The present work focuses on mineral reactions and slag formation of LKAB olivine iron ore pellets (MPBO) subjected to reducing conditions in the LKAB experimental blast furnace (EBF). The emphasis is on olivine reactions with surrounding iron oxides. Many factors influence the olivine behaviour. The study was performed by use of micro methods; optical microscopy, micro probe analysis, micro Raman and Mössbuer spectroscopy and thremodynamic modeling. During manufacturing, in oxidising atmosphere at high temperature (1350°C), olivine alterations occur through slag formation and rim reactions with iron oxides and other additives. To be able to describe olivine behaviour in the rather complex blast furnace reduction process one has to consider factors such as reactions kinetics, reduction degree of iron oxides, vertical and horizontal position in the furnace and reactions with alkali. Samples were collected from the EBF both from in shaft probing during operation and from excavation following quenching of the EBF. The initial slag forming olivine consist of primary forsterite – (Mg1.9Fe0.1)SiO4 – with inclusions of hematite and an amorphous silica rich phase, a first corona with lamellae of magnesioferrite, olivine and orthopyroxene, a second corona of amorphous silica and magnesioferrite. During reduction in the upper shaft in the EBF (700-900°C) Fe3+ reduces to Fe2+. The amorphous silica in the second corona absorbs alkali, Al, Fe2+, Mg, and Ca and form glasses of varying compositions. The lamellae in the first corona will merge into a single phase olivine rim. With further reduction the glasses in the second corona will merge with the olivine rim forming an iron rich olivine rim and leaving the elements that do not fit into the olivine crystal lattice as small silicate glass inclusions. Diffusion of magnesium and iron between olivines and iron oxides increase with increasing temperature in the lower shaft of the EBF (750-1100°C). In the cohesive zone of the EBF (1100-1200°C) Fe2+ is not stable any longer and Fe2+ will be expelled from the olivine as metallic iron blebs, and the olivine will form a complex melt with a typical composition of alkali-Al2O3-MgO-SiO2. Alkali plays an important role in this final olivine consumption. The quench time for samples collected with probes and excavation are minutes respectively hours. A study of the quench rate’s effect on the phases showed no differences in the upper shaft. However, in the lower shaft wüstite separates into wüstite and magnetite when wüstite grows out of its stability field during slow cooling of excavated samples. There is also a higher alkali and aluminium deposition in the glass phases surrounding olivines in excavated pellets as a result of alkali and aluminium gas condensing on the burden in the EBF during cooling. Coating applied to olivine pellets was studied in the EBF with the aim to investigate its behaviour, particularly its ability to capture alkali. The coating materials were kaolinite, bauxite, olivine and limestone. No significant reactions were observed in the upper shaft. In the lower shaft a majority of the phases were amorphous and reflecting the original coating compositions. Deposition from the EBF gas phase occurs and kalsilite (KAlSiO4) is found in all samples; coating used for binding alkali is redundant from a quality perspective.

This master’s thesis evaluates five existing Convolutional Neural Network (CNN) models for semantic segmentation of optical microscopy images of iron ore pellets. The models are PSPNet, FC-DenseNet, DeepLabv3+, BiSeNet and GCN. The dataset used for training and evaluation contains 180 microscopy images of iron ore pellets collected from LKAB’s experimental blast furnace in Luleå, Sweden. This thesis also investigates the impact of the dataset size and data augmentation on performance. The best performing CNN model on the task was PSPNet, which had an average accuracy of 91.7% on the dataset. Simple data augmentation techniques, horizontal and vertical flipping, improved the models’ average accuracy performance with 3.4% on average. From the results in this thesis, it was concluded that there are benefits to using CNNs for analysis of iron ore pellets, with time-saving and improved analysis as the two notable areas.

