Study on Productive Process Model Basic Oxygen Furnace Steelmaking Based on RBF Neural Network

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1 IJCSI Internatonal Journal of Computer Scence Issues, Vol., Issue 3, No 2, May 24 ISSN (Prnt): ISSN (Onlne): Study on Productve Process Model Basc Oxygen Furnace Steelmakng Based on RBF Neural Network Zhhong Ma, Qan Zhou 2, Pen Lu 2 and XuDong Zhang 3 Basc Scence Department,Tann Agrcultural Unversty, Tann 3384 Chna 2 Department of food Scence, Tann Agrcultural Unversty, Tann 3384 Chna 3 School of Mechancal and Electrcal Engneerng,Tann Agrculture Unversty, Tann 3384,Chna Abstract The endpont temperature and carbon content of basc oxygen furnace (BOF) are the control of the BOF steelmakng process. There exsts a complex relatonshp among them and each control varable. Whle the multple lnear model s lmted to predct the endpont temperature and carbon content through each control varable, and the onlne contnue measurement can t be made. So the predctve model of some control varables of the BOF steelmakng based on RBF neural network was put forward, and the study of verfyng model was made by comparng the predctve value wth the practcal data of 89 converters n a factory. It turned out that the method has hgh accurate predcton, and t can be used n the process of predcton n steel enterprses. Keywords: RBF neural network,the multple lnear model, Natural language forecast the fnsh, the same, and Youlang Yang Zhao Xu [3] and others RBF neural network s used to research of end phosphorus content n forecast. But n the actual operaton process, the qualfed endpont temperature and carbon content of molten steel are the ultmate goal of the operaton (value), n order to acheve the desred goal, we need to determne the sublance after the relevant control varables of the optmal value n the process of steelmakng, therefore we propose a RBF neural network model s establshed to solve the end goals determne, some relevant control varables unknown producton process operaton problem, made out to the end of the molten steel temperature and carbon content wth ther mnmum devaton between the set pont.. Introducton The old problem of converter steelmakng are operatng accordng to the producton process of relevant control varables (oxygen or oxygen blowng tme, the qualty of the coolant and addtve, etc.) to predct the endpont temperature and endpont carbon content. Such as Yongfu Wang Xaopng L [] and others usng the hybrd model dynamc process was studed for the end pont, Tanyou Cha [2] by usng the mproved RBF neural network to 2. Modelng 2.The multple lnear regresson model The BOF steelmakng s a very complcated process, so n order to meet the requrements of qualfed molten steel, the endpont temperature and carbon content(followng we use ET and ECC to nstead) of BOF must be precse control. By establshng multple lnear regresson model to determne the relatonshp among the ET and Copyrght (c) 24 Internatonal Journal of Computer Scence Issues. All Rghts Reserved.

