A GUI for online presentation of steel and steelmaking ladle temperature data and simulation.

Continuous casting is a casting process that produces steel slabs in a continuous manner with steel being poured at the top of the caster and a steel strand emerging from the mould below. Molten steel is transferred from the AOD converter to the caster using a ladle. The ladle is designed to be strong and insulated. Complete insulation is never achieved. Some of the heat is lost to the refractories by convection and conduction. Heat losses by radiation also occur. It is important to know the temperature of the melt during the process. For this reason, an online model was previously developed to simulate the steel and ladle wall temperatures during the ladle cycle. The model was developed as an ODE based model using grey box modeling technique. The model’s performance was acceptable and needed to be presented in a user friendly way. The aim of this thesis work was basically to design a GUI that presents steel and ladle wall temperatures calculated by the model and also allow the user to make adjustments to the model. This thesis work also discusses the sensitivity analysis of different parameters involved and their effects on different temperature estimations.

Estimation of genetic variance for macro- and micro-environmental sensitivity using double hierarchical generalized linear models

Background: Genetic variation for environmental sensitivity indicates that animals are genetically different in their response to environmental factors. Environmental factors are either identifiable (e.g. temperature) and called macro-environmental or unknown and called micro-environmental. The objectives of this study were to develop a statistical method to estimate genetic parameters for macro- and micro-environmental sensitivities simultaneously, to investigate bias and precision of resulting estimates of genetic parameters and to develop and evaluate use of Akaike’s information criterion using h-likelihood to select the best fitting model. Methods: We assumed that genetic variation in macro- and micro-environmental sensitivities is expressed as genetic variance in the slope of a linear reaction norm and environmental variance, respectively. A reaction norm model to estimate genetic variance for macro-environmental sensitivity was combined with a structural model for residual variance to estimate genetic variance for micro-environmental sensitivity using a double hierarchical generalized linear model in ASReml. Akaike’s information criterion was constructed as model selection criterion using approximated h-likelihood. Populations of sires with large half-sib offspring groups were simulated to investigate bias and precision of estimated genetic parameters. Results: Designs with 100 sires, each with at least 100 offspring, are required to have standard deviations of estimated variances lower than 50% of the true value. When the number of offspring increased, standard deviations of estimates across replicates decreased substantially, especially for genetic variances of macro- and micro-environmental sensitivities. Standard deviations of estimated genetic correlations across replicates were quite large (between 0.1 and 0.4), especially when sires had few offspring. Practically, no bias was observed for estimates of any of the parameters. Using Akaike’s information criterion the true genetic model was selected as the best statistical model in at least 90% of 100 replicates when the number of offspring per sire was 100. Application of the model to lactation milk yield in dairy cattle showed that genetic variance for micro- and macro-environmental sensitivities existed. Conclusion: The algorithm and model selection criterion presented here can contribute to better understand genetic control of macro- and micro-environmental sensitivities. Designs or datasets should have at least 100 sires each with 100 offspring.