[pt] As pelotas de minério de ferro são produzidas a partir de um processo de aglomeração de finos de minério denominado pelotização, e possuem granulometria adequada para utilização em fornos siderúrgicos. Nesta dissertação dois fenômenos associados às superfícies das pelotas foram estudados: a formação de trincas e a presença de recobrimento (coating). Durante a pelotização, as pelotas são submetidas a diversos esforços compressivos e mudanças bruscas de temperatura. Desta forma, são geradas trincas em sua superfície, que são prejudiciais à resistência e ao desempenho nos fornos de redução. Já durante o processo de redução pode ocorrer a formação de pontes de ferro entre as pelotas, que se aglomeram formando clusters que comprometem o fluxo de gases no interior dos fornos. Este problema pode ser minimizado recobrindo as pelotas com uma mistura a base de óxidos de magnésio, o coating, que inibe a formação das pontes. Tendo em vista a importância de caracterizar trincas e coating na superfície das pelotas, a presente dissertação desenvolveu metodologias de aquisição, processamento e análise digital de imagens adquiridas com um estereoscópio. Foram desenvolvidos porta-amostras ajustáveis que permitiram a aquisição de imagens 2D de pelotas aproximadamente esféricas de diferentes tamanhos, cobrindo a maior parte da superfície e evitando a sobreposição de regiões de análise. A rotina de análise de trincas comparou dois métodos de segmentação e forneceu atributos como espessura média, fração de área e comprimento. A rotina de análise de coating utilizou segmentação por limiarização e mediu a fração de área ocupada em cada pelota. O uso dos porta-amostras foi fundamental para o sucesso do procedimento de aquisição. As rotinas de análise de trincas ou de coating se mostraram robustas para diferentes amostras. / [en] Iron ore pellets are produced from an ore fines agglomeration process called pelletizing, and are suitable for use in steel furnaces. In this dissertation two phenomena associated with the pellet surfaces were studied: crack formation and the presence of coating. During pelletizing, the pellets undergo various compressive forces and sudden changes in temperature. In this way, cracks are generated on its surface, which are detrimental to strength and performance in reduction furnaces. Already during the reduction process the formation of iron bridges can occur between the pellets, which clump forming clusters that compromise the flow of gases inside the furnaces. This problem can be minimized by coating the pellets with a magnesium oxide coating, which inhibits the formation of bridges. Given the importance of characterizing cracks and coating on the surface of the pellets, this dissertation developed methodologies for acquisition, processing and digital analysis of images acquired with a stereoscope. Adjustable sample holders were developed which allowed the acquisition of 2D images of approximately spherical pellets of different sizes, covering most of the surface and avoiding overlapping analysis regions. The crack analysis routine compared two segmentation methods and provided attributes such as mean thickness, area fraction and length. The coating analysis routine used threshold segmentation and measured the fraction of area occupied in each pellet. The use of the sample holders was fundamental to the success of the acquisition procedure. Crack analysis or coating routines were robust for different samples.

The dust particles generated due to mechanical wear of iron ore pellets and clusters formed in molten stainless steel alloyed with rare earth metals (REM) are considered in this study. Firstly, the influence of the characteristics of iron ore pellets, applied load on a pellet bed and partial reduction of the pellets on the size distribution of the generated dust was investigated. Secondly, REM clusters are investigated to evaluate the size distribution of the clusters. Also, an extreme value distribution (EVD) analysis has been applied for the observed REM clusters. The large sized pellets showed 10-20% higher wear rate than small sized pellets during wear in a planetary mill. Moreover, an increase of ~67% was observed in the friction and dust generation in the pellet bed as the applied load increased from 1 to 3 kg. Also, it was observed that a higher friction in the pellet bed can lead to an increased amount of airborne particles. The mechanical wear experiments of pellets reduced at 500 °C (P500) and 850 °C (P850) showed that P500 pellets exhibit ~16-35% higher wear rate than unreduced pellets. For the P850 pellets, the wear is inhibited by formation of a metallic layer at the outer surface of the pellets. The mechanism of dust generation has been explained using the obtained results. A reliable cluster size distribution of REM clusters was obtained by improving the observation method and it was used to explicate the formation and growth mechanism of REM clusters. The results show that the growth of clusters is governed by different types of collisions depending on the size of the clusters. For EVD analysis three different size parameters were considered. Moreover, using the maximum length of clusters results in a better correlation of EVD regression lines compared to other size parameters. Moreover, a comparison of predicted and observed maximum lengths of clusters showed that further work