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Ju rgen Dorm, Chr ist ian Dopp lerlabo rato ry for Expert Systems

EEL PRODUCTION INVOLVES Anumber of stages, such asmelting, casting,rolling, and forging, that entail complexchemical and thermic reactions as well asintri cate mechanical operations. B ecausethese processes do not lend themselves toexact mathematical modeling, steel manu-facturers must turn to techniques for rea-soning wi th incomplete and uncertain data.Their decisions often rely on the experienceof i ndividual experts. Nearly all steelmak-ers worldwide now use expert systems,fuzzy logic, and neural nets to improvequality assurance and production effi-ciency. This special track of IE EE Expertlooks at several typical, successfullyfielded systems.For many years, the steel industrys mainobjective has been to maximize productionby automating processes and streamliningplant organization. As with the Republic ofK oreas Kwangyang Works, steel manufac-turers have been erecting new plants from

scratch, locating them near the sea to makethe delivery of steel f rom blast furnace tofinal shipmentasdirect as possible. Becausethey restricted the diversity of their prod-ucts, such new plants have become verycompetitive. Asias steel i ndustry, inpartic-ular, used these approaches in the 1980stoproduce high-quality steel cheaper than itsWestern competitors. (See the sidebar forhistorical overview of steel production.)However, the continuing improvement ofsubstitutes f or steel has raised the demandfor even higher-quality steel wi th dedicatedcharacteristics. By using different alloyingmetals and various heat and surface treat-ments, steelmakers now can offer amani-fold of products. Ongoing research into newsteel qualities has produced a broad range ofproducts, which present many newcontrolproblems. A lthough other industries reflectthe same tendency toward processing insmaller lot sizes, the steelmaking environ-ment shows more diversity than most be-

cause of the particular characteristics of itsmatenal and manufactunng technology Fur-thermore, the capital-intensive nature of theindustry can make unanticipated violakonsof technological constraints extremelycostly A l ook at several typical factors wil lil lustrate these considerationsM ost steelmaking processes are tempera-ture-sensitive For each process, the steelmust have aprescribed temperature, and anytime it spends waiting on the next process-ingstep will incur acostlyreheating. M ore-over, because chemical reactions depend ontemperature, any loss of heat during pro-cessing may degrade the steels quality I i theprescribed sohkfication temperature profileis violated, an incorrect internal structure ofthe steel might resultA lthough process ames are dif fi cult to pre-dict precisely, steelmakers do exercise somedegree of control. T reatment time in furnacesdepends on the temperature, which can becontrol led through heat input, generall y sub-

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j ect to wine global energy constraint. Steel-maher\ c;in a l s o control casting and roll ingtiiiic\ tow ine extciit by varying the speed at\\ hich they run thc caster or rolling mill. Byslouing ;I process down if it appears that anordei-uill arrive too early at the next riggre-,grr i i , . 01-peeding it up if it appears it will ar-r i \ c too late. they can also use real-timeproce\s control to meet requirements for syn-chronicity. (A n aggregateisa common steel-mahingterm lor machine.)

Techno og al cons de -a ions in the pro-eltiction of'highei- grades of steel impose re-qiiireincnts on the sequence in which ordersarc pi-oduccd. Chemicals added to steel to;ichie\ c certain charucterirtics react with the\teelmahiny aggregate. Residuals remain inthe agyrcgate and may be absorbed by oneof the next orders. which inay be corruptedby thi\ inf il tration. T he width and thicknessot thestccl product also constrain sequencesin the ca9ring and roll ing processes.Produc-tion-run ciigineerswill avoid some obviouslyincompatible sequences, but sometimes\chedii lc incompatible ones anyway for lackof a closed tractable causal model that wouldprei ent them from doing s o . A fterward,cau\al models can explain these errors, andthis negative experience will lead to a mod-ification of the production process.

Autom ating productionThe nature of these problems that compli -

cateproduction control-the vagueness anda h iy\-changing nature of the knowledge-ha\ prevented steelmakers from makingclo\ed control loops for steel production.T hi \ industry has always been very innova-t i l e i n the application of new productiontechnologic\ and the latest computer tech-nology. I t was one of the first. for example,to :ipply fault-tolerant computer systems tof u l f i l l the high requirements for availabilityan d reliability of control systems. Despitethi\ I-apidautomation. the process operatorha\ remained ;in important link in the pro-duction proce\s. and with the introduction ofthew new control \ystems. operators arebeing overloaded with proce5s data. (Sectht:moclern steel production sidebar, next page.)

