded as follows. Cosder a trag dataset {( x, y),,( x, y)}, xr R, whch s the total umber of trag samples. x s dmeso put vectors ad y represets output. The basc dea of SVR s to map the data to a hgher-dmesoal feature space va a (Advace ole publcato: 0 July 205)
Egeerg Letters, 23:3, EL_23_3_08 olear mappg ad the to do lear regresso ths space. So, the goal s to fd a fucto f ( x ) that gves a devato from the actual output y. The optmal decso fucto s wrtte as f ( x) ( w( x)) b () where w s a adustable weght vector, ( x) s the data features space ad b s scalar threshold. They ca be estmated by mmzg the regularzed rsk fucto 2 m( w C L( y f( x))) (2) 2 Subected to y f( x) 2 Where w s regularzed rsk whch cotrols the 2 fucto capacty; C L( y f( x)) s emprcal rsk, the parameter C s a regularzato costat tug the trade-off betwee the trag error ad the geeralzato performace. Ly ( f( x)) s loss fucto whch mpacts mmze effect of emprcal rsk ad decdes how to push the devato y f( x ). The error ( )-sestve loss fucto L ( y, f( x, w)) defed as Eq.(3) f y f () x L (, x y, f()) x (3) y f() x, otherwse The problem of fdg w ad b to reduce the emprcal rsk wth respect to a -sestve loss fucto s equvalet to the covex optmzato problem that mmzes the marg ( w ) ad slack varables (, ) as m( w C ( )) 2 2 () y f( x) Subected to f( x) y 0,2,, The above optmzato problem s solved by Lagrage multplers ad ts soluto s gve by f ( x) ( ) xx b (5) where b w( xr xs) ; 2 ad are Lagrage multplers; ad s the umber of support vectors. Nolear trasformato of SVR s realzed by defg the approprate kerel fucto K( x, x ) ( x ) ( x ) (6) The fucto of kerel fucto s to replace er product hgh dmesoal feature space as a brdge betwee lear ad olear. Thus t ca avod dmeso dsaster geerated by complex hgh dmesoal operato ad model dmeso rase. The form of f( x ) s smlar to a RBF eural etwork, whch s expressed as Fg.. Output s the lear combato of termedate odes. The basc dfferece betwee SVR ad RBF s theoretcal bases. The ceter of every bass fucto of SVR correspodg to a support vector, whch ad output weght are decded by algorthm automatcally. x x 2 x 3 x K( x, x) K( x 2, x) K ( x, x) Fg. Predcto model based SVR B. Ivasve Weed Optmzato Ivasve weed optmzato was developed by Mehraba ad Lucas 2006[7]. IWO s a populato-based meta-heurstc algorthm that mmcs the colozg behavor of weeds. Compared wth other algorthms, IWO s smpler ad has approprate capablty ad covergece rate to the global optmal pot of obectve fucto. Some of the dstctve propertes of IWO comparso wth other evolutoary algorthms are the way of reproducto, spatal dspersal, ad exclusve competto. The process s addressed detal as follows: () Italzato A certa umber of weeds are radomly spread over the D-dmesoal search space. Ths tal populato of each geerato wll be termed as {, 2,, }. x x x (2) Reproducto The hgher the weed s ftess, the more seeds t produces. Each member of the populato s allowed to produce seeds wth a specfed rego cetered at ts ow posto. The umber of seeds produced by x, {,2,, } depeds o ts ow ad the coloy s lowest ad hghest ftess: the umber of seeds produced by a plat vares learly from mmum to maxmum possble amouts of produced seeds. The procedure s show Fg.2. Namely, the umber of produced seeds for the th plat every repeat ca be calculated wth the followg equato: F F max S ( Smax Sm ) Sm Fm Fmax (7) where F s the curret weed s ftess. S max ad S m respectvely represet the maxmum ad the least value of a weed. F max ad Fm respectvely represet the maxmum ad the least ftess of the curret populato. (Advace ole publcato: 0 July 205)
