A process model for EAF steelmaking

Transcription

1 A process model for EAF steelmakng Jens Wendelstorf and Karl-Henz Sptzer Clausthal Unversty of Technology, Insttute of Metallurgy, Robert-Koch-Strasse 42 D Clausthal-Zellerfeld, Germany Tel.: Fax: E-mal: Key words: EAF, electrc arc furnace, modellng, process automaton, steel makng, meltng, DRI, smulaton INTRODUCTION Most current Electrc Arc Furnace (EAF) steelmakng makes use of level-2 automaton systems [Nl00, He97]. The nstrumentaton of the furnaces allows for a quanttatve tme dependent measurement of the energy and mass nputs. Ths data can be used for onlne or offlne modellng of the meltdown process. The development of such models started n 1974 [Wal74] and n smpler forms they became state-of-the-art wthn the followng decades [Ame81, Koh95, Koh99, Iro05]. The model developments were undertaken nto several drectons, e.g. Determnaton of overall process characterstc and process control, e.g. [Sch84, Bel99, Bek00, Bou03]. CFD modellng of the Off-gas system [Tan03] or the heat transfer nsde the furnace [Guo05]. Modellng of meltdown and slag chemstry [Mat97, Bek99, Mor01] and slag foamng [Oos01, Mor02]. In most cases, the resultng models are specfc to the EAF or even an ndvdual plant. They use dfferent approaches and models for offlne and onlne applcatons. Detals on model calculatons usng real ndustral data are rare n the open lterature, but the developments durng the last decades and ths paper have some mportant common propertes: 1. The physcal conservaton laws of mass, energy and speces are derved and solved. 2. The general numercal method s to set-up and solve a set of nonlnear ordnary dfferental equatons (n most cases the trval explct ntegraton scheme s used). 3. The models requre ntal values and (n general tme dependent) data for the tme dependent nputs lke electrcal- and chemcal nputs and mass loads (scrap, DRI ). From these general features, a unversal approach for the modellng of metallurgcal processes (Process Reactor Model) was developed [Sp05]. Ths concept s appled to the EAF here. The process model presented s sutable for onlne as well as for offlne applcatons and can be ntegrated nto the steel plant IT system, e.g. n the framework of current level-2 automaton systems [Mat03, Bho05]. To avod confuson, the term process model s used for an algorthm mplemented as a software subroutne usng some ntal values at start-up and a number of (measured or extrapolated) nput values durng operaton for the calculaton of output values lke melt temperature TM or current mass of lqud metal ML. The PRM approach descrbed n the frst secton s also used for other metallurgcal processes lke ladle furnace melt condtonng. The concept and software envronment allows the balancng of mass, energy and speces n a fnte number of balance volumes. As descrbed n the second secton, the melt, the sold nput (scrap, DRI ), the slag, the shell, the panels and the roof are modelled together wth the gas mass and energy flows. The physcal model of the EAF operaton s set-up by defnng source and exchange terms wthn and between the balance volumes. The energy and mass balances are solved tme dependently. The model allows for a quanttatve predcton of values lke the melt temperature and s operated wthn a level-2 automaton system as well as an ndependent offlne model for optmzng the EAF operaton. Fnally the prncpal achevements, challenges and lmtatons of EAF process modellng are dscussed. [jw7981] Publshed n: AISTech 2006, May 1-4, 2006, Cleveland, Oho, USA, Volume 2 435

