Project overview

Digitisation and process measurement technology

AgiFlex - Agent-based mod­els min­im­iz­ing car­bon usage in flex­ible and ef­fi­cient fu­ture in­teg­rated steel­works

Com­pre­hens­ive changes in the steel in­dustry's pro­duc­tion chains are ne­ces­sary to re­duce CO2 emis­sions. Against this back­ground, pro­cess in­teg­ra­tion must be re-op­tim­ized, es­pe­cially with re­gard to gas and en­ergy flows. Ex­ist­ing tools of cur­rent in­form­a­tion and com­mu­nic­a­tion tech­no­lo­gies are not suit­able for these new tasks due to a lack of flex­ib­il­ity and op­tim­iz­a­tion cap­ab­il­ity.

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SufCon­In­spect - En­abling zero-de­fect man­u­fac­tur­ing for flat steel pro­duc­tion by means of op­tim­ized in­spec­tion res­ults and a new level of on­line sur­face qual­ity con­trol

Re­source-ef­fi­ciency and com­pet­it­ive­ness are main aims of the European Green Deal trans­form­a­tion. In this glob­al re­struc­tur­ing pro­cess yield im­prove­ment and re­duc­tion of waste aimed at by a zero-de­fect pro­duc­tion are low-cost op­por­tun­it­ies for European steel man­u­fac­tur­ers to real­ize a more sus­tain­able pro­duc­tion.

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Steam­Dry - Su­per­heated steam dry­ing for sus­tain­able and re­cyc­lable web-like ma­ter­i­als

The Steam­Dry pro­ject aims to sig­ni­fic­antly de­crease en­ergy con­sump­tion and CO2 emis­sions in the paper and board man­u­fac­tur­ing in­dustry by show­cas­ing a concept that achieves a re­mark­able 60% en­ergy sav­ings in thermal dry­ing and 40% en­ergy sav­ings on a pro­duc­tion line, with the po­ten­tial for up to 100% CO2 emis­sion re­duc­tion.

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RoboIn­spect - Mo­bile Ro­bots for In­spec­tion of Steel Plants

The ob­ject­ive of ROBOIN­SPECT is to in­tro­duce novel ro­bot­ic in­spec­tion sys­tems in the European steel in­dustry. For this pur­pose, ex­ist­ing tech­no­lo­gies based on un­manned vehicles are as­signed to in­spec­tion tasks and tech­no­lo­gic­al gaps are closed by fur­ther de­vel­op­ments.

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En­er­MIND - En­ergy Man­age­ment in the Era of In­dustry 4.0

Vari­ous suc­cess­fully fin­ished European re­search pro­jects have re­mark­ably proven the po­ten­tial of in­tel­li­gent through pro­cess in­form­a­tion usage. Only in the field of En­ergy Man­age­ment at least 5 pro­jects can be found hav­ing im­port­ant res­ults with sig­ni­fic­ant im­pact able to im­prove any state-of-the-art En­ergy Man­age­ment Sys­tem (EMS) on the mar­ket. To fill this gap between re­search and in­dus­tri­al prac­tice dur­ing En­er­MIND-pro­ject one soft­ware sup­pli­er and two re­search in­sti­tutes co­oper­ate and real­ize a next gen­er­a­tion EMS pilot solu­tion

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OptDeslag - In­creased yield and en­hanced steel qual­ity by im­proved deslag­ging and slag con­di­tion­ing

The per­form­ance of many me­tal­lur­gic­al op­er­a­tions is sig­ni­fic­antly in­flu­enced by the con­di­tion of the slag. There­fore, in some cases the slag has to be re­moved as far as pos­sible. When the deslag­ging pro­cess is over­done, li­quid steel/hot metal from the li­quid bulk may also be re­moved, which con­trib­utes sig­ni­fic­antly to losses of steel/hot metal.