Egeerg Letters, 23:3, EL_23_3_08 Fg.2 Seed producto procedure a coloy of weeds (3) Spatal dspersal The geerated seeds are radomly dstrbuted over the D-dmesoal search space by ormally dstrbuted radom umbers wth mea equal to zero, but wth a varyg 2 varace. Ths meas that seeds wll be radomly dstrbuted so that they abde ear to the paret plat. The posto of ew seed s gve accordg to t t ( ) ( ) t max pow m max m tmax (8) where tmax s the maxmum umber of teratos allowed, t s the curret terato umber ad pow represets the o-lear modulato dex. () Compettve excluso If a plat leaves o offsprg the t would go extct, otherwse they would take over the world. Thus, there s a eed of some kg of competto betwee plats to lmt the maxmum umber of plats a populato. Itally, the umber of weeds a coloy wll reach ts maxmum (pop_max) by fast reproducto. I ths stuato, every plat s permtted to produce seeds by accordace wth reproducto method. However, t s expected that by ths tme the ftter plats have reproduced more tha udesrable plats. From the o, oly the fttest plats, amog the exstg oes ad the reproduced oes, are take the coloy ad the steps to are repeated utl the maxmum umber of teratos (or fucto evaluatos) have bee reached. So, every geerato the populato sze must be less tha or equal to pop_max. Ths method s kow as compettve excluso ad s the selecto procedure of IWO. III. IWO-SVR IMPLEMENTATION FOR PREDICTION OF OYGEN SUPPLY The flow chart of bo-oxdato pretreatmet s showed Fg.3. Oxdato tak to 3 are used for the frst stage oxdato treatmet, tak ad tak 5 are respectvely used for secod ad thrd oe. Oxyge supply of oxdato tak at dfferet levels s decded by bacteral actvty, so t s affected by temperature, PH, pulp desty, oxdato reducto potetal (ORP) ad so o whch fluece the bacteral actvty[3]. Ad the data the collecto s olear, hgh dmeso, ostatoary ad so forth. Stadard support vector mache has features to overcome olear, hgh dmeso, but t s hard to solve other problems. So we propose a predcto model of oxyge supply based IWO-SVR ths paper, cosderg the followg ssues: Fg.3 The process of bo-oxdato pretreatmet (Advace ole publcato: 0 July 205)
Egeerg Letters, 23:3, EL_23_3_08 A. Data Processg I order to mprove predcto precso of the model, data collected oxdato tak eed to be processed advace. () Outler processg Compoet bologcal oxdato tak s complex, oxdato reactos betwee bactera ad pulp occur uder strog acd codto. So data acqured by sesors devates from the real value due to exteral dsturbace. It may extremely affect data structure ad dstrbuto of the whole system, f outler exsts. Wpg out the outler s a mportat way to mprove accuracy of the model. Accordg to the error processg crtero statstcal dscrmats, gve sample data ( x, x2,, x ), average s x, devato s v x x (,2,, ), calculatg stadard devato by Bayes formula 2 2 S [ v /( )]. If the devato v ( ) of oe sample data x satsfyg: v 3, x s cosdered as a outler whch should be elmated. (2) Normalzg parameters I order to mprove the calculato effcecy, ad prevet dvdual data from overflowg durg the calculato, put parameters should be ormalzed as follows: x m x x, 2,, (9) max x m x where x represets each put parameter, x accordgly represets ormalzed put parameter. (3) Correlato aalyss Data measured real tme of bologcal oxdato tak cludes temperature, PH, pulp desty, oxdato reducto potetal. From the aalyss of producto process, the above varables have multple correlatos, strog couplg relatoshp. Iflueces of every factor to oxyge supply are ot eglected. There are prcpal compoet aalyss (PCA) ad correlato aalyss to deal wth multple correlatos. I ths paper, support vector mache s proposed to model, t oly eed to kow the degree of relatoshp betwee varables. Usg Pearso correlato aalyss[], t s showed as follow: Table. Correlato coeffcet Y 2 3 Y -0.35 7-0.3 2 0.68 3 0.08 0.002-0.3-0.07-0.37 0.03 3-0.09 3 where respectvely represets PH, oxdato reducto potetal, temperature, pulp desty, Y represets oxyge supply. From Table, oxyge supply s postvely correlated to oxdato reducto potetal, temperature ad pulp desty, s egatvely correlated to PH. Accordg to the aalyss, oxdato reducto potetal, temperature, pulp desty, PH are chose as put, oxyge supply s output. () Cluster Collectg a mass of data affectg the oxyge supply, stll hudreds of data s obtaed after data preprocessg. It ot oly creases hard to tra SVM, but also flueces accuracy of predcto model. So t eeds to classfy the data ad K-Medod clusterg[5] s as follows: a) select k tal categorcal obects from the dataset ad assg them as tal medod of k-cluster; b) compute