T hestecl industry adopted expert systemsrelatively early lor further automating pro-duction. The five leading J apanese steel com-panic\ reported the first successful applica-t ions. ' The designers of the Scheplanwhecluling system. for instance, cl aimed that

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Historical overview on steelmakingCompared to other industries, ron and steel production has a very long history. Archaeolo-gists have found iron tools, weapons, and omaments n prehistoric tombs, and between 1350and 1100BC early societies n various parts of theworld had begun smelting iron ore intoiron on a large scale.The earliest smelting was done in primitive, charcoal-fueledhearths that used wind powerto provide combustion.Thisdirect reduction produced a soft, malleable wrought i ron. whichearly ironworkers then forged by hammering.Hearths soon gave way to simple blast furnaces. Reaching a height of 16 feet by I350AD,these fumaces were continuously charged with iron ore and charcoal , and later limestone, ;\the smelting process evolved. Because the produced high-carbon iron was rather brittle, i tcould neither be rolled nor forged.The first indirect reduction occurred in Belgium about 1620.The two-step procedurefirstused an initial smelting to eliminate the ore's oxygen content. The second step purified the re-sulting pig iron, removing carbon and other elements.Later improvements involved theu s ~ -ing and handling of the steel.Because improvements in steelmaking usually entail huge investments, he modern steelplant evolves slowly over decades.A new fundamental technology seems to arise every I O to15years that requires a reorganization of the production process. The introduction olcontinu-

ouscasters n the late 1970smarked a such great turning point. Casters have enhanced theproductivity signi ficantly, but the need to continuously feed them creates new problems.Minimil ls for rolling small lots are the latest profound innovation.

its operation saved $ 1 million a year by re-ducing the time that ladles carrying hot steelto the casters must wait.'

A lthough quali ty optimi zation and energyconsumption are important aims, the mostimportant motivation for applying expertsystems seems to be production standard-ization. I n the steelmaking plant, for exam-ple, it ismore important to have a safe andcontinuous supply from the blast f urnacethan a high, but i rregular, supply. Becausethere isso much freedom in production de-cisions, it is better for quali ty assurance pur-poses to have formal rules about how to pro-ceed, even if they are less than optimal.Having an expert system that acts as a con-sultant or even as a decision maker will makedecisions more transparent.

Applying expert systemsSteelmakers apply expert systems instead

of conventional software because the con-trolling software has to reason with existinguncertainties and master the inherent com-plexity of typical control problems. The con-trol of the blast furnace il lustrates i\sut.s mo-tivating the iniplernentation of expertsystems.

The main focus of the blast furnace qual-ity i mprovement effort is hot-metal siliconvariability. This iscontrol led by the heat lev-els inside the furnace. If the furnace is toohot, the silicon will increase; too cool, sili-con levels will decrease. Unfortunately. i t is

impossibleto iiieii\urc the teinperaiurc 111-side the furnace. so htincli-eel\ 01' \cti\ot.\ O I Ithe walls indirectly nic;i\tii-c tlic hol iiict:il.\temperature.

Existing expert $1 teiiij addre\\ t~ oproblerns:

predicting abnormal \ittiation\ \tic11 ;I\slips (abnormal and \tidclcn de\cciicliiig\of the raw matcrinl\ charged i n tlic l u i -nace) and channeling (tlic heated g;i\reache\ thetop ofthe l'iiriiace \\ it l io i it rc'-action) andkeeping the thermal coliclition stahlc,.

Operator\ can adjii\t furnace lieat IC \el \and hot-metal temperattire\ t>> 1ii;in 1pi x1 112such variables as ore-to-cokc r a t i o \ . hla\ttemperature\, fuel-iii,jection IC \ l\ . iiiicl t>la\tmoisture levels. I f the hot-rnctal \i l icoiifalls below the desired \ alue. the hot-iiictaltemperature wil l ; i I w he below i t\ goal. 'l'licoperator will need to iiicrciw the heat lc \e l .T he problems i n controlling thi\ procc\\ ~ r cthe long reaction time\ anc l the tlil'lcren~-c-actions of human operators. I n d i \ idual op -eratorb use dillerent ;ictioiis. art tlieiii ;I I (111-ferent times. and apply clillcrcnl magiiitutle\of change\. T hcy also I'rcqucritlyu\ c old tliitafrom previou\ c;i\es toclccitle irc~ctioii\.aim not to optimic.e hut to \(ariclardi/e th i \control. The unccrtninty ol'iiian) clata \ ;iluc\makes i t difficult to find ;I \iiiiplc control ZII-gorithm. An cxpcrt \) \tci i i lei\ ii1;iiiuI;ictiir-crs build ;I moclel o l he ph) \ic;iI and c,liL>ii-ical prows\ in the I'tirti;ice u tl i \! tiiholicvalues. abs racting from t li e [Ii( ti :iiicl\ o 1