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Dyn­Stir - Dy­nam­ic stir­ring for im­prove­ment of en­ergy ef­fi­ciency in sec­ond­ary steel­mak­ing

Ladle stir­ring is of great in­terest to the steel plants, as it has major im­pact in the clean­ness and qual­ity of the steel. At the same time, stir­ring in­tens­ity and time is in­flu­en­cing the tem­per­at­ure de­crease of the li­quid steel, and there­fore an im­port­ant para­met­er re­gard­ing en­ergy ef­fi­ciency of steel pro­duc­tion. • Stir­ring is often car­ried out on the basis of stat­ic pre-set para­met­ers, without re­gard to the chan­ging me­tal­lur­gic­al spe­cific­a­tions between one heat and the next, and only mon­itored manu­ally by op­er­at­ors.

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PRESED - Pre­dict­ive Sensor Data min­ing

Some steel­works ex­per­i­ences prob­lems with product de­fi­cien­cies like sliv­ers and cracks. Sev­er­al pre-stud­ies in­dic­ated a re­la­tion­ship between the time de­pend­ence of a set of meas­ured val­ues and the oc­cur­rence fre­quency of the de­fect. Meth­ods for pre­dict­ing, de­tect­ing and re­du­cing the de­fect early in the pro­cess are de­sired.

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SitErk - Con­tinu­ous op­tim­isa­tion of pro­cesses and sys­tems in in­dus­tri­al pro­duc­tion through the auto­mat­ic re­cog­ni­tion of pro­cess situ­ations based on high-res­ol­u­tion sys­tem and pro­cess data

Pro­cess ex­perts want to ana­lyse sen­sori­al data with re­gard to a spe­cif­ic scen­ario or situ­ation. A situ­ation is defined by one or more pat­terns oc­cur­ring in one or more sen­sori­al data streams. Part­ner iba AG has soft­ware plat­form to re­view data re­cor­ded by the iba data ac­quis­i­tion sys­tems. This plat­form is used as basis for a re­cog­ni­tion tool.

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Check­S­IS - Per­form­ance as­sess­ment for auto­mat­ic sur­face in­spec­tion sys­tems

In mod­ern steel pro­duc­tion, auto­mat­ic sur­face in­spec­tion sys­tems (ASIS) are com­monly used to de­tect and clas­si­fy sur­face de­fects on strip steel. But to “meas­ure” the qual­ity of a steel sur­face in the sense of meas­ure­ment en­gin­eer­ing is still an un­solved prob­lem.

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AL­CHIMIA - Data and de­cent­ral­ized Ar­ti­fi­cial in­tel­li­gence for a com­pet­it­ive and green European me­tal­lurgy in­dustry

The Green Deal will make Europe the first cli­mate-neut­ral con­tin­ent in the world. To do that, European in­dus­tries must con­trib­ute to a green­er and more sus­tain­able Europe, which is spe­cific­ally re­cog­nized in a new policy for a cli­mate-neut­ral and cir­cu­lar in­dustry. Among the en­ergy-in­tens­ive in­dus­tries, the me­tal­lurgy in­dustry poses a major chal­lenge due to the tradeoff between main­tain­ing eco­nom­ic prof­it­ab­il­ity and pro­gress­ively im­ple­ment­ing the re­quired trans­form­a­tions for green­er pro­duc­tion.

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COCOP- Co­ordin­at­ing Op­tim­isa­tion of Com­plex In­dus­tri­al Pro­cesses

Pro­cess in­dustry faces a strong need to in­crease product qual­ity and re­duce op­er­at­ing costs and en­vir­on­ment­al foot­print. A com­plex plant com­prises con­tinu­ous and/or batch unit pro­cesses. The plant’s com­plex­ity stems from its dy­nam­ic prop­er­ties, so a plant-wide mon­it­or­ing and con­trol is a re­quire­ment for achiev­ing eco­nom­ic­ally and en­vir­on­ment­ally ef­fi­cient op­er­a­tion.