the dstace betwee every obect ad cluster ceter, dstrbute the obect to the cluster represeted by ceter accordg to the dstace. c) whe all obects are allocated to clusters. Recheck the smlarty of all categorcal obects wth curret medods assged to all clusters. If a categorcal obect s foud whch s earest to some other cluster medod the de-allocate from the curret cluster ad allocate categorcal obect to ew cluster & recalculate medod. d) repeat c) utl o obect has chaged clusters after a full cycle test of whole data set. (5) Dscrmat aalyss Dscrmat aalyss s to select varable whch ca provde more formato from the classfcato of the kow observato obects. Set up dscrmato fucto to mmze false rate of detfyg the category. I accordace wth the truth of varables effectg oxyge supply, Fsher dscrmat aalyss[6] s adopted to buld the fucto ad aalyzed whch class predctg data belog to. The the SVM model s appled to predct oxyge supply correspodg to the class. B. IWO-SVR Predcto Model Pealty parameter C, kerel parameter of RBF ad sestve loss parameter have a great effect o predcto results of SVR model. I order to get the SVR model wth better predcto performace, C,, are optmzed by other algorthm. Because of global ad local search of IWO, compettve excluso betwee chld ad paret to avod premature covergece ad the local mmum, IWO s proposed to optmze the above parameters. Cocrete steps are as follows: Step Collect data from a gold me, preprocess the data ad dvde t to trag ad predcto sample. Step2 Classfy the trag sample to 3 classes based o craft ad expertse, get fal cluster ceters. Step3 The trag process accordg to the theory metoed secto s appled to SVR wth the subset for trag, ad ts optmal decso fucto ca be obtaed. The the predcto process s performed wth the subset for testg. The error of predcto wll be obtaed. Step Optmzato for the parameters C,, are performed accordg to the prcple of IWO algorthm troduced secto 2. The error of predcto s used as the ftess fucto of IWO algorthm. The process of Step3 ad Step s repeated utl termato codtos are acheved. The a (Advace ole publcato: 0 July 205)
Egeerg Letters, 23:3, EL_23_3_08 predcto of oxyge supply ca be coducted wth the optmzed ad traed SVM. Step5 Buld dscrmat fucto wth Fsher dscrmat aalyss, dfferetate class of predcto sample wth the dscrmat fucto. Step6 Use the correspodg predcto model of the class to predct oxyge supply, ad obta the output. The predcto model s showed Fg.. oxdato tak trag sample cluster predcto sample dscrmat aalyss SVR predcto error Fg. IWO-SVR predcto model IWO C. Result ad Dscussos Choosg ole data from a gold me, 8 groups of data are fally obtaed after preprocessg. Former 380 groups are used for trag ad others for predcto. Trag sample s classfed to three categores by K-Medod cluster, ad cluster ceters are showed as follows: Table 2. Fal class ceter Class Class 2 Class 3 PH.83068.883 2.558 ORP(mV) 68.780 562.670 3 97.9972 Temperature( C ) 39.503 38.362 35.029 Oxyge supply 3 ( m / h ) 5 78 27 Establsh the dscrmatory fucto of classfcato o the kow observato obect, get lear dscrmat coeffcet by usg de-ormalzed Fsher dscrmato, showed Table 3. Table 3. Fsher lear dscrmat factor Class Class 2 Class 3 PH 953.953 8080.88 5.2 ORP 2.968 -.859-2.276 Temperature 83.72 06.069 68.56 Pulp desty 2553.96 235.92 732.69 (Costat ) Classfcato -8560.0 6-8933.33 6 Table. Classfcato results Predcto members for class Predcto members for class 2-258.5 7 Predcto members for class 3 200 0 0 2 0 35 5 3 9 2 30 Accordg to Table 3, the dscrmatory fuctos are: F 953.9532.9682 83.723 2553.96 8560.06 F 8080.88.859 06.069 235.92 8933.336 F3 5.22.2762 68.563 732.69 258.57 2 2 3 From the Table, the accuracy of classfcato s 96.3% to show feasblty ad effectveess of dscrmatory fucto. Oxyge supply (m 3 /h) Oxyge supply (m 3 /h) 700 650 600 550 500 50 00 350 300 IWO-SVR trag result orgal data trag data 250 0 20 0 60 80 00 20 0 60 80 200 Trag sample Oxyge supply (m 3 /h) 950 900 850 800 750 700 650 Fg.5. IWO-SVR trag results of the class IWO-SVR trag result orgal data trag data 600 0 20 0 60 80 00 20 0 Trag sample 30 320 300 280 260 20 220 200 80 60 Fg.6. IWO-SVR trag results of the class 2 IWO-SVR trag result orgal data trag data 0 0 20 0 60 80 00 20 30 Trag sample Fg.7. IWO-SVR trag results of the class 3 (Advace ole publcato: 0 July 205)
