An Experimental Study of Submerged Entry Nozzles (SEN) Focusing on Decarburization and Clogging

The submerged entry nozzle (SEN) is used to transport the molten steel from a tundish to a mould. The main purpose of its usage is to prevent oxygen and nitrogen pick-up by molten steel from the gas. Furthermore, to achieve the desired flow conditions in the mould. Therefore, the SEN can be considered as a vital factor for a stable casting process and the steel quality. In addition, the steelmaking processes occur at high temperatures around 1873 K, so the interaction between the refractory materials of the SEN and molten steel is unavoidable. Therefore, the knowledge of the SEN behaviors during preheating and casting processes is necessary for the design of the steelmaking processes  The internal surfaces of modern SENs are coated with a glass/silicon powder layer to prevent the SEN graphite oxidation during preheating. The effects of the interaction between the coating layer and the SEN base refractory materials on clogging were studied. A large number of accretion samples formed inside alumina-graphite clogged SENs were examined using FEG-SEM-EDS and Feature analysis. The internal coated SENs were used for continuous casting of stainless steel grades alloyed with Rare Earth Metals (REM). The post-mortem study results clearly revealed the formation of a multi-layer accretion. A harmful effect of the SENs decarburization on the accretion thickness was also indicated. In addition, the results indicated a penetration of the formed alkaline-rich glaze into the alumina-graphite base refractory. More specifically, the alkaline-rich glaze reacts with graphite to form a carbon monoxide gas. Thereafter, dissociation of CO at the interface between SEN and molten metal takes place. This leads to reoxidation of dissolved alloying elements such as REM (Rare Earth Metal). This reoxidation forms the “In Situ” REM oxides at the interface between the SEN and the REM alloyed molten steel. Also, the interaction of the penetrated glaze with alumina in the SEN base refractory materials leads to the formation of a high-viscous alumina-rich glaze during the SEN preheating process. This, in turn, creates a very uneven surface at the SEN internal surface. Furthermore, these uneven areas react with dissolved REM in molten steel to form REM aluminates, REM silicates and REM alumina-silicates. The formation of the large “in-situ” REM oxides and the reaction of the REM alloying elements with the previously mentioned SEN´s uneven areas may provide a large REM-rich surface in contact with the primary inclusions in molten steel. This may facilitate the attraction and agglomeration of the primary REM oxide inclusions on the SEN internal surface and thereafter the clogging. The study revealed the disadvantages of the glass/silicon powder coating applications and the SEN decarburization. The decarburization behaviors of Al2O3-C, ZrO2-C and MgO-C refractory materials from a commercial Submerged Entry Nozzle (SEN), were also investigated for different gas atmospheres consisting of CO2, O2 and Ar. The gas ratio values were kept the same as it is in a propane combustion flue gas at different Air-Fuel-Ratio (AFR) values for both Air-Fuel and Oxygen-Fuel combustion systems. Laboratory experiments were carried out under nonisothermal conditions followed by isothermal heating. The decarburization ratio (α) values of all three refractory types were determined by measuring the real time weight losses of the samples. The results showed the higher decarburization ratio (α) values increasing for MgO-C refractory when changing the Air-Fuel combustion to Oxygen-Fuel combustion at the same AFR value. It substantiates the SEN preheating advantage at higher temperatures for shorter holding times compared to heating at lower temperatures during longer holding times for Al2O3-C samples. Diffusion models were proposed for estimation of the decarburization rate of an Al2O3-C refractory in the SEN. Two different methods were studied to prevent the SEN decarburization during preheating: The effect of an ZrSi2 antioxidant and the coexistence of an antioxidant additive and a (4B2O3 ·BaO) glass powder on carbon oxidation for non-isothermal and isothermal heating conditions in a controlled atmosphere. The coexistence of 8 wt% ZrSi2 and 15 wt% (4B2O3 ·BaO) glass powder of the total alumina-graphite refractory base materials, presented the most effective resistance to carbon oxidation. The 121% volume expansion due to the Zircon formation during heating and filling up the open pores by a (4B2O3 ·BaO) glaze during the green body sintering led to an excellent carbon oxidation resistance. The effects of the plasma spray-PVD coating of the Yttria Stabilized Zirconia (YSZ) powder on the carbon oxidation of the Al2O3-C coated samples were investigated. Trials were performed at non-isothermal heating conditions in a controlled atmosphere. Also, the applied temperature profile for the laboratory trials were defined based on the industrial preheating trials. The controlled atmospheres consisted of CO2, O2 and Ar. The thicknesses of the decarburized layers were measured and examined using light optic microscopy, FEG-SEM and EDS. A 250-290 μm YSZ coating is suggested to be an appropriate coating, as it provides both an even surface as well as prevention of the decarburization even during heating in air. In addition, the interactions between the YSZ coated alumina-graphite refractory base materials in contact with a cerium alloyed molten stainless steel were surveyed. The YSZ coating provided a total prevention of the alumina reduction by cerium. Therefore, the prevention of the first clogging product formed on the surface of the SEN refractory base materials. Therefore, the YSZ plasma-PVD coating can be recommended for coating of the hot surface of the commercial SENs.

