Mines in general, make use of compressed air systems for daily operational activities. Compressed air on mines is traditionally distributed in two typical fashions. Firstly, direct pipe feed systems for single shafts or compressed air ring networks where multiple shafts are supplied with compressed air from an integral system. These compressed air networks make use of number compressors feeding the ring from various locations in the network. While mines have sophisticated control systems to control these compressors they are not dynamic. Compressors are selected on static priorities for a chosen time period of the day. While this is acceptable for some days it is not always the ideal solution. The compressed air demand of the ring is dynamic and it is difficult to estimate the future need of the system. The Dynamic Compressor Selector (DCS) is described as a solution to this problem. DCS is a computer based control system featuring a Graphical User Interface (GUI). The aim of DCS is to dynamically calculate a control pressure set-point, given the demand for compressed air as well as choose the optimal compressors to supply the given compressed air. This will reduce the power requirement of the compressed air ring as well as reduce compressor cycling. DCS was implemented and tested on a single mine compressed air system. Achieved results were 1.8 MW in electricity savings as well as the added benefit of reduced cycling. This saving results in a cost saving of R3.7 million per annum. The problems and shortfalls of the system are also discussed as well as possible future directions for moving forward. / MIng (Computer and Electronic Engineering), North-West University, Potchefstroom Campus, 2014

Mines in general, make use of compressed air systems for daily operational activities. Compressed air on mines is traditionally distributed in two typical fashions. Firstly, direct pipe feed systems for single shafts or compressed air ring networks where multiple shafts are supplied with compressed air from an integral system. These compressed air networks make use of number compressors feeding the ring from various locations in the network. While mines have sophisticated control systems to control these compressors they are not dynamic. Compressors are selected on static priorities for a chosen time period of the day. While this is acceptable for some days it is not always the ideal solution. The compressed air demand of the ring is dynamic and it is difficult to estimate the future need of the system. The Dynamic Compressor Selector (DCS) is described as a solution to this problem. DCS is a computer based control system featuring a Graphical User Interface (GUI). The aim of DCS is to dynamically calculate a control pressure set-point, given the demand for compressed air as well as choose the optimal compressors to supply the given compressed air. This will reduce the power requirement of the compressed air ring as well as reduce compressor cycling. DCS was implemented and tested on a single mine compressed air system. Achieved results were 1.8 MW in electricity savings as well as the added benefit of reduced cycling. This saving results in a cost saving of R3.7 million per annum. The problems and shortfalls of the system are also discussed as well as possible future directions for moving forward. / MIng (Computer and Electronic Engineering), North-West University, Potchefstroom Campus, 2014

Indiana University-Purdue University Indianapolis (IUPUI) / The compressed air system is widely used throughout the industry. Air compressors are one of the most costly systems to operate in industrial plants in terms of energy consumption. Therefore, it becomes one of the primary targets when it comes to electrical energy and load management practices. Load forecasting is the first step in developing energy management systems both on the supply and user side. A comprehensive literature review has been conducted, and there was a need to study if predicting compressed air system’s load is a possibility. System’s load profile will be valuable to the industry practitioners as well as related software providers in developing better practice and tools for load management and look-ahead scheduling programs. Feed forward neural networks (FFNN) and long short-term memory (LSTM) techniques have been used to perform 15 minutes ahead prediction. Three cases of different sizes and control methods have been studied. The results proved the possibility of the forecast. In this study two control methods have been developed by using the prediction. The first control method is designed for variable speed driven air compressors. The goal was to decrease the maximum electrical load for the air compressor by using the system's full operational capabilities and the air receiver tank. This goal has been achieved by optimizing the system operation and developing a practical control method. The results can be used to decrease the maximum electrical load consumed by the system as well as assuring the sufficient air for the users during the peak compressed air demand by users. This method can also prevent backup or secondary systems from running during the peak compressed air demand which can result in more energy and demand savings. Load management plays a pivotal role and developing maximum load reduction methods by users can result in more sustainability as well as the cost reduction for developing sustainable energy production sources. The last part of this research is concentrated on reducing the energy consumed by load/unload controlled air compressors. Two novel control methods have been introduced. One method uses the prediction as input, and the other one doesn't require prediction. Both of them resulted in energy consumption reduction by increasing the off period with the same compressed air output or in other words without sacrificing the required compressed air needed for production. / 2019-12-05