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SmartLadle - Smart con­sid­er­a­tion of ac­tu­al ladle status mon­itored by novel sensors for sec­ond­ary me­tal­lurgy pro­cess para­met­ers and ladle main­ten­ance strategies

The steel­mak­ing ladle has a strong in­flu­ence on the suc­cess of sec­ond­ary me­tal­lur­gic­al treat­ment dur­ing li­quid steel pro­duc­tion: Thermal ladle state in­flu­ences tem­per­at­ure evol­u­tion of the melt, his­tory and ladle lin­ing in­flu­ence the steel qual­ity. For safety reas­ons, suf­fi­cient re­fract­ory thick­ness that de­creases over ladle life­time must be guar­an­teed.

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Sup­port­Cast - Sup­port­ing Con­trol by In­spec­tion of Sur­face Qual­ity and Se­greg­a­tion on Cast Products through in­teg­ra­tion of Novel On­line Mon­it­or­ing and Ad­vanced Mod­el­ling into an Ac­cess­ible Cloud Ac­cess Plat­form

Eine gute Ober­flächen­qual­ität und ein op­ti­males Ent­mis­chungs­ver­hal­ten im ge­gossen­en Produkt sind zen­t­rale The­men beim Strang­gießen. Die Ober­flächen­qual­ität wird so­wohl durch die Er­s­tar­rung im Meniskus als auch durch die Nachbe­hand­lung in der Sekun­därkühlzone bee­in­flusst, während die Ent­mis­chung hauptsäch­lich durch die Ab­kühl­geschwindigkeit bee­in­flusst wird.

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Op­Con­Di­giCast - Di­git­al Twin Tech­no­logy for Com­pre­hens­ive Sim­u­la­tion, Op­tim­iz­a­tion and Con­trol of Con­tinu­ous Steel Cast­ing Pro­cess

Semi-fin­ished products man­u­fac­tured for qual­ity steel grades by con­tinu­ous cast­ing re­quire pre­cise con­trol of the op­er­at­ing para­met­ers that af­fect the cool­ing rate and so­lid­i­fic­a­tion in order to avoid de­fects such as crack­ing at high pro­ductiv­ity. This re­quires an ex­pan­ded know­ledge base of the com­plex, mu­tu­ally in­flu­en­cing pro­cesses of mass and heat trans­port, so­lid­i­fic­a­tion and ther­mo­mech­an­ics that occur dur­ing cast­ing.

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OMiAS - Op­tic­al on­line meas­ure­ment meth­od for in-situ ana­lys­is of the chem­ic­al com­pos­i­tion of a steel melt dur­ing li­quid steel pro­duc­tion

The chem­ic­al com­pos­i­tion has a major in­flu­ence on the prop­er­ties of steel. But until now, no op­er­a­tion­al tech­no­logy is avail­able for on­line ana­lys­is of the com­pos­i­tion. Fur­ther on, the labor­at­ory ana­lys­is of steel samples to de­term­ine the pro­cess status is only avail­able with a delay of about five minutes, which res­ults in a sig­ni­fic­ant ex­ten­sion of the pro­cess time and thus a loss of pro­ductiv­ity.

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CAPRI - Kog­nit­ive Auto­mat­is­ier­ung­s­platt­form für die di­gitale Trans­form­a­tion der europäis­chen PRozess-In­dus­trie

The over­all ob­ject­ive of CAPRI is to de­vel­op, test and ex­per­i­ment an in­nov­at­ive Cog­nit­ive Auto­ma­tion Plat­form (CAP) for achiev­ing Pro­cess In­dustry Di­git­al Trans­form­a­tion en­abled by Cog­nit­ive Solu­tions that provide ex­ist­ing pro­cess in­dus­tries flex­ib­il­ity of op­er­a­tion, im­prove­ment of per­form­ance across dif­fer­ent in­dic­at­ors (KPIs) and state of the art qual­ity con­trol of its products and in­ter­me­di­ate flows.

