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Modelling of screw compressor plant operation under intermittent conditions

Chukanova, E. (2016). Modelling of screw compressor plant operation under intermittent conditions. (Unpublished Doctoral thesis, City, University of London)

Abstract

Compressor plant frequently operates under unsteady conditions. This is due to pressure fluctuations, variable flow demand, or unsteady inlet conditions, as well as shaft speed variation. Also, following demand, compressor plants often work intermittently with frequent starts and stops. This may cause premature wear, decrease of compressor performance and even failure, which might cost millions of pounds to industry in downtime. However, there is still a lack of published data which describes intermittent plant behaviour, or predicts the effects of unsteady operation upon compressor plant performance. Thus, there appears to be a need to develop a mathematical model to calculate compressor plant performance during intermittent operating conditions and to verify this model with experimental data.

Accordingly, this thesis describes an experimental and analytical study of screw compressor plant operating under unsteady conditions. For this purpose a one-dimensional model of the processes within a compressor was used, based on the differential equations of conservation of mass and energy, extended to include other plant components, such as storage tanks, control valves and connecting pipes. The model can simulate processes in both oil-free and oil-injected compressor plants during transient operation, including the effects of sudden changes in pressure, speed and valve area. Performance predictions obtained from the model gave good agreement with test results.

This model can, therefore, be used to predict a variety of events, which may occur in everyday compressor plant operation.

Publication Type: Thesis (Doctoral)
Subjects: T Technology > TJ Mechanical engineering and machinery
Departments: Doctoral Theses
School of Science & Technology > Engineering
School of Science & Technology > School of Science & Technology Doctoral Theses
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