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The aim of this research work was to develop a boiler model with few parameters required for energy planning. The showcase considered for this work was the boiler system of the energy center at Offenburg University of Applied Sciences. A grey box model of the boiler was developed systematically starting from model abstraction, simplification, model break-down and to the use of empirical correlations wherever necessary to describe the intermediate effects along with the use of information from manufacturer’s specification in order to reduce parameters. This strategy had resulted in a boiler model with only 6 parameters, namely, nominal burner capacity, water gallery capacity, air ratio, heat capacity of wall, thermal conductance on flue gas and hot water side. Most of these parameters can be obtained through the information available in the spec sheets and thus an energy planner will be able to parameterize the model with low effort. The model was validated with the monitored data of the showcase. It was tested for the start-up, shut-down behavior and the effect of storage.
Electric arc furnaces (EAF) are complex industrial plants whose actual behavior depends upon numerous factors. Due to its energy intensive operation, the EAF process has always been subject to optimization efforts. For these reasons, several models have been proposed in literature to analyze and predict different modes of operation. Most of these models focused on the processes inside the vessel itself. The present paper introduces a dynamic, physics-based model of a complete EAF plant which consists of the four subsystems vessel, electric system, electrode regulation, and off-gas system. Furthermore the solid phase is not treated to be homogenous but a simple spatial discretization is employed. Hence it is possible to simulate the energy input by electric arcs and fossil fuel burners depending on the state of the melting progress. The model is implemented in object-oriented, equation-based language Modelica. The simulation results are compared to literature data.