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Con­Sol­Cast – Com­pre­hens­ive mod­el­ling, mon­it­or­ing and con­trol of so­lid­i­fc­a­tion for op­tim­isa­tion of con­tinu­ous cast­ing pro­cess

New steel grades, in­creas­ing cast­ing speeds and qual­ity re­quire­ments of the cus­tom­ers raise the need for fur­ther im­proved mon­it­or­ing, op­tim­isa­tion and con­trol of the cast­ing pro­cess to avoid a strand break­out.

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Track­Opt - Track­Opt - Con­sist­ent ladle track­ing for op­tim­iz­a­tion of steel plant lo­gist­ics and product qual­ity

In­dustry 4.0 will not be­come a real­ity in the steel in­dustry until solu­tions are avail­able to track all in­ter­me­di­ate and end products along the en­tire steel pro­duc­tion chain and bey­ond to the end cus­tom­er. Due to the com­plex­ity, this is a real chal­lenge and def­in­itely not solved in sev­er­al areas of steel pro­duc­tion.

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SMARTER - Steam and gas net­work re­vamp­ing for the steel­works of the fu­ture

The steel in­dustry is an En­ergy In­tens­ive In­dustry and is there­fore ap­poin­ted as a major source of CO2 and other emis­sions. Every reg­u­la­tion ori­gin­ated from European or local gov­ern­ment hits the steel in­dustry to a great ex­tend due to the ex­traordin­ary mar­ket situ­ation com­bined with the dra­mat­ic­ally lower en­vir­on­ment­al re­quire­ments of the com­pet­it­ors form out­side the EU. To be able to pro­duce at com­pet­it­ive prices, the EU Steel sec­tor made and is mak­ing big ef­forts to im­prove en­ergy ef­fi­ciency by sav­ing en­ergy costs.

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Dyn­Re­Act_PDP - Roll-out re­fine­ment of pro­duc­tion schedul­ing through dy­nam­ic product rout­ing, con­sid­er­ing real-time plant mon­it­or­ing and op­tim­al re­ac­tion strategies

The op­tim­ised and tar­geted use of re­sources is man­dat­ory today and a key ob­ject­ive of mod­ern pro­duc­tion plan­ning. How­ever, ex­ist­ing pro­duc­tion plan­ning sys­tems lack the flex­ib­il­ity to deal with un­pre­dict­able events that fre­quently occur in real in­dustry.

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Hat­Flat - Hol­ist­ic As­sist­ance for Cross-Pro­cess Ana­lys­is and Pre­dic­tion of Strip and Plate Flat­ness

Flat­ness plays an over­whelm­ing role for both, the pro­cess qual­ity and for the qual­ity of strip or plate steel. Spe­cial sen­sori­al equip­ment was de­veloped to meas­ure flat­ness and flat­ness de­vi­ations, and, in the past, whole pro­cesses have been tailored to de­liv­er high qual­ity flat­ness char­ac­ter­ist­ics. Yet, for sev­er­al steel grades of grow­ing im­port­ance, good flat­ness is not re­pro­du­cibly achieved.

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s-X-AIPI – self-X Ar­ti­fi­cial In­tel­li­gence for European Pro­cess In­dustry di­git­al trans­form­a­tion

The over­all ob­ject­ive of s-X-AIPI is to re­search, de­vel­op and ex­per­i­ment an in­nov­at­ive tool­set of cus­tom trust­worthy self-X AI tech­no­lo­gies (autonom­ous AI that min­im­izes human in­volve­ment in the loop and ex­hib­its self-im­prov­ing abil­it­ies).

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Safe­D­ew­Point – Acid dew point and cor­ro­sion sensors for dy­nam­ic waste heat re­cov­ery from steel mill flue gases

Dur­ing com­bus­tion of steel mill gases acid dew point tem­per­at­ure var­ies strongly. Safe­D­ew­Point aims to re­cov­er waste heat in hot blast stoves, power plants and re­heat­ing fur­naces by dy­nam­ic ad­just­ment of flue gas tem­per­at­ure above the acid dew point.

