Modeling of burden distribution in the blast furnace

The blast furnace is the main ironmaking production unit in the world which converts iron ore with coke and hot blast into liquid iron, hot metal, which is used for steelmaking. The furnace acts as a counter-current reactor charged with layers of raw material of very different gas permeability. The arrangement of these layers, or burden distribution, is the most important factor influencing the gas flow conditions inside the furnace, which dictate the efficiency of the heat transfer and reduction processes. For proper control the furnace operators should know the overall conditions in the furnace and be able to predict how control actions affect the state of the furnace. However, due to high temperatures and pressure, hostile atmosphere and mechanical wear it is very difficult to measure internal variables. Instead, the operators have to rely extensively on measurements obtained at the boundaries of the furnace and make their decisions on the basis of heuristic rules and results from mathematical models. It is particularly difficult to understand the distribution of the burden materials because of the complex behavior of the particulate materials during charging. The aim of this doctoral thesis is to clarify some aspects of burden distribution and to develop tools that can aid the decision-making process in the control of the burden and gas distribution in the blast furnace. A relatively simple mathematical model was created for simulation of the distribution of the burden material with a bell-less top charging system. The model developed is fast and it can therefore be used by the operators to gain understanding of the formation of layers for different charging programs. The results were verified by findings from charging experiments using a small-scale charging rig at the laboratory. A basic gas flow model was developed which utilized the results of the burden distribution model to estimate the gas permeability of the upper part of the blast furnace. This combined formulation for gas and burden distribution made it possible to implement a search for the best combination of charging parameters to achieve a target gas temperature distribution. As this mathematical task is discontinuous and non-differentiable, a genetic algorithm was applied to solve the optimization problem. It was demonstrated that the method was able to evolve optimal charging programs that fulfilled the target conditions. Even though the burden distribution model provides information about the layer structure, it neglects some effects which influence the results, such as mixed layer formation and coke collapse. A more accurate numerical method for studying particle mechanics, the Discrete Element Method (DEM), was used to study some aspects of the charging process more closely. Model charging programs were simulated using DEM and compared with the results from small-scale experiments. The mixed layer was defined and the voidage of mixed layers was estimated. The mixed layer was found to have about 12% less voidage than layers of the individual burden components. Finally, a model for predicting the extent of coke collapse when heavier pellets are charged over a layer of lighter coke particles was formulated based on slope stability theory, and was used to update the coke layer distribution after charging in the mathematical model. In designing this revision, results from DEM simulations and charging experiments for some charging programs were used. The findings from the coke collapse analysis can be used to design charging programs with more stable coke layers.

Young Registered Nurses’ Intent to Leave the Profession in Finland - A Mixed-Method Study

The purpose of this study was to examine and expand understanding concerning young Finnish registered nurses (RN) with an intention to leave the profession and the related variables, specifically when that intention has emerged before the age of 30. The overall goal of the study was to develop a conceptual model in relation to young RNs’ intention to leave the profession. Suggestions for policymakers, nurse leaders and nurse managers are presented for how to retain more young RNs in the nursing workforce. Suggestions for future nursing research are also provided. Phase I consists of two sequential integrative literature reviews of 75 empirical articles concerning nurses’ intention to leave the profession. In phase II, data had been collected as part of the Nurses’ Early Exit (NEXT) study, using the BQ-12 structured postal questionnaire. A total of 147 young RNs participated in the study. The data were analysed with statistical methods. In phase III, firstly, an in-depth interpretive case study was conducted in order to understand how young RNs explain and make sense of their intention to leave the profession. The data in this study consisted of longitudinal career stories by three young RNs. The data was analysed by using narrative holistic-content and thematic methods. Secondly, a total of 15 young RNs were interviewed in order to explore in-depth their experiences concerning organizational turnover and their intent to leave the profession. The data was analysed using conventional content analysis. Based on earlier research, empirical research on the young RNs intention to leave the profession is scarce. Nurses’ intention to leave the profession has mainly been studied with quantitative descriptive studies, conducted with survey questionnaires. Furthermore, the quality of previous studies varies considerably. Moreover, nurses’ intention to leave the profession seems to be driven by a number of variables. According to the survey study, 26% of young RNs had often considered giving up nursing completely and starting a different kind of job during the course of the previous year. Many different variables were associated with an intention to leave the profession (e.g. personal burnout, job dissatisfaction). According to the in-depth inquiries, poor nursing practice environments and a nursing career as a ‘second-best’ or serendipitous career choice were themes associated with young RNs’ intention to leave the profession. In summary, young RNs intention to leave the profession is a complex phenomenon with multiple associated variables. These findings suggest that policymakers, nurse leaders and nurse managers should enable improvements in nursing practice environments in order to retain more young RNs. These improvements can include, for example, adequate staffing levels, balanced nursing workloads, measures to reduce work-related stress as well as possibilities for advancement and development. Young RNs’ requirements to provide high-quality and ethical nursing care must be recognized in society and health-care organizations. Moreover, sufficient mentoring and orientation programmes should be provided for all graduate RNs. Future research is needed into whether the motive for choosing a nursing career affects the length of the tenure in the profession. Both quantitative and in-depth research is needed for the comprehensive development of nursing-turnover research.