Conference Agenda

To align with global climate change mitigation goals, industrial companies are reassessing their carbon footprint. This reevaluation is spurred by heightened industry awareness of its contribution to global warming, with government incentives and regulatory requirements further propelling this commitment. High-energy consumption sectors, particularly air compression, are receiving special attention. This study compares high-speed direct drive centrifugal compressors and oil-free screw compressors , both supplied by VFDs, analyzing boundary conditions, component performances, and energy consumption. Results include the energy efficiency savings, weight reduction benefits and total cost of ownership (TCO) comparisons over 10 years. The results emphasize the potential of high-speed centrifugal compressors for sustainable industrial practices.

Twin screw machines that are used in vacuum pump application have large wrap angle and length-to-diameter ratio in comparison to classical twin screw machines used in compressor applications. However, the compressor type asymmetric rotor profile with lower wrap angle can be re-designed such that the machine can switch its operation from medium pressure ratio above ambient to a vacuum operation from very low pressure to ambient i.e., high pressure ratio. This switching results into a high heat generation rate within the rotors and demands for additional cooling measures in the rotor design. In this study, CFD model of a 3-5 Twin screw vacuum pump was used to evaluate the thermal performance of the machine. SCORG grid generator and ANSYS CFX solver were used for setting the model. Experimental observations related to inefficient heat dissipation on the intake of the rotors were analysed for its causes and potential design solutions. Air injection for cooling in the current configuration was evaluated and impact of design modifications was estimated using this CFD model. The developed model will be further used to redesign the high-pressure ports and air injection system to improve the thermal cooling effectiveness and specific power of the machine.

Comparing with medium-pressure chiller, low-pressure chiller usually has better system efficiency. At the same capacity, however, the radius of impellers in low-pressure compressor must be increased due to using higher specific volume refrigerants. In general, it means this kind of chillers have heavier structure and results in larger chiller room occupancy. Larger radius of impeller not only increases manufacturing cost but also enforces the axial bearing force during rated operation condition. Especially for active magnetic bearing (AMB) application, the load capacity of axial bearing force is one of the critical issues that leads to cost reduction or not. In this research, we developed an axial AMB using permanent magnets to provide the basic axial force of the thrust plate. This design allows the impeller can support larger axial load but is dangerous for output force reversed. Therefore, our design is particularly suitable for unidirectional axial force condition due to high pressure ratio applications. In order to improvement the controllability of AMB system, we designed a magnetic barrier zone in main flux path of permanent magnets to constrain its magnetic flux to make the flux of current coils and the flux of magnets be decoupled. We used static magnetic field analysis to deduce the analytical model of this axial AMB, and verified our ideas through hardware implementation. In our experiments, we demonstrated that the axial AMB can work normally with general PI controller, and the control current decreases linearly as the axial support weight increasing. This technology can reduce the axial bearing control current of the chiller. Especially for rated operating conditions, it can maintain a small current consumption and improve system performance.

This work presents a semi-supervised machine learning algorithm for identifying the end of the running-in process in reciprocating hermetic compressors. This identification is important for guaranteeing that the compressor reaches its tribological steady state before it is tested. Testing a not run-in compressor can result in erroneous measurements, particularly in energy performance tests. The most widely used procedure to avoid such problems is to operate the compressor under specific conditions for an empirically determined number of hours, but this method does not guarantee the end of the running-in process. The objective of this work is to present a fully automatic method for classification of the compressor state into running-in or tribological steady state. A test rig was built for imposing particular operating conditions for the compressor under test and for acquiring experimental data, such as pressures and temperatures at specific points of the refrigeration circuit, as well as the electric current. Four compressors which have never been turned on before were tested using the proposed rig to acquire data of the running-in phenomenon. The same compressors were tested twice more after operating for several hours to acquire data of compressors which are known to be in a tribological steady state. Processing based on a delayed space sliding window was used in the test time series to subsequence the root mean squared values of the electric current dataset. In addition, a semi-supervised machine learning method named self-training was used, considering k-nearest neighbours (KNN), random forest, and support vector machine methods as classifiers. The only two pieces of information assumed for the proposed method are that at the beginning of the first test of each compressor unit it was not run in yet, and that every other test the compressor was in its tribological steady state. The best classifier for identifying the end of the running-in process was obtained using the KNN method with less than 60 neighbours and a small number of features (4 or less). The results are consistent with comparative analyses and the running-in literature.