is required for the application of EVD analyses for REM clusters. / Studien fokuserar på två olika typer av mikropartiklar som är valda från olika delar av järn- och ståltillverkningsprocessen. Dessa partiklar är dels stoft som genereras på grund av mekanisk nötning av partiklar och dels klusters som bildas i flytande rostfria stål legerade med sällsynta jordartsmetaller (REM). Inledningsvis så undersöktes inverkan av tre faktorer på storleksfördelningen hos stoft som bildas vid hantering av järnoxidpellets. De undersökta faktorerna inkluderade karakteristiken hos järnoxidpellets, det applicerade trycket på pelletsbädden och den partiella reduktionen av järnoxidpellets. Därefter så utfördes tredimensionella undersökningar av REM kluster som extraherats med hjälp av elektrolytisk extraction för att bestämma storleksfördelningen hos klustren. Dessutom så utfördes en extremvärdesdistribution (EVD) studie för de studerade klustren. En planetkvarn användes för att undersöka inverkan of karakeristiken hos pellets på stoftbildningen. Resultaten visade att storleken på pellets kan påverka nötningshastigheten under dessa försöksförhållanden. Pellets som hade en större storlek (13.5&lt; Deq &lt;15.0 mm) uppvisade en 10 till 20% högre nötningshastighet i jämförelse med mindre pellets (9.5&lt; Deq &lt;12.5 mm). Baserat på analyserna av stoftet som genererades under nötningsexperimenten så konstaterades att nötningsmekanismerna för dessa pellets var abrasions- och kollisionsnötning. En pelletsbädd skapades för att möjliggöra studier av inverkan av ett applicerat tryck på stoftbildningen och friktionskrafterna i en pelletsbädd. Ett varierat tryck på mellan 1 till 3 kg applicerades på pelletsbädden. Resultaten visade att en ökning på ~67% av friktionskraften och stoftbildningen ägde rum när det applicerade trycket ökades från 1 till 3kg. Dessutom så visade resultaten att en högre friktionskraft i pelletsbädden kan resultera in en ökad mängd luftburna partiklar. Den mekaniska nötningen av pellets som reducerats vid 500 °C (P500) och 850 °C (P850) studerades också genom användande av en planetkvarn. Resultaten visade att P500 pellets uppvisade en ~ 16 till 35% högre nötningshastighet i jämförelse med oreducerade referenspellets.  Resultaten för P850 pellets visade att den mekaniska nötningen motverkades genom bildningen av ett metalliskt skikt på den yttre delen av pelletsen. Resultaten visade också att stoftet som bildats pga mekanisk nötning av reducerade pellets innehöll 3 till 6 gånger mer grova partiklar  (&gt;20µm) i jämförelse med stoft som bildats från oreducerade pellets. Slutligen så diskuterades hur dessa resultat kan relateras till industriella förhållanden med avseende på mekanismerna som är involverade i den mekaniska nötningen av pellets samt med avseende på relationen mellan hastigheten av de utgående gaserna och storlken och morfologin hos stoftpartiklarna. Klusters innehållande REM-oxider som extraherats från en 253MA rostfri stålsort undersöktes med användande av en tredimensionell teknik. En trovärdig storleksfördelning av klusters (CSD) erhölls genom att förbättra undersökningsmetoden och denna användes för att studera bildningen och tillväxten av REM oxider. Dessutom så användes cirkularitetsfaktorn hos klusters för att dela in klustren i två olika grupper, vilka bildas och tillväxer enligt olika mekanismer. Resultaten visade också att tillväxten av klusters gynnas av olika typer av kollisioner som beror av av storleken på klusters. För REM-klusters så drogs slutsatsen att turbulenta kollisioner är den huvudsakliga mekanismen som påverkar tillväxten. Avhandlingen behandlar även problemet om hur det är möjligt att hantera synfält där det inte förekommer kluster vid en extremvärdesdistribution (EVD) analys. Tre olika parametrar undersöktes i EVD analysen. Resultaten visar att om den maximala längden på kluster (LC) används i analysen så erhålls den bästa korrelationen gällande regressionslinjen för en EVD analys. Specifikt så var R2 värdet upp till 0.9876 i jämförelse med de andra storleksparametrarna som har värden i intervallet 0.9656 – 0.9774. Slutligen så visar resultaten från en jämförelse mellan beräknade och observerade maximala klusterlängder att EVD analyser för studier av REM kluster behöver undersökas ytterligare i framtiden. / <p>QC 20161128</p>

Modeling and simulation of granular matter has important applications in both natural science and industry. One widely used method is the discrete element method (DEM). It can be used for simulating granular matter in the gaseous, liquid as well as solid regime whereas alternative methods are in general applicable to only one. Discrete element analysis of large systems is, however, limited by long computational time. A number of solutions to radically improve the computational efficiency of DEM simulations are developed and analysed. These include treating the material as a nonsmooth dynamical system and methods for reducing the computational effort for solving the complementarity problem that arise from implicit treatment of the contact laws. This allow for large time-step integration and ultimately more and faster simulation studies or analysis of more complex systems. Acceleration methods that can reduce the computational complexity and degrees of freedom have been invented. These solutions are investigated in numerical experiments, validated using experimental data and applied for design exploration of iron ore pelletising systems. / <p>This work has been generously supported by Algoryx Simulation, LKAB (dnr 223-</p><p>2442-09), Umeå University and VINNOVA (2014-01901).</p>