2 IJCSI Internatonal Journal of Computer Scence Issues, Vol., Issue 3, No 2, May 24 ISSN (Prnt): ISSN (Onlne): ECC and each control varable whch ncludng the qualty of molten ron and scrap steel, steel temperature at the tme of the sublance, ECC at the tme of the sublance, oxygen blowng, the coolant and the qualty of the addtves etc. 2.2.Data processng Our model was based on the practcal data of 89 converters n a factory. In the process of BOF, there may exsts several orders of magntude n the qualty of nputtng materal, because the amount of molten ron and oxygen blowng amount value s compared commonly bg, whle the amount of coolant s small. So n order to overcome ths shortcomng, we should deal wth the orgnal data by standard. The followng s the standardzed formula: x ( ) ' k x x ( k) x p p p x ( k) k ( x ( k) x ) (, 2,, k, 2, p) ; 2 () s correspondng to the nput varables,, k s correspondng to the sample. Due to the steel n the process of convertng, some abnormal data mght be occurred, because of spllage or furnace of mproper operaton, and these part of data are free outsde the man body of data, they also do not agree wth the exstence of other data, whch wll affect the establshment of the multple regresson model, so we have to modfy or remove data. Frst of all, usng the herarchcal clusterng method[4] to fnd out the abnormal data and remove them accordng to the smlarty. Then usng Chauvenet crteron[5] dose the same as the herarchcal clusterng method, at last there reman 78 converters steel data. On the bass of these data, lnear regresson model s establshed. 2.3Soluton of the multple lnear regresson model In ths model, we use and to represent the ET and ECC, and these two varables have the same mpact factors whch ncludng the qualty of molten ron, the qualty of scrap steel, oxygen blowng, lme stone, grey, Magnesum the ball, Dolomte.However, the eghth varable steel temperature at the tme of the sublance have a relatonshp wth ET, and ECC at the tme of the sublancehave a relatonshp wth ECC. The multple lnear regresson model of ET and ECC can be represented by the followng formula: Y X X... X t Y X X... X c ' ' ' ' ' (2) (3) Accordng to the processed data, we can get the regresson model by usng MATLAB software programmng: Y X.725 X.7555 t 2 X.243X.3558 X.625 X X.796 X (4) 7 8 Y X +.43X c 2 X +.363X.75 X +.97 X X X (5) 2.4 Error analyss 7 8 We do error analyss n the regresson equaton of ET. By usng MATLAB software programmng can get avalable basc ndex error and resdual analyss dagram. Copyrght (c) 24 Internatonal Journal of Computer Scence Issues. All Rghts Reserved.

3 X c ) are IJCSI Internatonal Journal of Computer Scence Issues, Vol., Issue 3, No 2, May 24 ISSN (Prnt): ISSN (Onlne): Resdual Case Order Plot quadratc functon and so on. Ths artcle chooses the followng Gaussan functon: 2 2 ( v) exp( v / ) (7) Resduals Case Number Fg. Multple lnear resdual analyss From the program we can know the correlaton ndex =.884, F=53.329, the sgnfcance level p=<.5, so the regresson equaton of the molten steel ET was establshed. Smlarly, we get some related parameters of steel ECC of the regresson equaton, the correlaton ndex =.746, F=2.256, the sgnfcance level p=<.5, t was establshed, too. 3.RBF neural network RBF neural network s a forward network of three layers, ncludng the nput layer, hdden layer and output layer. Set the number of nput nodes s n, the hdden nodes are m, and the output nodes are l. Settng the nput of RBF neural network s, the output of the network: y w w ( X c ) m w Mathematcally speakng, w (6) R s offset tem, R(,2,, m) are weghts from the hdden layers to the output layers; ( bass functon; c R n X c radal are the European norm; are the center of the network. The radal bass functon, ( X c ) has many forms, generally ncludng the thn plate splne functon, Gaussan functon, Multple Mathematcally speakng, s a real constant to decde the shape of Gaussan functon. 3. Productve Process Model of BOF Steelmakng We establsh a neural network wth some known relevant varables and the end target value n productve process as shown n fgure 2. We take the ntal qualty of molten ron.746, scrap steel qualty.462, steel temperature at the tme of the sublance.528, ECC at the tme of the sublance.222, ET and ECC of molten steel are.6224 and.252 wth producton experence as the bass. Substantally, the termnal optmal control model s Mult-obectve optmzaton problem whch s based on predcton model, for the amount of nput varables ncludng lqud ron and varous accessores, and the output varables ncludng the ET and ECC. In our subect, optmzaton goal s to acheve the mnmum dstance from the predct carbon temperature value and the settng temperature value. In order to smplfy the problem, we use the weghted operaton to lnk two obectves nto one goal. Shown by the followng formula: f ( C, T) c C C t T T (8) f ( C, T) s optmzaton functon, the mnmum s the optmal value of the multobectve optmzaton. C and T respectvely stand for ECC and temperature whch are C calculated by predcton model, and T respectvely stand for preset ECC and temperature, c and t s equal to, then stand for weghts. Ths paper references to the graduate dssertaton of Bngyao Ca [7], The converter steel-makng endpont carbon temperature Copyrght (c) 24 Internatonal Journal of Computer Scence Issues. All Rghts Reserved.