2 THE PROCESS REACTOR MODEL Modern steel plants make use of enterprse IT systems. Wthn these systems, level 1 (control, e.g. PLC) and level 2 (unt, e.g. EAFplant) automaton requres process models. Durng the producton cycle, a number of mportant quanttes are unknown or can not be measured for fundamental reasons, e.g. The current (average) temperature of the sold materal (scrap, DRI ) s not observable. The current melt and slag temperature are dffcult of measure n the EAF [Lam05]. The current mass of the melt n the furnace can not be measured. The use of onlne process models can therefore enhance the knowledge on the current process state and thus support optmzed process operaton by provdng addtonal nput data for process control. Addtonally, these models can use extrapolated nput data to predct the future trends of the process varables. The same models can be used offlne wth real or artfcal process operaton data to optmze the plant operaton or to analyse the mpact of process nnovatons. Durng the nstallaton and optmzaton of such a process model the requrements of the model justfy and support the mplementaton of advanced measurement systems, e.g. n order to get precse data on the energy and mass nputs nto the furnace. As shown n Fgure 1, the process model (for onlne and offlne use) s an mportant part of the EAF process and operaton optmzaton cycle. The nstrumentaton of the furnace (weghtng of scrap, DRI ) contnuously delvers measurement data durng the operaton whch s feed nto the model. To enhance the models abltes to predct mportant parameters lke melt mass and temperature, new or more precse measurements can be mplemented. The more precse data gves than better opportuntes to test and furthermore enhance the model. Such more detaled or accurate models wll allow better control durng the operaton (onlne model) or the development of optmzed process operaton modes. As a concluson, the model and ts development cycle s prncpally lnked to the development of the EAF operaton tself. Some of these relatons wll be analysed later wthn ths paper by a so called senstvty analyss usng the 7 balance volume process model EAF07.. Fgure 1: Process modellng n the context of the plant optmzaton cycle. [jw7981] Publshed n: AISTech 2006, May 1-4, 2006, Cleveland, Oho, USA, Volume 2 436

3 As sketched n Fgure 2, the process model tself s deduced from a unversal toolbox for the development of Process Reactor Models, especally for metallurgcal processes. The approach s to use as much fundamental physcs as possble and only a mnmum of process dependent coeffcents. The model solves equatons from physcal conservaton laws for mass, momentum, energy and speces. For a conserved quantty ψ n the balance volume, the dfferental equaton for the tme evoluton of ψ s d ψ = Fψ,, j + Sψ,, (1) dt j whereas S ψ, s the sum over all source terms of the balanced quantty ψ n the balance volume and F ψ,, j s the nteracton term for ψ between the balance volumes and j. For the smple case of heat transport wthout mass or speces transport between two balance volumes, the nteracton term for the enthalpy content H s boundary FH,, j = A, j α, j ( T T ), j. (2) boundary The nteracton area A, j and the heat transfer coeffcent α, j must be provded, whle the actual boundary temperature T, j can be calculated from the symmetry/conservaton relatons. The heat conducton nsde the balance volumes s ncluded n α, j as a specal case of the boundary layer concept used by defnng α, j n equaton (2). For nteractons lke meltng or chemcal reactons, where combned heat and speces transport takes place, the nterrelatons of the F ψ,, j have to be taken nto account,.e. by solvng the resultng (nonlnear) equaton system to determne all F ψ,, j self consstently. The source terms S ψ, are used to nclude external parameters lke electrc arc heatng, alloyng, reacton for nternal thermodynamc equlbrum, chargng etc. and are thus strongly dependng on the applcaton, e.g. the EAF, as descrbed n the followng secton. Fgure 2: Process Reactor Model concept. [jw7981] Publshed n: AISTech 2006, May 1-4, 2006, Cleveland, Oho, USA, Volume 2 437