[pt] A porosidade e o arranjo espacial dos poros são essenciais para a transferência de calor e para o processo de redução das pelotas de minério de ferro em fornos siderúrgicos. Portanto, a caracterização microestrutural das pelotas torna-se importante para o controle de qualidade do produto final, o aço, auxiliando no entendimento de seu comportamento nos altos-fornos. Atualmente, as técnicas mais utilizadas para a caracterização são a microscopia ótica, que oferece resultados somente bidimensionais e com isso não representa exatamente a realidade; e a Porosimetria por intrusão de mercúrio, na qual utiliza-se mercúrio, que é altamente nocivo à saúde humana, e avalia apenas poros conectados com a superfície. Além disso, são técnicas consideradas destrutivas, ou seja, não é possível fazer outras análises porque há a perda do material. Este trabalho propõe otimizar uma metodologia de caracterização tridimensional de porosidade em pelotas a partir da técnica de Microtomografia Computadorizada de Raios X (microCT) – que é uma técnica não destrutiva e fornece informações tridimensionais, porém apresenta limitações relacionadas ao tempo de análise e resolução – e análise e processamento das imagens geradas. Foi possível caracterizar em 3D a porosidade de amostras cedidas pela empresa Vale, a partir da distribuição espacial e obtenção do volume dos poros, além da discriminação de poros abertos e fechados por uma nova metodologia desenvolvida. Assim, a metodologia de aquisição foi otimizada, alcançando-se uma redução de tempo para todas as análises - foram necessárias 3 horas para a análise de uma pelota inteira. Confirmou-se que a resolução de fato causa grande impacto na caracterização de porosidade em pelotas de minério de ferro, evidenciado na grande diferença entre os percentuais de porosidades medidos nas diferentes resoluções alcançadas: 14,83 por cento para 7,6 micrometros, 23,69 por cento para 4 micrometros e 26,75 por cento para 2 micrometros. / [en] Porosity and pore space arrangement are essential for heat transfer and the reduction process of iron ore pellets in steelworks. Therefore, the pellet microstructural characterization becomes important for the quality control of the final product, steel, helping in the understanding of its behavior in the blast furnaces. Currently, the most used techniques for characterization are optical microscopy, which offers only two-dimensional results and thus does not represent exactly the reality; and mercury intrusion porosimetry that evaluates only pores connected to the surface, and uses mercury, which is highly harmful to human health. Moreover, they are techniques considered destructive as it is not possible to do other analyzes in the same samples, since they are destroyed. This work proposes to optimize a methodology of three-dimensional characterization of porosity in pellets using the technique of x-ray microtomography (microCT). This is a non - destructive technique that provides 3D information, but presents limitations related to the time of analysis and resolution. It was possible to characterize in 3D pellet samples provided by the Vale company, obtaining the porosity and the pore volume distribution. Open and closed porosity was also measured by a new developed methodology. Thus, the acquisition methodology was optimized, reaching a reduction of time for all the analyzes - it took 3 hours for the analysis of an entire ball. It was confirmed that the resolution had a great impact on the porosity characterization of iron ore pellets, evidenced by the great difference between the porosities measured at the different resolutions reached: 14.83 percent for 7.6 micrometers, 23.69 percent for 4 micrometers and 26.75 percent for 2 micrometers.

This master’s thesis evaluates automatic data annotation and machine learning predictions of iron ore pellets using tools provided by Amazon Web Services (AWS) in the cloud. The main tool in focus is Amazon SageMaker which is capable of automatic data annotation as well as building, training and deploying machine learning models quickly. Three different models was trained using SageMakers built in semantic segmentation algorithm, PSP, FCN and DeepLabV3. The dataset used for training and evaluation contains 180 images of iron ore pellets collected from LKAB’s experimental blast furnace in Luleå, Sweden. The Amazon Web Services solution for automatic annotation was shown to be of no use when annotating microscopic images of iron ore pellets. Ilastik which is an interactive learning and segmentation toolkit showed far superiority for the task at hand. Out of the three trained networks Fully-Convolutional Network (FCN) performed best looking at inference and training times, it was the quickest network to train and performed within 1% worse than the fastest in regard to inference time. The Fully-Convolutional Network had an average accuracy of 85.8% on the dataset, where both PSP &amp; DeepLabV3 was showing similar performance. From the results in this thesis it was concluded that there are benefits of running deep neural networks as a cloud service for analysis and management ofiron ore pellets.