M os bl a\ I'Llr1i:lce\ 111a~agcI'\hc C I (11 c

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Modern s teel production and controlSteel production requires different groups of processes often involv-ing different plants. In the iron-making stage, steelworkers charge ironoxide (pellets, sintered ore,or iron ore), coke, and limestone n layersinto ablastfurnace to producepig iron. Typically, a blast furnace canhold several thousand cubic meters and operates continuously for about

I O years before shutting down for repairs.Starting at the top of the furnace, the charge takes about eight hoursto descend through the shaft. Sol id, liquid, and gas coexist in the blastfurnace, with heat exchanges and reduction reactions governing thephenomena inside. These are expressed in terms of furnace conditionsand heat. Changes in the properties of the raw materials and the chang-ing profile of the furnace body disturb the furnaces operation. T heprocess consumes huge amounts of energy.Special torpedo cars transport the liquid pig iron tapped from theblast furnace (up to 10,000ons of hot metal per day for large blast fur-naces) to the steelmaking shop. Here,basic oxygen umaces (BOF) re-tine the iron steel to the desired composition by blowing oxygen intothe hot metal, which oxidizes impurities. During each run, or hear, theprocessing of up to300tons of pig iron takes about20to30minutes.Steelworkers must control molten steels composition and tempera-ture. Such control is especially critical in the oxygen-blowing step.Recently, because of the increased use of continuous casting and wide-spread use of hot-metal pretreatment, the requirements for blowingcontrol have become stricter. If no direct charging from the blastfurnace is possible or if huge amounts of(cold) scrap iron are charged, plants mustuseelectric arc urnaces to smelt themetal, a process that requires a hugeamount of energy and takes considerablylonger than the BOF converter process.liquid steel is poured into ladles, beforecranes transport it to various refinementaggregates (machines). n making the var-ious grades of steel that customers order,these aggregates eliminate further impuri-ties and add further alloying metals.The last stage in the steelmaking shopeither uses continuous casters to cast thesteel into bil lets, blooms, or slabs,oramore traditional process that involvesmaking ingots, soaking the ingots, andpassing them through a slabbingbloom-ing mill.is very important. Fluctuations in thelevel of molten steel in the mold of thecaster cause inclusions of gas in the metaland surface defects. Heating the cast

Next comes ladle refinement, where the

In continuous casting, real-time control

measured data values. Rules allow thespecification of certain standards-when andhow an operator should react.One of the first well -described systems inthis domain was Nippon SteelsArtificial andL ogical I ntelli gence System (A L IS), whichcontrols several blast furnaces. Compar-isons between human and expert system per-formance showed that in 25% of the casesstudied. the expert system performed betterand only in 7% did the human excel. Fur-

products up to 1200C in a reheat furnace eliminates surface defectsand prepares them for hot rolling. This process also poses many controlproblems, both in combustion efficiency and steel exi t temperature.Steelmakers control the reheating process with mathematical models,linear programming, and expert systems. The control of the reheatingprocess is closely linked with the hot-metal process.Othersteps involve rolling the cast goods in the rolling mil ls, first

rolling the steel warm and then cold to achieve high accuracies n sur-face structure, dimensions, compression, and impact strength. Thesestages each involve several passes. The rolling process greatly affectsthe quality of the final product, sothe thickness, shape, and chemicalproperties of the rolled steel require highly accurate control. Control-ling the pace of the mill presents another problem, one that involves de-termining the optimum reheating furnace discharge interval to allowthe hot rolling mill to run continuously.continuous annealing completes the processing of the steel and deter-mines the properties of the final products. Here, the mill heats steelproducts to a predetermined, closely controlled temperature for a givenlength of time to obtain the desired quality. The final stage may involvea variety of operations such as pickling (descaling coils with acid),tempering, orcoating of theproduced coils, pipes, wires, and plates,before shipment to customers. (FigureA shows the cross section of atypical high-grade steel.)

