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Att kunna simulera en AOD-process (Argon, OxygenandDecarburization) är i dagsläget möjligt med hjälp av en programvara. En sådan programvara används till utbildning och forskning för utveckling av rostfritt stål.   Kobolde & Partners AB är ett företag som äger en sådan programvara. I detta projekt byggs programmet ut för att ge användare möjlighet att simulera en AOD-process med osäkerhet i mätdata. Genom detta kan forskare få ökad förståelse hur processens mätresultat skiljer sig med osäkerhet i mätdata. / : Being able to simulate an AOD process (Argon, Oxygen andDecarburization) is possible nowadays with help of software. This kind ofsoftware is used for education andresearch for development of stainless steel.   Kobolde & Partners AB is a company that owns this kind of software. This project will further develop this software to enable users to simulate an AOD process with uncertainty in measurement. This allows scientist to get more knowledge how a process measurement results

In this thesis, we have presented a systematic analysis of the change of cloud properties due to variation in aerosol concentration over Indian region using satellite observations, and Weather Research and Forecasting Model coupled with Chemistry (WRF-Chem) simulations. The Tropical Rainfall Measurement Mission (TRMM) based Microwave Imager (TMI) estimates (2A12) have been used to compare and contrast the characteristics of cloud liquid water and ice over the Indian land region and the surrounding oceans, during the pre-monsoon (May) and monsoon (June–September) seasons. Based on the spatial homogeneity of rainfall, we have selected five regions for our study (three over ocean, two over land). In general, we find that the mean cloud liquid water and cloud ice content of land and oceanic regions are different, with the ocean regions showing higher amount of CLW. A comparison across the ocean regions suggests that the cloud liquid water over the or graphically influenced Arabian Sea (close to the Indian west coast) behaves differently from the cloud liquid water over a trapped ocean (Bay of Bengal) or an open ocean (Equatorial Indian Ocean). Specifically, the Arabian Sea region shows higher liquid water for a lower range of rainfall, whereas the Bay of Bengal and the Equatorial Indian Ocean show higher liquid water for a higher range of rainfall. Apart from geographic differences, we also documented seasonal differences by comparing cloud liquid water profiles between monsoon and pre-monsoon periods, as well as between early and peak phases of the monsoon. We find that the cloud liquid water during the lean periods of rainfall (May or June) is higher than during the peak and late monsoon season (July-September) for raining clouds over central India. However, this is not true over the ocean. As active and break phases are important signatures of the monsoon progression, we also analyzed the differences in cloud liquid water during various phases of the monsoon, namely, active, break, active-to-break (a2b) and break-to-active (b2a) transition phases. We find that the cloud liquid water content during the b2a transition phase is significantly higher than that during the a2b transition phase over central India. We speculate that this could be attributed to higher amount of aerosol loading over this region during the break phase. We lend credence to this aerosol-liquid water/rain association by comparing the central Indian cloud liquid water with Southeast Asia (where the aerosol loading is significantly smaller) and find that in the latter region, there are no significant differences in cloud liquid water during the different phases of their monsoon. The second part of our study involves evaluating the ability of the Weather Research and Forecasting Model coupled with Chemistry (WRF-Chem) to simulate the observed variation of cloud liquid water and rain efficiency. We have used no chemistry option, and the model was run with constant aerosol concentration. The model simulations (at 4.5 km resolution) are done for the month of June–July 2004 since this period was particularly favorable for the study of an active–break cycle of the monsoon. We first evaluate the sensitivity of the model to different parameterizations (microphysical, boundary layer, land surface) on the simulation of rain over central India and Bay of Bengal. This is done to identify an “optimal” combination of parameterizations which reproduces the best correlation with observed rain over these regions. In this default configuration (control run), where the aerosol concentration is kept constant throughout the simulation period, the model is not able to reproduce the observed variations of cloud liquid water during the different phases of an active-break cycle. To this end, we proceeded to modify the model by developing an aerosol-rain relation, using Aerosol Robotic Network (AERONET) and TRMM 3B42 data that realistically captures the variation of aerosol with rain. It is worth highlighting here that our goal was to primarily isolate the indirect effect of aerosols in determining the observed changes in cloud liquid water (CLW) during the active-break phases of the Indian monsoon, without getting into the complexity of a full chemistry model such as that incorporated in WRF-Chem. Moreover, the proposed modification (modified run) is necessitated by the lack of realistic emission estimates over the Indian region as well as the presence of inherent biases in monsoon simulation in WRF. The main differences we find between the modified and control simulations is in the mean as well as spatial variability of CLW. We find that the proposed modification (i.e., rate of change of aerosol concentration as a function of rain rate) leads to a realistic variation in the CLW during the active-break cycle of Indian monsoon. Specifically, the peak value of CLW in the b2a (a2b) phase is larger (smaller) in the modified as compared to the control run. These results indicate a stronger change in CLW amount in the upper levels between the two transition phases in the modified scheme as compared to the control simulation. More significantly, we also observe a change in sign at the lower levels of the atmosphere, i.e., from a strong positive difference in the control run to a negative difference in the modified simulation, similar to that observed. Additionally, we investigated the impact of the proposed modification, via CLW changes, on cloud coverage, size of clouds and their spatial variability. We find that the transformation of optically thin clouds to thick clouds during the break phase was associated with larger cloud size in modified compared to the control simulation. Moreover, the higher rate of decay of the spatial variability of CLW with grid resolution, using the modified scheme, suggests that clusters of larger clouds are more in the modified compared to control simulation. Taken together, the interactive aerosol loading proposed in this thesis yields model simulations that better mimic the observed CLW variability between the transition phases.