<p>  </p> <p>Direct reduction ironmaking has gained popularity as a low carbon alternative to the typical blast furnace ironmaking route. A popular method of producing direct reduced iron is through the reduction of iron ore pellets in a reduction shaft furnace. Critical to this process is the use of a reliable continuous pellet feed system to provide a steady flow of pellets to the furnace. Therefore, any disruption in pellet flow can have a significant negative impact on the production rate of iron. </p> <p><br></p> <p>An iron ore pellet feed system for a direct reduction ironmaking furnace is jamming during winter operation. The pellets are jamming in a hopper at the top of the feed system above the furnace, and a hot gas, that seals off the furnace flue gas, flows counter to the pellets. A computational model of the feed system is built utilizing the discrete element method and computational fluid dynamics, using Siemen’s commercial multiphysics software Star-CCM+, to study the conditions that cause the jam to occur. The study is divided into six parts: pellet bulk flow calibration, computational cost reduction, modeling of the baseline operation, modeling the effect of moisture, development of a thermal model, and investigation of the minimal amount of icy and wet material to jam the system. The findings show that the location of jamming during operation matches the area in the simulation where it is most likely to occur, and that moisture alone is unlikely to result in jamming. Results indicate that the system will jam when charged with a minimum of 15% icy pellets, and when charged with 10% icy together with 5% wet pellets. Experimental work is recommended to validate the findings and to calibrate the simulations accordingly.</p>

[pt] O processo de formação de pelotas de minério de ferro consiste na preparação das matérias-primas, formação da pelota crua e endurecimento por meio da queima. O produto final deve ser um material poroso que permita a difusão de gases no forno de redução e que, simultaneamente, resista a compressão, característica relevante durante o transporte e no carregamento do forno. No entanto, durante o tratamento térmico e o transporte podem surgir trincas que comprometem a integridade das pelotas. A discriminação de poros e trincas é, portanto, um importante fator para a análise microestrutural e controle de qualidade do material. A microtomografia de raios-x é uma técnica não destrutiva que gera imagens tridimensionais, o que permite uma visualização completa da pelota. No entanto, a metodologia usual de processamento digital de imagens, baseada em extração de atributos de tamanho e forma, apresenta limitações para discriminar poros de trincas. Redes Neurais Deep Learning são uma alternativa poderosa para classificar tipos de objetos em imagens, utilizando como entrada as intensidades dos pixels e atributos automaticamente determinados pela rede. Após treinar um modelo com os padrões correspondente a cada classe, é possível atribuir cada pixel da imagem a uma das classes presentes, permitindo uma segmentação semântica. Nesta dissertação, otimizou-se uma rede Deep Learning com arquitetura U-Net, usando como conjunto de treinamento poucas camadas 2D da imagem 3D original. Aplicando o modelo à pelota utilizada no treinamento foi possível discriminar poros de trincas de forma adequada. A aplicação do modelo a outras pelotas exigiu a incorporação de camadas destas pelotas ao treinamento e otimização de parâmetros do modelo. Os resultados apresentaram classificação adequada, apesar de apresentar dificuldades de criar um modelo geral para discriminação entre poros e trincas em pelotas de minério de ferro. / [en] The iron ore pellet forming process consists of preparing the raw materials, forming the raw pellet and hardening by firing. The end product must be a porous material which allows gas to diffuse in the blast furnace and at the same time resists compression, which is a relevant feature during transport and loading of the furnace. However, during heat treatment and transport cracks may appear that compromise the integrity of the pellets. The discrimination of pores and cracks is therefore an important factor for microstructural analysis and material quality control. X-ray microtomography is a non-destructive technique that generates three-dimensional images, allowing a full view of the pellet. However, the usual methodology of digital image processing, based on extraction of size and shape attributes, has limitations to discriminate crack from pores. Deep Learning Neural Networks are a powerful alternative to classifying object types in images, using as input the pixel intensities and attributes automatically determined by the network. After training a model with the patterns corresponding to each class, it is possible to assign each pixel of the image to one of the classes present, allowing a semantic segmentation. In this dissertation, a Deep Learning network with U-Net architecture was optimized, using as a training set a few 2D layers of the original 3D image. Applying the model to the pellet used in training it was possible to discriminate cracks pores properly. Application of the model to other pellets required the incorporation of layers of these pellets into the training and optimization of model parameters. The results were adequately classified, despite the difficulty of creating a general model for discrimination between pores and cracks in iron ore pellets.