Finally, the rolling mill produces a variety of steel products. Batch or

Fig ur e A. Cross section (x600)of K1OO steel produced at Austrias Bohler Uddeho lm plant . An oc id-treated austenit, itcontains 1.23% carbon, 0.4% silicon, and 12.5% manganese.

thermore, the system is continually modifiedto improve its competence.

This issueToday, as the latest conferences on pro-duction control in steelmaking show, almost

every steelmaker-from developing coun-tries to the traditional steelmakers-appliesexpert systems.4 One great challenge now

facing the steel industry is to improve theself -adapting capabil ities of expert systems.A s mentioned, modif ications of the produc-tion process are quite regular. and the ten-dency is to adopt even more flexibil ity. I n-telligent steelmaking aggregates that adaptthemselvestonew steel compositions and re-quirements for the produced good are i n-portant research topics now. Kcscarch intointelligent organizers that can learn ne&strategies if the manufacturing objective or

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the production technology changes is also ongoing. The first article,by N icol as Pican, Frkdkric A lexandre, and Patrick Bresson, addressesthis i ssue. T hey have developed a system that incorporates an artif i-cial neural network to preset the parameters of a steel temper mill .They also show that a combination ofA I and conventional techniquesoften solves industrial problems best.A n i ntermediate step in self- learning systems are systems thatassist human experts. B ecause users of expert systems are notfamil iar with expert system techniques, they need simple techniquesto adapt a system to new production facil ities and strategies. The ar-ticle by J ii rgen Dorn and Reza Shams describes experience alongthese li nes and makes propositions for improving this capabil ity.

A focus of future research is the cooperation between expert sys-tems in the steel industry. At the moment, expert systems are single-user, front-end computer systems dedicated to one function. T heirintegration with the existing organization is very simplistic. M ostsystems couple to a process computer or a production-planning sys-tem to obtain required input data. However, stronger coupling wouldincrease the benefits of expert systems. T he simplest solution is anexpert system that performs this cooperation as its main task. How-ever, a more generic approach would let expert systems cooperate inanopen framework.

For example, a steelmaking shop scheduling system should re-ceive knowledge of the status of the blast furnace, because the sup-ply situation wi ll infl uence the scheduling strategy. More usefulwould be cooperation between a scheduling system and an intelli-gent machine such as a caster that can decide which sequences aregood and when maintenance operations should occur. N egotiationsare also necessary between a steelmaking plant and its customers-the roll ing mi ll s and other plants. B ecause these plants operate underdif ferent sequencing criteria, a best sequence for one plant is not nec-essaril y good for the other.Despite even more pervasive automation n the future, human ex-perts wi ll remain unavoidable for production control i n the steel in-dustry, because new production fail ures that cannot be handled ade-quately by a system occur quite regularly.

References1. T. Saito, Application ofA rtificial Intelligencein the Japanese ron andSteel Industry,Proc. Sixth Intl Federation ofAutomatic Control Symp.Automation in Mining, Mineral andMetal Processing, 1989, pp. 30-38.

M. NumaoandS.Morishita, Scheplan-A Scheduling Expert for Steel-making Process, Proc. Fourth C o f Artificial Intelligence Applica-tions,AIA APress, Menlo Park, Calif., 1988, pp. 467472.S.Amano et al., Expert System for Blast Furnace Operation at K im-itsu Works, . Iron and Steel Inst. o J apan,Vol. 30, No. 2, 1990, pp.

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4. Preprints ofthe ntl Con$ onComputerized Production Control n SteelPlant,The Korean Instituteof Metals and Materials,Seoul, 1993.FEBRUARY 1996

J iirgen Dorn isasenior researcher at the Chris-tian Doppler Laboratory forExpert Systems in V ienna, a basic research laboratory established by the Aus-trian steel industry to improve technology transfer from universities to steelindustry. He received hisMS andPhD in computer science from the Techni-cal University of Berlin. He was involved in the development of two sched-uling expert systems for the Austrian steel industry and works as a consultantfor the international steel industry in the field of expert systems.He is mem-ber of the AAAI. Readers can contact him at the Christian Doppler Labora-tory for Expert Systems, Vienna Univ. ofTechnology,Paniglgasse 16,A-1040Vienna, Austria; dom@dbai.tuwien.ac.at.

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