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detailed programme - SIMS - Scandinavian Simulation Society

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Abstract: The transient temperature conditions near the bottom of well IDDP-1<br />

in Krafla, which was drilled into a magma intrusion, have been simulated by some<br />

simple models addressing: (i) evolution of temperature conditions at the magma<br />

intrusion, (ii) cooling of a permeable layer above the magma due to drilling<br />

circulation losses, (iii) reheating of the permeable layer after drilling and (iv)<br />

temperature evolution during discharge testing in 2010. The modelling is quite<br />

speculative because of limited data constraints, but results indicate that the<br />

temperature conditions and evolution can be explained by the models used. The<br />

possibility that the magma was emplaced during the Krafla volcanic episode 25 –<br />

35 years ago can neither be confirmed nor refuted, but if the intrusion is so old a<br />

thickness of at least 50 – 100 m is required. The effective thick¬ness of the<br />

permeable layer and its equilibrium temperature, are estimated to be about 45 m<br />

and 390 – 400°C, respectively. No direct contact with the magma is needed to<br />

explain the superheated (up to 380°C) steam discharged by well IDDP-1. The<br />

situation near the bottom of the well clearly warrants further study, both through<br />

more complex modelling and with further data-constraints.<br />

Authors: Guðni Axelsson, Thorsteinn Egilson, Sigríður S. Gylfadóttir.<br />

Paper 24<br />

Title: Condition monitoring of an epicyclic gearbox at a water power station<br />

Keyword: condition monitoring, diagnostics, higher order derivatives, epicyclic<br />

gearbox<br />

Abstract: Epicyclic gearing or planetary gearing is a gear system, which consists<br />

of one or more outer gears, or planet gears, revolving about a central, or sun<br />

gear. Typically, the planet gears are mounted on a movable arm or carrier which<br />

itself may rotate relative to the sun gear. Epicyclic gearboxes are challenging to<br />

monitor due to complex structure with many rolling elements. Complex structure<br />

and versatile components also mean a long stoppage if a failure occurs.<br />

Therefore, an early detection of incipient faults is important.<br />

In this paper, vibration analysis is used for condition monitoring of an epicyclic<br />

gearbox at a water power station. There is a distinct difference between vibration<br />

quantities: vibration velocity responds very well to the vibrations with frequencies<br />

less than 1000 Hz; an even better response is gained by acceleration and its<br />

higher derivatives, which also include more information on higher frequencies.<br />

Because of a quite high rotational speed of the output, the vibration velocity is<br />

not good enough for condition monitoring of the gear in question. Acceleration<br />

and its higher order derivatives should be used to get better responsiveness for<br />

changes in the condition of the gearbox.<br />

Modelling is needed to calculate the vibration components in the frequency range<br />

to identify the possible faulty components. There was also one vibration<br />

component in the gear, which source was not definitely discovered.<br />

Authors: Jussi Immonen, Sulo Lahdelma, Esko Juuso.<br />

Paper 18<br />

Title: Input Variable Selection in Modelling of Desulphurization Efficiency<br />

Keyword: desulphurization plant, input variable selection, modelling<br />

Abstract: Several methods are applied in order to find the input variables with<br />

predictive power to the modelling of SO2 removal efficiency in a desulphurization<br />

plant. In model development, the preliminary assumption is that one or several<br />

candidate variables are capable of describing some of the output behaviour. In<br />

non-linear and complex industrial processes, the nature of the relationships<br />

between the variables may be vague and a functional first principles based model<br />

may be impossible to define. Therefore, it is justified to use data-driven statistical<br />

modelling approaches such as artificial neural networks (ANNs). However, such<br />

models may become corrupted due to the inclusion of uninformative, weakly<br />

informative or redundant variables. In this study, the emphasis is on the selection

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