Luossavaara-Kiirunavaara Aktiebolag (LKAB) är en gruvdriftskoncern med järnmalmsbrytning som huvudsaklig verksamhet. En av produkterna tas fram genom att raffinera järnmalm till pellets vilket är en efterfrågad produkt vid ståltillverkning.   Vid sintring av råkulor till pellets är temperaturen väsentlig för att få så hög oxidation som möjligt av magnetit till hematit och för att få rätt fasthet. För att reglera värmen bör temperaturen mätas bland råkulorna eftersom det är kulornas temperatur som är det viktiga.                                   Projektet går ut på att undersöka möjligheterna att mäta temperatur bland råkulor på gratevagnar som åker genom en pelletsugn med ett permanent mätsystem. Här ställs höga krav på mätsystemet på grund av miljö, portabilitet och drifttid. Miljöanalyser gjordes för att ta reda på vilken påfrestning ett mätsystem bör klara av. Förslag om placering av mätutrustning framtogs genom kartläggning av miljön. En värmeundersökning utfördes av vad hög värme har för påverkan på elektroniska komponenter som kan behövas till ett mätsystem. Det gjordes även en undersökning över vilka komponenter som kan klara av den värme ett mätsystem kan bli utsatt för. Utifrån detta kunde ett prototypmätsystem konstrueras för att mäta temperaturen där ett mätsystem kan placeras på en gratevagn samt för att skapa möjlighet för mätningar med termoelement i bädd. Mätsystemet skapades med trådlös kommunikation, möjlighet till loggning av data och med möjlighet att ansluta tre termoelement av typ S. Olika typer av trådlös kommunikation utvärderades för att se vad som kan vara mest lämpat med tanke på miljö, räckvidd, strålning och strömförbrukning. Strömförsörjning undersöktes för att hitta den mest lämpade strömförsörjningen med längst drifttid och högst funktionalitet. En utredning gjordes för att hitta monteringsmöjligheter av ett mätsystem på en gratevagn. Värmeavskärmning och isolering undersöktes för att få ner temperaturen i kapslingen och för elektroniken i ett mätsystem. Några olika temperaturgivare granskades för att hitta en som klarar av att mäta temperaturer upp till 1500°C och som fysiskt kan monteras på en gratevagn. / Luossavaara-Kiirunavaara Aktiebolag (LKAB) is a mining group with iron ore mining as the main business. One of the products is produced by refining iron ore to iron pellets, which is a quality and environmentally clever product  in steel production.                                     When iron pellets are produced by roasting green pellets the temperature is very important. The goal is to get as much oxidation as possible from hematite to magnetite and to get correct mechanical properties. To control the process it’s important to know the temperature of the green pellets specifically and not the surrounding mechanical structure, ceramics lining in the furnace and the influence from radiation.   The goal of the project is to examine possibilities to measure temperature with a permanent measurement system of green pellets which are transported on grate cars trough pellet plant. The measurement system has to meet some requirements of environment, portability and operating time. Analysis of the environment has been made to get specification of what a measurement system has to withstand. Proposition about assembly location has been investigated by analysis of the environment. A thermal analysis has been made of electronic components, especially electronic components that can be used in a measurement system. A analysis has also been made of which electronic components that are suited for a measurement system and can withstand the heat. A development measurement system has been developed from the results of the analysis. The system has been constructed to measure the temperature of what a system can be exposed of on a grate car. This with wireless and data-logging support and future measurements possibilities with three thermocouples of type S.   Wireless data transfer was examined to see what type is most suited for the task with knowledge of environment, transmission range, radiation and power consumption. Power sources was examined to find a power source with longest working time and highest functionality. Heat insulation and reflective materials has been investigated with the goal to get the temperature in the system enclosure as low as possible. Some different types of temperature sensors that can measure temperatures up to 1500°C and which can be mounted on a grate car has been evaluated.