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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
1

Towards Understanding slag build-up in a Grate-Kiln furnace : A study of what parameters in the Grate-Kiln furnace leads to increased slag build-up, in a modern pellet production kiln / Mot ökad förståelse av slaguppbyggnad i ett kulsintersverk

Olsson, Oscar, Österman, Uno January 2022 (has links)
As more data is being gathered in industrial production facilities, the interest in applying machine learning models to the data is growing. This includes the iron ore mining industry, and in particular the build-up of slag in grate-kiln furnaces. Slag is a byproduct in the pelletizing process within these furnaces, that can cause production stops, quality issues, and unplanned maintenance. Previous studies on slag build-up have been done mainly by chemists and process engineers. Whilst previous research has hypothesized contributing factors to slag build-up, the studies have mostly been conducted in simulation environments and thus have not used real sensor data utilizing machine learning models. Luossavaara-Kiirunavaara Aktiebolag (LKAB) has provided data from one of their grate-kiln furnaces, a time-series data of sensor readings, that compressed before storage.  A Scala package was built to ingest and interpolate the LKAB data and make it ready for machine learning experiments. The estimation of slag within the kiln was found too arbitrary to make accurate predictions. Therefore, three quality metrics, tightly connected to the build-up of slag, were selected as target variables instead. Independent and identically distributed (IID) units of data were created by isolating fuel usage, product type produced and production rate. Further, another IID criterion was created, adjusting the time for each feature in order to be able to compare feature values for a single pellet in production. Specifically, the time it takes for a pellet to go from the feature sensor to the quality test was added to the original timestamp. This resulted in a table where each row represents multiple features and quality measures for the same small batch of pellets. An IID unit of interest was then used to find the most contributing features by using principal component analysis (PCA) and lasso regression. It was found that using the two mentioned methods, the number of features could be reduced to a smaller set of important features. Further, using decision tree regression with the subset of features, selected from the most important features, it was found that decision tree regression had a similar performance with the subset of features as the lasso regression. Decision tree and lasso regression were chosen for interpretability, which was important in order to be able to discuss the contributing factors with LKAB process engineers. / Idag genereras allt mer data från industriella produktionsanläggningar och intresset att applicera maskininlärningsmodeller på denna data växer. Detta inkluderar även industrin för utvining av järnmalm, i synnerhet uppbyggnaden av slagg i grate-kiln ugnar. Slagg är en biprodukt från pelletsproduktionen som kan orsaka produktionsstopp, kvalitetsbrister och oplanerat underhåll av ugnarna. Tidigare forskning kring slagguppbyggnad har i huvudsak gjorts av kemister och processingenjörer och ett antal bidragande faktorer till slagguppbyggnad ha antagits. Däremot har dessa studier främst utförts i simulerad experimentmiljö och därför inte applicerat maskininlärningsmodeler på sensordata från produktion. Luossavaara-Kiirunavaara Aktiebolag (LKAB) har till denna studie framställt och försett data från en av deras grate-kiln ugnar, specifikt tidsseriedata från sensorer som har komprimerats innan lagring. Ett Scala-paket byggdes för att ladda in och interpolera LKAB:s data, för att sedan göra den redo och applicerbar för experiment med maskininlärningsmodeller. Direkta mätningar för slagguppbyggnad och slaggnivå upptäcktes vara för slumpartade och bristfälliga för prediktion, därför användas istället tre kvalitetsmätningar, med tydligt samband till påföljderna från slagguppbyggnad, som målvariabler. Independent and identically distributed (IID) enheter skapades för all data genom att isolera bränsleanvändning, produkttyp och produktionstakt. Vidare, skapades ytterligare ett kriterie för IID:er, en tidsjustering av varje variabel för att göra det möjligt att kunna jämföra variabler inbördes för en enskild pellet i produktion. Specifikt, användes tiden det tar för en pellet från att den mäts av en enskild sensor till att kvalitetstestet tas. Tidsskillnaden adderas sedan till sensormätningens tidsstämpel. Detta resulterade i en tabell där varje rad representerade samma lilla mängd av pellets. En IID enhet av intresse analyserades sedan för att undersöka vilka variabler som har störst varians och påverkan genom en principal komponentsanalys (PCA) och lassoregression. Genom att använda dessa metoder konstaterades det att antalet variabler kunde reduceras till ett mindre antal variabler och ett nytt, mindre, dataset av de viktigaste variablerna skapades. Vidare, genom regression av beslutsträd med de viktigaste variablerna, konstaterades att beslutträdsregression och lassoregression hade liknande prestanda när data med de viktigaste variablerna användes. Beslutträdsregression och lassoregression användes för att experimentens resultat skulle ha en hög förklaringsgrad, vilket är viktigt för att kunna diskutera variabler med högst påverkan på slagguppbyggnaden och ge resultat som är tolkbara och användbara för LKAB:s processingenjörer.
2

Degradation Mechanisms of Heat Resistant Steel at Elevated Temperatures : In an Iron Ore Pelletizing Industry

Nilsson, Erik A. A. January 2017 (has links)
This thesis focuses on the different degradation mechanisms of the stainless steel in a travelling grate in a Grate-Kiln iron ore pellet indurator. The travelling grate is a conveyor belt that transports green-body pellets to a rotary kiln while the pellets are being dried and pre-heated to a temperature of 900-1100 °C by recycled hot air. After unloading of the pellets to the rotary-kiln for further sintering, the travelling grate is cooled in room temperature while returning to the loading zone of the wet pellets. The steel was tested during thermal cycling in a test-rig, in order to simulate the influence of thermo mechanical fatigue and oxide spallation. The influence of erosion-deposition was investigated in a modified horizontal industrial combustion kiln at 800 °C, with slag and coal from production used as erosive media and combustion fuel, respectively. The influence of minor alloying additions of Mn, Si and Ti on the microstructure was explored by eight different casted alloy compositions. Isothermal heat treatments were performed at 800 °C during 200 hours on steel immersed in deposits recovered from a travelling grate in production. The three main degradation mechanisms found in this work are thermal spallation, erosion-deposition and deposit induced accelerated corrosion (DIAC). Thermal spallation of the oxide layer is caused by the thermal expansion difference between the oxide and the metal during heating and cooling. It has been found that Ti improves the spallation resistance while Si reduces it. Spallation of deposits is another cause believed to increase the degradation. Erosion-deposition appears due to simultaneous erosion and deposition of particles on the travelling grate that causes erosion or deposition depending on the amount of alkali metals in the environment. The velocity of the particles also influences erosion and deposition in the way that higher velocities increase erosion. DIAC is proposed to form on the travelling grate due to the concentration of chloride- and sulphate containing alkali metals in the deposits.  Other than these major degrading mechanisms, minor degradation mechanisms such as internal oxidation, sigma formation, carburization and sensitization towards inter-granular attack have been found inside the steel during heating. Thermo mechanical fatigue (TMF) causes intergranular cracks in the material of the travelling grate. Casting issues such as micro-segregation have also been addressed in this thesis. A few different ways to improve degradation resistance have been proposed, such as homogenization heat treatments, optimization of process parameters and inhibitor solutions.

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