4 MODELLING OF THE EAF OPERATION For the applcaton of the PRM concept descrbed above to a runnng EAF model, several steps are requred: 1. Analyss of the EAF operaton: The prmary functon s the meltng of sold materal by electrcally generated energy. 2. Defnton of nput and output quanttes: ~20 tme dependent values lke current electrc arc power PEL or DRI feedng rate MDR. ~20 process parameters lke furnace dmensons and heat transfer coeffcents. ~20 output results lke melt mass ML and temperature TL. several parameters for the model nstallaton and control, ncludng the energy dstrbuton parameter LEL. 3. Defnton of the balance volumes and quanttes to be balanced, see Table I, e.g. a. Upper shell, lower shell, roof : Energy balance for the calculaton of an average temperature. b. Lqud metal, sold metal (scrap, HBI, DRI): Mass-, energy- and carbon- balance. c. Slag and gas : Mass and energy balance. 4. Defnton of the nteracton functons between the balance volumes: Energy transport as defned n (2) or energy and mass transport (meltng/soldfcaton). 5. Defnton of the specal features and source terms for the ndvdual balance volumes (see below). 6. Defnton and acquston of data sets for ntal values and tme dependent nput data. 7. Implementaton and test usng the data sets from step Commssonng at an ndustral EAF. 9. Iteratve optmzaton as descrbed n Fgure 1. No./balanced quantty upper shell H [J] 2 lqud metal H [J] M [kg] M C [kg] 3 sold metal H [J] M [kg] M C [kg] 4 slag H [J] M [kg] 5 gas H [J] M [kg] 6 roof H [J] 7 lower shell H [J] Table I: Defnton of the EAF07 module. The model memory can be adapted to a specfc plant stuaton as requred and the model ncludes a database for the specfc enthalpy dependng on composton and temperature. The calculatons are performed usng tme step of e.g. 1s. For the EAF, a number of features were ncluded as follows: Electrcal Energy For each tme step, the electrcal (arc) energy nput s specfed by the value of PEL [W]. The constant global model (ft-) energy dstrbuton parameter LEL and the current mass values determne the energy dstrbuton between lqud, sold, slag, wall and gas balance volumes. Chemcal Energy Tme dependent nput values for oxygen blowng, gas burners etc. are used and ther energy nput s dstrbuted to the correspondng balance volumes. Materal Input For some applcatons, the ntal scrap basket s modelled as an ntal value for the balance volume sold, whle n general tme dependent addtons lke MDR [kg/s] for DRI addton s used by the source term concept explaned above. Energy Losses The losses of the shell to the envronment are modelled by coolng power nput values whch can be drectly calculated from coolng water flow and temperature dfference measurements. Losses to ambent ar, e.g. for the lower shell, are modelled by a heat transfer coeffcent to the balance volume, the envronment. Tappng or deslaggng s modelled usng the source terms. Gas and Waste Gas Currently, the balance volume gas, s modelled by assumng a constant mass of gas nsde the furnace and the nput parameter waste gas flow MAG [kg/s] and the output parameter leakage ar flow MAI [kg/s]. [jw7981] Publshed n: AISTech 2006, May 1-4, 2006, Cleveland, Oho, USA, Volume 2 438

5 RESULTS The model n ths example s runnng n onlne mode at an ndustral 80t EBT AC EAF furnace [Sp05]. The tested operaton mode s as follows: 1. Intal scrap basket of ~36t contanng ~30% heavy scrap, ~15% return scrap, ~50% DRI and ~5% lme. 2. After reachng the full power level (see Fgure 3), contnuous DRI feedng wth up to 30kg/s provded the rest of the requred sold materal for reachng the tappng level of ~80t. 3. Addtonal refnng and temperature adjustment and tappng after 60 to 75mn. 60 EAF07: Input data for melt P EL [MW] M DRI [kg/s] t [s] Fgure 3: Input data used for the calculatons. The results of the model calculatons are shown n Fgure 4 and Fgure 5. The ntal basket leads to some freezng of the ntal melt (sump), followed by sold scrap heatng wthn the frst 20 mnutes. After reachng full power level and start of DRI feed, the mass of lqud metal contnuously ncreases, whle the mass of sold materal n the furnace decreases. The model predcts an end of the meltdown phase after 45 mnutes. Durng the meltdown phase, the lqud steel temperature stays at lqudus level (1750 K = 1477 C). To avod confuson, the melt and sold temperatures n Fgure 5 are shown only when there s a relevant amount of mass wthn that balance volumes. The drop n slag temperature after 15 mnutes s related to the onset of DRI feedng at t=875 s whch also ncreases slag mass. The decrease of the average sold materal temperature after 40 mn, surprsng on the frst sght, results from the ncreasng coolng effect of the added DRI on the decreasng amount of sold materal durng the last 15 mnutes of the meltdown phase. The slag s ntally equlbratng wth the sold materal and about 200 K cooler than the melt n the overheatng phase. [jw7981] Publshed n: AISTech 2006, May 1-4, 2006, Cleveland, Oho, USA, Volume 2 439