Egeerg Letters, 23:3, EL_23_3_08 Modelg IWO-SVR for every class, the parameters of C,, are eeded to optmzed by IWO, the result obtaed s tabulated Table 5. Table 5. Parameters of C,, Class Class 2 Class 3 C 0.330 0.89 0.57 0.57 9.9 8.50 0.022 0.00 0.0 From these parameters, the IWO-SVR matched curve of every class wth trag the sample, respectvely showed Fg.5, Fg.6, Fg.7. From above fgures, the predcto model of IWO-SVR has a strog learg ablty ad good trag result. 8 predctg samples are assged to the class whch belogs to by dscrmat aalyss. The the sample s appled to predct oxyge supply by meas of IWO-SVR model of the class. Ths predcto method compares to SVR model[7] wth the same data, takes 00 samples of class for example, the result s showed as follows: Oxyge supply (m 3 /h) Error (m 3 /h) 700 650 600 550 500 50 00 Predcto result orgal data IWO-SVR predcto SVR predcto 350 0 0 20 30 0 50 60 70 80 90 00 Test sample 200 50 00 50 0-50 -00 Fg.8. Comparso of the predcto effect Error IWO-SVR predcto SVR predcto -50 0 0 20 30 0 50 60 70 80 90 00 Test sample Fg.9. Comparso of the error effect Fgure 8 gves a more sgfcat ad comprehesve vew of the performace of the IWO-SVR by showg the oxyge supply tred of class. Moreover, IWO-SVR model obvously has the lower error of oxyge supply predcto tha SVR model Fg.9. The performace evaluato of the basc ad the mproved SVR algorthm s showed the Table 6. The IWO- SVR model s superor to SVR deed. Table 6. The performace wth IWO-SVR, SVR Method Tra Test Accuracy MAPE RMSE MAPE RMSE IWO-SV R 0.265.039 3.5572 67.75 SVR 2.9963 58.0529 3.8378 73.08 IV. CONCLUSION I ths paper we propose a IWO-SVR model, whch combes IWO ad SVR, for predctg oxyge supply of oxdato taks. From expermetal results, the IWO-SVR model ca acheve a better predcto performace compared to the SVR. We cosder the tellget model cotrbutes to adust oxyge supply automatcally to esure hgh oxyge rate ad reduce eergy cosumpto ACKNOWLEDGMENT The authors would lke to ackowledge the support of the Natural Scece Foudato of Cha (N0. 66307). REFERENCES [] Y. Zhao,. Da. Dscussos o Eergy-savg the Ar Compress System of Udergroud Mg, Yua Metallurgy,Vol.35, pp.3-5,2006. [2] R. Sadur, M. Hasauzzama, N. A. Rahm. Eergy, Ecoomc, ad Evrometal Aalyss of the Malaysa Idustral Compressed-ar Systems, Clea Techologes ad Evrometal Polcy, Vol., pp.95-20, 202. [3].Y. Gog, et al. The Research of Gas Predcto Based o LS-SVM ad ICA, Joural of Computatoal Iformato Systems, Vol.0, pp.8273-8283, 20. [] G.M. Cu, T. Su, Y. Zhag. Applcato of Support Vector Mache (SVM) Predcto of Molte Iro Temperature Blast Furace, Cotrol Egeerg of Cha, Vol.20, pp.809-82, 203. [5] C.H. Yag, et al. Aluma Evaporato Cocetrato Predcto Based o Adaptve Weghted LS-SVR, Cotrol Egeerg of Cha, Vol.9, pp.87-90, 202. [6] W. L,.Y Na. Research o Predcto of Oxdato Reducto Potetal Based o Improved ABC ad LSSVM Algorthm, Computer Measuremet & Cotrol, Vol.22, pp. 395-398, 20. [7] A. R. Mehraba, C. Lucas. A ovel umercal optmzato algorthm spred from weed colozato, Ecologcal Iformatcs, Vol., pp.355-366, 2006. [8] H. Wu, C. Lu. Broadbad matchg etwork desg for ateas usg vasve weed optmzato algorthm, Cyber-Eabled Dstrbuted Computg ad Kowledge Dscovery, 203 Iteratoal Coferece o, IEEE, 203, pp.500-503. [9] B. Saravaa, E.R. Vasudeva, D.P. Kothar. A Soluto to Ut Commtmet Problem Usg Ivasve Weed Optmzato Algorthm, the 203 Iteratoal Coferece o Power, Eergy ad Cotrol, ICPEC 203, February 6, 203. [0] H. Lu,. Lv. Stock Prce Predcto Model Based o Modfed IWO Neural Network ad ts Applcatos, Bo Techology: A Ida Joural, Vol.0, pp. 398-3930, 20. [] S. H. Ca, et al. Applcato of IWO-SVM approach fault dagoss of aalog crcuts, Cotrol ad Decso Coferece (CCDC), 203 25th Chese. Guzhou, May, pp.786-79, 203. (Advace ole publcato: 0 July 205)
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