4 c IJCSI Internatonal Journal of Computer Scence Issues, Vol., Issue 3, No 2, May 24 ISSN (Prnt): ISSN (Onlne): predcton and control key technology research, values for.4, t values for.6. Mn f ( C, T).4 C C.6 T T (9) Fg.2 Neural network of Operaton Process 3.2 Model applcaton We take the actual data as the network tranng data from 78 furnaces n.2, f ( C, T ) s the evaluaton standard n network test. Oxygen absorpton Lmestone f ( C, T) stands for the greatest degree to close to the molten steel of ET and ECC under requred values. We use the MATLAB to solve ths problem. Learnng speed plays an mportant role n RBF neural network, we need to add the number of tranng because learnng speed s too slow to study; on the other hand, learnng speed s too fast, t s easy to cause the network learnng convergence,then affect the tranng accuracy. After tranng the model agan and agan, we get the best effect when vector s., number of tranng for 3 tmes, the tranng accuracy of.. The results are shown n table. Table The analyss table of neural network optmal results Grey Magnesum the ball By the table above, when control the oxygen ntake.54, lmestone.74, grey, magnesum the ball, Dolomte.6733, f ( C, T ) =.992 s the best answer. Because of the devaton whch s between the network and Dolomte.2637 Fnal temperature Fnal carbon content the actual value, there s a certan devaton between carbon temperature value and target value, but the devaton s n the allowed range. We take the results nto the lnear equaton (4), (5), shown n the followng table: Table 2 The comparson of RBF neural network model and multple lnear regresson Error Varable Target value RBF neural network method Multple lnear regresson method Predctve value Relatve error Predctve value Relatve error ECC ET By the table above, RBF neural network model has hgher accuracy and practcablty and s better than multple lnear regresson model. So we can use the RBF neural network to solve the converter steelmakng related control varable nputs n the producton process. 4. Concluson Each control varable and the molten steel n the course of converter steel-makng endpont carbon content and molten steel temperature there exsts a complex relatonshp. We set up multple lnear programmng model, although could ndcate the relatonshp between the varous data, but there are stll nsuffcent. Copyrght (c) 24 Internatonal Journal of Computer Scence Issues. All Rghts Reserved.

5 IJCSI Internatonal Journal of Computer Scence Issues, Vol., Issue 3, No 2, May 24 ISSN (Prnt): ISSN (Onlne): The model of converter steelmakng based on RBF neural network has hgh precson. It Can be appled to target steel carbon content and oxygen content and relevant control varables unknown or known only know that some of the control varable values ths knd of problem, so as to mprove the work effcency of converter steelmakng. References [] Yongfu Wang, Xaopng L, Tanyou Cha. Dynamc process of converter steelmakng presettng hybrd modelng and forecastng of the model[j]. Journal of Northeastern Unversty, 23,24(8): [2] Tanyou Cha, Shumng Xe, Bn Du. Based on Neural network's converter steelmakng end forecast[j].chnese ournal of nonferrous metals,999,9(4): [3] Youlang Yang, Zhao Xu, Wenhu Wang. An applcaton of the RBF neural network based on MATLAB[J]. Metallurgcal perodcals,29,6: [4] Zhonggeng Han. Mathematcal modelng methods and applcatons [M].Be Jng:Hgher educaton press, 29:89-. [5] Shuwu Yang. When n = 3 or 4 dsplay's code s nvald[j]. Journal of physcs experment,988,8(5):224. [6] Nng Gao, Janzhong Zhang. The applcaton of MATLAB n the RBF neural network model[j].agrculture Network Informaton,29,2:-2. [7] Chang, L-Zhong, and Zheng-Bang L. Study on BP neural net based converter statc control model. Langang(Steelmakng) 22.6 (26): Frst Author: Frst Author: Zh-hong Ma (975-), male, born n Nngxa, Assocate Professor of the Tann agrcultural Unversty, master's degree, manly engaged n the teachng of mathematcs and appled mathematcs research. Copyrght (c) 24 Internatonal Journal of Computer Scence Issues. All Rghts Reserved.