Automating condition monitoring of vegetation on railway trackbeds and embankments

Vegetation growing on railway trackbeds and embankments present potential problems. The presence of vegetation threatens the safety of personnel inspecting the railway infrastructure. In addition vegetation growth clogs the ballast and results in inadequate track drainage which in turn could lead to the collapse of the railway embankment. Assessing vegetation within the realm of railway maintenance is mainly carried out manually by making visual inspections along the track. This is done either on-site or by watching videos recorded by maintenance vehicles mainly operated by the national railway administrative body. A need for the automated detection and characterisation of vegetation on railways (a subset of vegetation control/management) has been identified in collaboration with local railway maintenance subcontractors and Trafikverket, the Swedish Transport Administration (STA). The latter is responsible for long-term planning of the transport system for all types of traffic, as well as for the building, operation and maintenance of public roads and railways. The purpose of this research project was to investigate how vegetation can be measured and quantified by human raters and how machine vision can automate the same process. Data were acquired at railway trackbeds and embankments during field measurement experiments. All field data (such as images) in this thesis work was acquired on operational, lightly trafficked railway tracks, mostly trafficked by goods trains. Data were also generated by letting (human) raters conduct visual estimates of plant cover and/or count the number of plants, either on-site or in-house by making visual estimates of the images acquired from the field experiments. Later, the degree of reliability of(human) raters’ visual estimates were investigated and compared against machine vision algorithms. The overall results of the investigations involving human raters showed inconsistency in their estimates, and are therefore unreliable. As a result of the exploration of machine vision, computational methods and algorithms enabling automatic detection and characterisation of vegetation along railways were developed. The results achieved in the current work have shown that the use of image data for detecting vegetation is indeed possible and that such results could form the base for decisions regarding vegetation control. The performance of the machine vision algorithm which quantifies the vegetation cover was able to process 98% of the im-age data. Investigations of classifying plants from images were conducted in in order to recognise the specie. The classification rate accuracy was 95%.Objective measurements such as the ones proposed in thesis offers easy access to the measurements to all the involved parties and makes the subcontracting process easier i.e., both the subcontractors and the national railway administration are given the same reference framework concerning vegetation before signing a contract, which can then be crosschecked post maintenance.A very important issue which comes with an increasing ability to recognise species is the maintenance of biological diversity. Biological diversity along the trackbeds and embankments can be mapped, and maintained, through better and robust monitoring procedures. Continuously monitoring the state of vegetation along railways is highly recommended in order to identify a need for maintenance actions, and in addition to keep track of biodiversity. The computational methods or algorithms developed form the foundation of an automatic inspection system capable of objectively supporting manual inspections, or replacing manual inspections.

Monitoring vegetation on railway embankments : supporting maintenance decisions

The national railway administrations in Scandinavia, Germany, and Austria mainly resort to manual inspections to control vegetation growth along railway embankments. Manually inspecting railways is slow and time consuming. A more worrying aspect concerns the fact that human observers are often unable to estimate the true cover of vegetation on railway embankments. Further human observers often tend to disagree with each other when more than one observer is engaged for inspection. Lack of proper techniques to identify the true cover of vegetation even result in the excess usage of herbicides; seriously harming the environment and threating the ecology. Hence work in this study has investigated aspects relevant to human variationand agreement to be able to report better inspection routines. This was studied by mainly carrying out two separate yet relevant investigations.First, thirteen observers were separately asked to estimate the vegetation cover in nine imagesacquired (in nadir view) over the railway tracks. All such estimates were compared relatively and an analysis of variance resulted in a significant difference on the observers’ cover estimates (p<0.05). Bearing in difference between the observers, a second follow-up field-study on the railway tracks was initiated and properly investigated. Two railway segments (strata) representingdifferent levels of vegetationwere carefully selected. Five sample plots (each covering an area of one-by-one meter) were randomizedfrom each stratumalong the rails from the aforementioned segments and ten images were acquired in nadir view. Further three observers (with knowledge in the railway maintenance domain) were separately asked to estimate the plant cover by visually examining theplots. Again an analysis of variance resulted in a significant difference on the observers’ cover estimates (p<0.05) confirming the result from the first investigation.The differences in observations are compared against a computer vision algorithm which detects the "true" cover of vegetation in a given image. The true cover is defined as the amount of greenish pixels in each image as detected by the computer vision algorithm. Results achieved through comparison strongly indicate that inconsistency is prevalent among the estimates reported by the observers. Hence, an automated approach reporting the use of computer vision is suggested, thus transferring the manual inspections into objective monitored inspections