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Dro­Mo­S­Plan – Drones for autonom­ous Mon­it­or­ing of Steel Plants

Drone tech­no­logy is in­creas­ingly being used for ci­vil­ian pur­poses and fur­ther de­veloped in these areas. For a suc­cess­ful trans­fer to the steel in­dustry, tech­nic­al, eco­nom­ic, legal and so­cial frame­work con­di­tions have to be ex­amined. Al­gorithms for autonom­ous and in­tel­li­gent fly­ing are needed to in­crease safety in con­fined and com­plex in­dus­tri­al en­vir­on­ments. In order to make drones a re­li­able tool for in­dustry, a more ro­bust design, the use of dur­able and res­ist­ant com­pon­ents and auto­mat­ic char­ging sta­tions must be de­veloped.

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Qual­ity4.0 – Trans­par­ent product qual­ity su­per­vi­sion in the age of In­dustry 4.0

Pro­gress­ive di­git­al­iz­a­tion is chan­ging the game of many in­dus­tri­al sec­tors. One of the main as­pects of todays’ In­dustry 4.0 vis­ion is the ho­ri­zont­al in­teg­ra­tion of in­form­a­tion over com­plete sup­ply chains. To share qual­ity in­form­a­tion bi­d­irec­tion­al across com­pany bound­ar­ies helps as well steel cus­tom­ers as sup­pli­ers to lower pro­duc­tion costs, in­crease yield and im­prove iden­ti­fic­a­tion of qual­ity prob­lems. But: To share wrong qual­ity in­form­a­tion may cause severe cus­tom­er un­cer­tainty and long-term dam­aged cus­tom­er con­fid­ence, and should be avoided at all costs.

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MACO Pilot – Op­ti­mier­ung der On­line-Konzen­tra­tions-Über­wachung und Re­gel­ung an Edel­s­tahl-Mis­chsäure­beiz­an­la­gen

Zun­ehmend­er wirtschaft­liche Druck im europäis­chen Stahlsek­t­or er­fordert hoch­flex­ible und gün­stige Produk­tion­sprozesse Beiz­prozess ist von be­son­ders hoher Bedeu­tung für die Edel­s­tahl-Produk­tion Hohe Prozess­flex­ib­il­ität er­fordert eine schnelle An­pas­sung definiert­er Konzen­tra­tion­en in in­dus­tri­el­len Beizbädern zur Ein­hal­tung kon­stant hoher Produk­tqual­itäten und An­la­gen-Produkt­iv­ität Im RFCS Pro­jekt FLEX­PRO­MUS wurde er­fol­greich eine in­nov­at­ive Mess­tech­nik zur kontinu­ier­lichen on­line-Ana­lyse von HF-HNO3-Säuregemis­chen en­twick­elt Erste Pro­to­typentests an Edel­s­tahl-Band­beiz­an­la­gen zeigten sehr er­folgs­ver­sprechende Ergeb­n­isse

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To­tOptL­is – Through-pro­cess op­tim­isa­tion of li­quid steel­mak­ing

Pro­cess con­trol in sec­ond­ary me­tal­lurgy is based on stat­ic op­er­at­ing in­struc­tions and manu­al in­ter­ven­tions, tak­ing into ac­count meas­ure­ments and model cal­cu­la­tions at in­di­vidu­al treat­ment sta­tions. Tar­get val­ues for single treat­ment steps do not en­sure op­tim­al over­all pro­cess op­er­a­tion.

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stack­Mon­it­or – On­line Blast Fur­nace Stack Status Mon­it­or­ing

The de­creas­ing and fluc­tu­at­ing qual­ity of raw ma­ter­i­als and the aim to max­im­ise PCI and de­crease coke rates force European blast fur­naces to op­er­ate closer to op­er­a­tion­al lim­its. At same time pro­ductiv­ity and ef­fi­ciency must be raised to sur­vive in glob­al com­pet­i­tion. High stack per­meab­il­ity and stable gas dis­tri­bu­tion be­come most im­port­ant.