The process industries span multiple industrial sectors and constitute a substantial part of the entire manufacturing industry. Since companies belonging to this family of industries are often very asset intensive, their ability to respond to changes is often limited in the short term. The adaptation of the capabilities of existing processes, and conversely finding products and market segments to match the production system capabilities, are an important part of product- and market development activities in the process industry. The importance to companies in the process industry of having a well-articulated manufacturing strategy congruent with the business strategy is second to none. However, to facilitate manufacturing strategy developments, it is essential to start with an improved characterization and understanding of the material transformation system. To that end an extensive set of variables was developed and related measures and scales were defined. The resulting configuration model, focusing on company generic process capabilities in the process industries, is to be regarded as a conceptual taxonomy and as a proposition available for further testing. The usability of the model was subsequently assessed using “mini-cases” in the forestry industry, where the respondents confirmed that the company’s overall strategy could benefit from this kind of platform as a possible avenue to follow. The model was deployed as an instrument in the profiling of company material transformation systems to facilitate the further development of companies' functional and business strategies. The use of company-generic production capabilities was studied in three case companies representing the mineral, food and steel industries. The model was found by the respondents to be usable as a knowledge platform to develop production strategies. In the final analysis of the research results, a new concept emerged called “production capability configuration": A process-industrial company’s alignment of its generic production capabilities in the areas of raw materials, process technology and products to improve the consistency among the variable elements that define operations and improve the congruence between operations and its environment. From the perspective of value creation and capture, firms must be able to manufacture products in a competitive cost structure within the framework of a proper business model. By using the configuration model, the relationship between manufacturing and innovation activities has been studied in the previously mentioned three case studies. In many cases the gap in capability appears as a limitation in the production system, requiring development efforts and sometimes investments to overcome. This is illustrated with two examples from the steel industry, where development efforts of the production system capabilities are initiated to better match the market demands. One example is the increase the volume- and product flexibility of an existing stainless steel melt shop, resulting in a proposed oblong Argon Oxygen Decarburisation (AOD) converter configuration that was subsequently verified using water modelling. The second example is from a carbon steel mill, where the target was to increase the raw material- and volume flexibility of another melt shop, by modifying the capabilities of the Electric Arc Furnace (EAF). Enabling EAF technologies are further described and evaluated using operational data and engineering type of estimates. / <p>QC 20170116</p>

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