6 EAF07: Melt melt mass [kg] sold deslaggng basket DRI added slag tme [s] Fgure 4: Calculated masses durng meltdown EAF07: Melt melt measured (1714 C) temperature [K] slag sold tme [s] Fgure 5: Calculated temperatures durng meltdown. In Fgure 5 the temperature measured 10 mn before tappng s marked. These temperatures were used to determne the model parameter LEL. Ths parameter s used to dstrbute the electrcal power of the arc between the melt, sold and slag (LEL=1) and the gas (LEL=0) balance volumes. Usng the nput data from a number of melts, the calculated melt temperature ML at the tme of measurement was compared wth the expermental value and a mean square devaton functon s calculated. A sngle representatve value of LEL was obtaned by mnmsaton of that functon. The model does therefore not rely on temperature measurements to predct values lke the end of the meltdown phase. The well known rescalng of the model data wth the nformaton from actual measurements s possble but not recommended. A better approach s to determne the model parameters lke LEL (see above) by comparson of measurements wth the model predctons for a larger number of heats. Prncpal parameters not dependng on the plant confguraton, e.g. heat transfer coeffcents between the lqud and sold materal (H23 and H32) can also be determned by separate experments or modellng. [jw7981] Publshed n: AISTech 2006, May 1-4, 2006, Cleveland, Oho, USA, Volume 2 440

7 Senstvty analyss Before valdatng the qualty of a model or the determnaton of model parameters, a senstvty calculaton can provde mportant nformaton on the models requrements regardng the measurement of nput data. By nvestgatng the effects of a varaton of nput data for sngle tme steps or better for a number of typcal complete test cases ( melts, batches ), mportant quanttatve nformaton on the overall system behavour s obtaned. Selected values of such an analyss are shown n Table II. EAF07 Senstvty Map: t [s] = t [s] = Absolute Values: Percentage Values: Relatve Values: Target TM[K]: Melt: % 100% Allowed Category: Input: Value: Error: TM t=4000s [K] t MS=0 [s] TM t=4000s t MS=0 TM t=4000s [K] t MS=0 [s] TM t=4000s Error: ntal value TIB [K] % % 0.0% N/A N/A ntal value TIS [K] % % 0.5% % 15 ntal value MII [kg] % % 2.6% % 239 parameter DDR [m] % % 0.1% % parameter H23 [W/m 2 K] % % -0.2% N/A N/A parameter H32 [W/m 2 K] % % -0.2% % 44 parameter LEL [1] % % 0.2% % 0.00 tme dep. nput PEL [W] 6.0E+07 10% % -1.0% % tme dep. nput MDR [kg/s] 35 10% % 2.1% % 0.24 Table II: Calculated senstvty map. The analyss was undertaken usng real ndustral data for the ntal values and the tme dependent values of electrcal power PEL and DRI feedng rate MDR. The rows represent these nput values and the columns show the values and relatve varatons of the modellng results for a varaton of 10% n the nput. The reference data for melt number 2721 computed from 0 to 4000 s s 1954 K for the melt temperature TM at t=4000 s and 3641 s for reachng the value MS=0,.e. meltng all sold materal. In lne 4 of the table (the frst lne wth results), one can see that a varaton of the ntal (t=0s) lower shell temperature TIB by 10% does not nfluence the selected results (TM(t=4000 s) and t(ms=0)). The ntal temperature of the sold materal TIS (representng the temperature of the scrap basket as t was modelled by ntal values n that nstallaton) has some mpact on the melt temperature before tappng. A 10% or 77 K ncrease n scrap basket temperature gves 50 K more for the predcted tappng TM(t=4000 s) temperature. The next lne shows the effect of weghtng precson of the ntal scrap basket. At ths pont, the last 2 columns ( Target TM [K] ) should be explaned: If the model should predct the temperature of the melt after 4000 s wth 10 K precson, the ntal scrap weghtng must have a 0.7% accuracy. Ths s a physcal lmtaton, because f the amount of energy s fxed, addtonal ntal mass means a later onset of the overheatng phase. Fnally, the computatonal results of the senstvty analyss gve the followng conclusons: The model s able to predct tappng temperature,.e. there s no dependency on hdden or unknown parameters. The model s able to predct the meltdown status wthn the furnace. A T=10 K tappng temperature (or 0.5%) precson mples the followng requrements to the nput data: o 0.5% weghtng precson of sold (scrap, DRI, ) addtons. o 0.4% measurement precson for real electrc power. o About 2% accuracy for other ntal values lke sold temperature, DRI propertes, o Less than 2% varaton n the power dstrbuton parameter LEL for dfferent data sets durng the parameter determnaton phase. o Less than 9% varaton n the heat transfer coeffcent H32 (sold->lqud) for dfferent data sets durng the parameter determnaton phase for dfferent sets of expermental data. Problems wth the last topc can be fxed by more complex and detaled models or a smaller varaton n the furnace operaton parameters. It should be emphaszed that a good agreement between measurement and model predcton after ths parameter determnaton phase does not necessarly valdate the model. A fnal valdaton of such a model should fulfl a number of addtonal requrements: Valdaton of the predcton accuracy for dfferent plant mplementatons and dfferng furnaces. Valdaton of the predcton accuracy for dfferng test cases, e.g. wth and wthout DRI feedng. Measurement of model parameters ndependently from the EAF, e.g. ndependent determnaton of heat transfer coeffcents. If dsagreements are found for that last pont, the fundamental assumptons used for the model should be revsed and the need may arse for more detaled or complex descrptons. Ths top down approach can thus stmulate fundamental research by provdng detaled and quanttatve knowledge of the practcal mpact of the expected results. [jw7981] Publshed n: AISTech 2006, May 1-4, 2006, Cleveland, Oho, USA, Volume 2 441

