Automated mine compressed air control for sustainable savings
Abstract
Compressed air generation consists of approximately 19% of a mine’s total electricity consumption. It was found that manually operated compressed air networks are controlled inefficiently. The need exists to reduce the electricity cost of a mining complex by optimising the control of the compressed air network.
This was achieved by integrating the demand and supply sides of compressed air networks. In order to accomplish this, the demand and supply of compressed air networks were characterised. Additional investigation identified electrical cost saving strategies implemented on the demand and supply sides. An existing compressor controller was identified that is capable to automatically control the supply of compressed air according to the requirements of compressed air demand.
The Dynamic Compressor Selector (DCS) controller was identified as a suitable compressed air control system. The DCS controller simulates a virtual compressed air network in order to calculate the pressure set point for compressors. The DCS controller then schedules the compressors in order to maintain the required network pressure. Flow loss, pressure drop and future flow and pressure profiles are considered to calculate the required network pressure and compressor schedules.
The DCS controller was implemented at a gold mining complex in South Africa. The DCS controller was able to simulate compressor discharge pressure set points and was able to schedule the most effective compressor combination based on actual and future demand requirements. However, when the simulation result was evaluated certain limitations and complications were encountered.
An improved control strategy was subsequently developed. Communication to different equipment and field instrumentation has already been established. Therefore, the improved control strategy uses the DCS controller as a backbone for the established communication links. The improved control strategy is able to calculate the pressure set points of compressors by considering auto compression, flow loss and pressure drops.
Certain conditions were identified in order to determine when a compressor should be started or stopped. To further optimise the compressed air network, the guide vane angles of each compressor was set to an optimal position when there are major disturbances in the system. This resulted in a more stable network pressure.
The improved control strategy was able to automatically control the supply of compressed air to accurately match the demand of compressed air. This resulted in an improvement in the energy efficiency of the compressed air network. With the implementation of the improved control strategy, an average evening peak clip in excess of 3 MW was realised for a period of three months. The improved control strategy should be rolled out to all major compressed air networks in the industry
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