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Deep­Qual­ity – Use of ro­bust deep learn­ing meth­ods for the auto­mat­ic qual­ity as­sess­ment of steel products

Deep Learn­ing aims to im­prove the auto­mat­ic qual­ity as­sess­ment of steel products by means of a hol­ist­ic ap­proach com­bin­ing deep learn­ing tech­no­logy with soph­ist­ic­ated man­age­ment of un­der­ly­ing train­ing data to en­able the op­tim­al use of all avail­able data sources and sim­ul­tan­eously sim­pli­fy the con­fig­ur­ab­il­ity and main­tain­ab­il­ity of pre­vi­ous de­cision sup­port sys­tem.

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Melt­Con – De­term­in­ing pro­cess con­di­tions for on­line mon­it­or­ing of tem­per­at­ure and car­bon con­tent in the elec­tric arc fur­nace to op­tim­ise end point con­trol

Elec­tric Arc Fur­nace has high de­mand of elec­tric­al and chem­ic­al en­ergy. Pre­cise end point con­trol es­sen­tial for en­ergy ef­fi­cient steel­mak­ing. Mod­els strongly de­pend on start­ing con­di­tions. Spot meas­ure­ments not mean­ing­ful in in­homo­gen­eous melts.

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Top­Temp – Shaft fur­nace ana­lys­is with acous­tic gas tem­per­at­ure meas­ure­ment

Shaft fur­naces such as blast fur­naces for pig iron pro­duc­tion are highly ef­fect­ive ag­greg­ates, but very com­plex physico-chem­ic­al in­ter­ac­tions make their op­er­a­tion very de­mand­ing. B + D of­fers the meas­ur­ing sys­tem SOMA for 2D acous­tic gas tem­per­at­ure meas­ure­ment in shaft fur­naces. Thanks to its high spa­tial and tem­por­al res­ol­u­tion, the meas­ur­ing sys­tem already provides new in­form­a­tion that has not yet been fully util­ized dur­ing op­er­a­tion.

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FOMTM – Ap­plic­a­tion of fibre op­tic­al thermal mon­it­or­ing at CC-bil­let and slab mould for im­proved pro­cess con­trol and product qual­ity

The ini­tial so­lid­i­fic­a­tion of the mol­ten steel is im­port­ant for de­term­in­ing the qual­ity of the cast product. Rim form­a­tion can have severe dam­aging im­pact on the sur­face of the so­lid­i­fy­ing strand. Cur­rently there is no reg­u­lar use of thermal mould mon­it­or­ing for tube moulds for long product cast­ing.

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Eval­HD – High res­ol­u­tion pro­cess data for qual­ity as­sess­ment

Plant-wide qual­ity data­bases exist at many steel pro­du­cers Data-ware­houses are op­tim­ized for per-coil data ac­cess For stat­ist­ic­al ana­lys­is per-coil data ag­greg­a­tions are used No ef­fi­cient ag­greg­a­tion of inner-coil qual­ity in­form­a­tion pos­sible

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ICon­Sys – Im­proved qual­ity man­age­ment in flat steel pro­duc­tion

DSS de­veloped dur­ing DEC­FLAQ pro­ject is suc­cess­ful run­ning since 2007 at Thyssen­K­rupp Rassel­stein Op­er­at­ors are sup­por­ted to check if the product was prop­erly pro­cessed Only qual­ity de­grad­a­tions were de­tec­ted No check if man­u­fac­tur­ing spe­cific­a­tions were con­sidered

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I2M­Steel – Soft­ware agents for a new auto­ma­tion paradigm

I2M­Steel – De­vel­op­ment of a new in­form­a­tion and auto­ma­tion paradigm for in­tel­li­gent and in­teg­rated man­u­fac­tur­ing in the pro­cess in­dustry based on holon­ic agents

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