A dynamic optimal control system for complex compressed air networks
Mines use large compressed air networks to supply shafts and processing plants with compressed air. These networks can be complex where multiple compressors are located at different locations. To add to the complexity of the network, each end user of compressed air is a separate business entity - each following its own schedule and usage requirements. Some mines have general guidelines controlling these schedules. Most mines still use static compressor control on compressed air networks. Advanced control strategies are based on simulation results of typical usage patterns. These static controls only work when all the end users use compressed air according to the data on which the control strategy was devised. If one end user deviates from this plan, the strategy becomes non-optimal. This happens almost on a daily basis. Previous work into dynamic control of compressed air networks was only based on basic networks where compressors were stationed close together. As soon as compressors are stationed further apart, there is a noticeable pressure drop. Due to this effect, the controller could select compressors too far away from the demand and the system would not provide a viable solution. The Dynamic Compressor Controller (DCC) discussed in this thesis solves this problem. The DCC accomplishes this by calculating multiple compressed air set points - one for each individual compressor. These set points take the location and demand of the compressed air network into account. The operating and trimming compressor are selected dynamically. In order to reduce cycling of compressors, the future airflow is predicted to ensure sufficient compressed air supply. The above-mentioned factors are combined to simulate the compressed air network state and propose an optimal solution for controlling the network. The solution prescribes optimal operating compressor schedules as well as pressure set points for all compressors. The prescribed pressure set point is the minimum supply pressure needed to supply the entire network with required air pressure. Due to this, the DCC will lower the running cost of the compressed air network and ensure a more stable compressed air supply by eliminating the oversupply of compressed air. The DCC was tested at two different mines - one mining platinum and the other mining gold. Both mines have large compressed air networks. However, the operating conditions and the requirements of the mines differed. If implemented, the DCC will be able to reduce the electricity consumption of the gold mine by up to 86 MWh per day. This can be extrapolated as a yearly reduction of R17 million in cost. The electricity consumption of the platinum mine could only be reduced by 0.5 MWh per day as it already had an optimised control schedule due to the previous implementation of a dynamic compressed air controller. This can be extrapolated as a yearly reduction of R650 000. In South Africa, mines consume 16% of the total electricity produced by Eskom, with gold and platinum mines accounting for 80% of that. The amount of electricity consumed by compressed air generation ranges from 25% in gold mines to 40% in platinum mines. This can be extrapolated to 6% of the total electricity usage of South Africa being consumed by compressed air generation. This can further be extrapolated to stating that the DCC has the potential to reduce the total electricity consumption of South Africa by up to 1.%.
Dynamic compressor control, Dynamic compressor system, DSM, Energy management, Mine compressor, Compressed air ring, Compressed air supply, Compressor control
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North-West University (South Africa) , Potchefstroom Campus