<div>The compressed air system is widely used throughout the industry. Air compressors are one of the most costly systems to operate in industrial plants in therms of energy consumption. Therefore, it becomes one of the primary target when it comes to electrical energy and load management practices. Load forecasting is the first step in developing energy management systems both on the supply and user side. A comprehensive literature review has been conducted, and there was a need to study if predicting compressed air system’s load is a possibility. </div><div><br></div><div>System’s load profile will be valuable to the industry practitioners as well as related software providers in developing better practice and tools for load management and look-ahead scheduling programs. Feed forward neural networks (FFNN) and long short-term memory (LSTM) techniques have been used to perform 15 minutes ahead prediction. Three cases of different sizes and control methods have been studied. The results proved the possibility of the forecast. In this study two control methods have been developed by using the prediction. The first control method is designed for variable speed driven air compressors. The goal was to decrease the maximum electrical load for the air compressor by using the system's full operational capabilities and the air receiver tank. This goal has been achieved by optimizing the system operation and developing a practical control method. The results can be used to decrease the maximum electrical load consumed by the system as well as assuring the sufficient air for the users during the peak compressed air demand by users. This method can also prevent backup or secondary systems from running during the peak compressed air demand which can result in more energy and demand savings. Load management plays a pivotal role and developing maximum load reduction methods by users can result in more sustainability as well as the cost reduction for developing sustainable energy production sources. The last part of this research is concentrated on reducing the energy consumed by load/unload controlled air compressors. Two novel control methods have been introduced. One method uses the prediction as input, and the other one doesn't require prediction. Both of them resulted in energy consumption reduction by increasing the off period with the same compressed air output or in other words without sacrificing the required compressed air needed for production.</div><div><br></div>

Eskom is under increasing pressure to provide reliable and sustainable electricity. Demand Side Management (DSM), offers a short– to medium–term solution to this problem. During 2009, the mining sector consumed approximately 16% of the domestic electricity supplied by Eskom. This made the mining sector one of the major targets for Eskom–initiated DSM programmes. The mining industry uses compressed air for a wide variety of applications and production purposes. This creates many opportunities to reduce electricity consumption and operating costs. Reducing the airsystem demand may however not result in significant electrical energy savings, unless the compressed–air supply is accurately managed to meet the reduced demand. Until recently, compressor control in the mining sector generally consisted of operating the compressors continuously, regardless of the actual demand for compressed air. Excessive compressed air is blown off into the atmosphere resulting in energy loss. This usually occurs when the compressors are operated manually. A computer–controlled compressor management solution, which optimises the efficiency potential of the compressed–air supply, is required to obtain significant electrical energy savings. The need for such a solution was addressed by the development of an energy management solution for mine compressor systems. This solution is referred to as Energy Management System (EMS) and is capable of starting, stopping, loading and unloading compressors. In addition to this, compressor output can be controlled to maintain a desired pressure set–point. In this study, the development and implementation of EMS on ten different mine compressor systems is presented. Automatic compressor capacity control was implemented, while an operator manually initiated compressor starting; stopping; loading and unloading, according to EMS control schedules. Centralised compressor control is one of the main advantages offered by EMS, especially for compressed–air systems with multiple compressor systems at different geographic locations. EMS facilitated effective and sustainable electrical energy reductions for all these compressed–air systems. / Thesis (M. Ing. (Computer and Electronical Engineering))--North-West University, Potchefstroom Campus, 2011.

Eskom is under increasing pressure to provide reliable and sustainable electricity. Demand Side Management (DSM), offers a short– to medium–term solution to this problem. During 2009, the mining sector consumed approximately 16% of the domestic electricity supplied by Eskom. This made the mining sector one of the major targets for Eskom–initiated DSM programmes. The mining industry uses compressed air for a wide variety of applications and production purposes. This creates many opportunities to reduce electricity consumption and operating costs. Reducing the airsystem demand may however not result in significant electrical energy savings, unless the compressed–air supply is accurately managed to meet the reduced demand. Until recently, compressor control in the mining sector generally consisted of operating the compressors continuously, regardless of the actual demand for compressed air. Excessive compressed air is blown off into the atmosphere resulting in energy loss. This usually occurs when the compressors are operated manually. A computer–controlled compressor management solution, which optimises the efficiency potential of the compressed–air supply, is required to obtain significant electrical energy savings. The need for such a solution was addressed by the development of an energy management solution for mine compressor systems. This solution is referred to as Energy Management System (EMS) and is capable of starting, stopping, loading and unloading compressors. In addition to this, compressor output can be controlled to maintain a desired pressure set–point. In this study, the development and implementation of EMS on ten different mine compressor systems is presented. Automatic compressor capacity control was implemented, while an operator manually initiated compressor starting; stopping; loading and unloading, according to EMS control schedules. Centralised compressor control is one of the main advantages offered by EMS, especially for compressed–air systems with multiple compressor systems at different geographic locations. EMS facilitated effective and sustainable electrical energy reductions for all these compressed–air systems. / Thesis (M. Ing. (Computer and Electronical Engineering))--North-West University, Potchefstroom Campus, 2011.