8 SUMMARY, CONCLUSIONS AND OUTLOOK The devce and applcaton ndependent Process Reactor Model was brefly ntroduced as a concept and toolbox for the development of process models for metallurgcal plants. It was used for the development of the EAF07 module, a software component whch s capable for the onlne and offlne modellng of the meltdown process n the EAF. The results for the tme characterstcs of the masses and temperatures n the furnace are n good agreement wth expectatons and measurements. Only one EAF-specfc ft parameter, descrbng the dstrbuton of the electrcal energy nsde the furnace, was used. The avalable data supports the well defned property of the model,.e. t s not dependng on parameters changng ther values from tap to tap. The model can be used to predct tappng temperature or meltdown status wthn a precson determned prmary by the precson of the measured nput values and only secondary by the model or ts parameters. The applcaton of the model for a so called senstvty analyss gves addtonal hnts for the further development of the mathematcal process descrpton and the plant nstrumentaton. For hgh-end applcatons, a number of enhancements are under development: Enhancements of the energy balance (more detaled chemcal energy contrbutons). Enhancement of the gas and waste gas modellng. Implementaton of addtonal metallurgcal operatons (e.g. decarburzaton by oxygen blowng). Slag foamng effects. These requre addtonal features mplemented n the underlyng PRM tself as well as addtonal complexty to the EAF specfc parts and addtonal process values to be measured. The small number of balance volumes assures the onlne capablty of the model. Compared to a competng CFD approach, the computer hardware requrements are reduced to sngle CPU PC hardware or even long term stable systems lke the Semens SIMATIC S7-400 applcaton module FM 458. Addtonal benefts are expected from the model generalzaton tself and the ntegraton nto a seamless ndustral automaton envronment. ACKNOWLEDGEMENTS We are grateful to the colleagues from SIEMENS for ther support of ths work [Sp05]. For commercal references see [jw7981] Publshed n: AISTech 2006, May 1-4, 2006, Cleveland, Oho, USA, Volume 2 442

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