Compressed air makes up an important part of South African precious metal mining processes. Rising operational costs in the struggling mining sector increased the interest of the power utility, Eskom, and mine management in achievable electrical energy savings. Demand side management initiatives, funded by Eskom, realised a significant improvement in electrical energy efficiency of compressed air networks. Supply side interventions further aided optimisation by lowering operational costs. Previous research identified the need for integrating compressed air supply and demand side initiatives. Automated compressor control systems were needed in industry to realise missed opportunities due to human error on manual control systems. Automatic systems were found to be implemented in the industry, but missed savings opportunities were still encountered. This was due to the static nature of these control systems, requiring human intervention from skilled artisans. A comprehensive system is required that can adjust dynamically to the ever-changing demand and other system changes. Commercially available simulation software packages have been used by various mine groups to determine an optimal control philosophy. Satisfactory results were obtained, but the simulations were still based on static control inputs. No simulation system was found that could solve and optimise a system based on real-time instrumentation feedback. By combining simulation capabilities with dynamic control in real time, advanced optimisation could be achieved. Development was done on the theoretical design of the system, where mathematical calculations and the accuracy of the system were evaluated. This study proved that the new controller was viable and, as a result, the development of a fully dynamic control Automation of compressor networks through a dynamic control system iii system incorporating the verified mathematical models followed. All of this was done following a theoretical approach. Intricate control requirements on the supply side were evaluated to determine the impact of new intelligent compressor control strategies. It was found that improved compressor control realised an additional 6.2% electrical energy saving on top of existing savings initiatives. Practical limitations and human perception issues were also addressed. Financial cost-benefit analyses were used to evaluate the viability of using automated compressor control. Ample maintenance data obtained from two leading mining companies was used to evaluate the impact of increased stopping and starting of compressors. Financial cost savings from electrical energy efficiency control strategies were found to considerably outweigh the minimal increase in compressor maintenance. Savings potential on deep-level mines proved to be in the order of 5% of the baseline consumption. When these results are extrapolated to the remaining 22 South African deep-level gold and platinum mines already subjected to demand side management initiatives, potential savings of 12.67 MW can be realised. Based on the Eskom 2014/2015 Megaflex tariff structure, the financial cost saving from 12.67 MW is R61 million. / PhD (Electrical Engineering), North-West University, Potchefstroom Campus, 2015

Compressed air makes up an important part of South African precious metal mining processes. Rising operational costs in the struggling mining sector increased the interest of the power utility, Eskom, and mine management in achievable electrical energy savings. Demand side management initiatives, funded by Eskom, realised a significant improvement in electrical energy efficiency of compressed air networks. Supply side interventions further aided optimisation by lowering operational costs. Previous research identified the need for integrating compressed air supply and demand side initiatives. Automated compressor control systems were needed in industry to realise missed opportunities due to human error on manual control systems. Automatic systems were found to be implemented in the industry, but missed savings opportunities were still encountered. This was due to the static nature of these control systems, requiring human intervention from skilled artisans. A comprehensive system is required that can adjust dynamically to the ever-changing demand and other system changes. Commercially available simulation software packages have been used by various mine groups to determine an optimal control philosophy. Satisfactory results were obtained, but the simulations were still based on static control inputs. No simulation system was found that could solve and optimise a system based on real-time instrumentation feedback. By combining simulation capabilities with dynamic control in real time, advanced optimisation could be achieved. Development was done on the theoretical design of the system, where mathematical calculations and the accuracy of the system were evaluated. This study proved that the new controller was viable and, as a result, the development of a fully dynamic control Automation of compressor networks through a dynamic control system iii system incorporating the verified mathematical models followed. All of this was done following a theoretical approach. Intricate control requirements on the supply side were evaluated to determine the impact of new intelligent compressor control strategies. It was found that improved compressor control realised an additional 6.2% electrical energy saving on top of existing savings initiatives. Practical limitations and human perception issues were also addressed. Financial cost-benefit analyses were used to evaluate the viability of using automated compressor control. Ample maintenance data obtained from two leading mining companies was used to evaluate the impact of increased stopping and starting of compressors. Financial cost savings from electrical energy efficiency control strategies were found to considerably outweigh the minimal increase in compressor maintenance. Savings potential on deep-level mines proved to be in the order of 5% of the baseline consumption. When these results are extrapolated to the remaining 22 South African deep-level gold and platinum mines already subjected to demand side management initiatives, potential savings of 12.67 MW can be realised. Based on the Eskom 2014/2015 Megaflex tariff structure, the financial cost saving from 12.67 MW is R61 million. / PhD (Electrical Engineering), North-West University, Potchefstroom Campus, 2015

Various commercial software packages are available for simulating compressed air network operations. However, none of these software packages are able to dynamically prioritise compressor selection on large compressed air networks in the mining industry. In this dissertation, a dynamic compressor selector (DCS) will be developed that will actively and continuously monitor system demand. The software will ensure that the most suitable compressors, based on efficiency and position in the compressed air network, are always in operation. The study will be conducted at a platinum mine. Compressed air flow and pressure requirements will be maintained without compromising mine safety procedures. Significant energy savings will be realised. DCS will receive shaft pressure profiles from each of the shafts’ surface compressed air control valves. These parameters will be used to calculate and predict the compressed air demand. All pipe friction losses and leaks will be taken into account to determine the end-point pressure losses at different flow rates. DCS will then prioritise the compressors of the compressed air network based on the overall system requirement. This software combines the benefits of supply-side and demand-side management. Potential energy savings with DCS were proven and compressor cycling reduced. A DCS user-friendly interface was created to easily set up any mine’s compressed air network. / Thesis (MIng (Mechanical Engineering))--North-West University, Potchefstroom Campus, 2013

Various commercial software packages are available for simulating compressed air network operations. However, none of these software packages are able to dynamically prioritise compressor selection on large compressed air networks in the mining industry. In this dissertation, a dynamic compressor selector (DCS) will be developed that will actively and continuously monitor system demand. The software will ensure that the most suitable compressors, based on efficiency and position in the compressed air network, are always in operation. The study will be conducted at a platinum mine. Compressed air flow and pressure requirements will be maintained without compromising mine safety procedures. Significant energy savings will be realised. DCS will receive shaft pressure profiles from each of the shafts’ surface compressed air control valves. These parameters will be used to calculate and predict the compressed air demand. All pipe friction losses and leaks will be taken into account to determine the end-point pressure losses at different flow rates. DCS will then prioritise the compressors of the compressed air network based on the overall system requirement. This software combines the benefits of supply-side and demand-side management. Potential energy savings with DCS were proven and compressor cycling reduced. A DCS user-friendly interface was created to easily set up any mine’s compressed air network. / Thesis (MIng (Mechanical Engineering))--North-West University, Potchefstroom Campus, 2013