This case study details AngloGold Ashanti's use of Flownex® to optimize their compressed air system, achieving energy savings of 1 to 2.5 MW and annual savings of 24 GWh. The simulation adjusted compressor vane control speeds to improve system efficiency, preventing pressure drops and reducing unnecessary compressor starts, leading to significant cost and energy savings. Jean Greyling received an eta energy efficiency award for this project.
CHALLENGE
The challenge is to optimize components in a system in order to improve the overall efficiency of the system. This specific system is an air-compressor system of which the compressor vane control speed will be studied.
BENEFITS
The complete system can be modeled with Flownex.
Knowledge of how the system will react under different conditions can be obtained.
New philosophies can be tested.
Flownex can be used to test system upgrades to improve the efficiency of the system which may lead to significant cost and energy savings.
SOLUTION
In Flownex, the speed of the vane control and different vane positions of the compressor were simulated in order to investigate the pressure profile to optimize the efficiency of the system.
|
INTRODUCTION
This case study reviews an AngloGold Ashanti project which facilitated a saving of 2.5 MW by using Flownex to optimize compressor and piping systems. The project also won Jean Greyling of AngloGold Ashanti an eta (energy efficiency) award.
SYSTEM DESCRIPTION
Flownex was used to simulate the AngloGold Ashanti Vaal River compressed air network that consisted of several compressors connected with piping infrastructure over a distance of 32 km. The capacity of some of the compressors ranged from 30 000 to 100 000 CFM and the general operating function of these compressors was to accommodate the underground base loads that ranged between 75 and 106 kg/s.
When the consumption exceeded the supply, the ring pressure would be constant up to a point where the reserve was consumed from the reservoir. This resulted in the system pressure dropping, followed by the compressor master controller sending a command for the Moore controller to open the guide vanes. Although the opening of the guide vanes was not a problem during off-peak conditions, it posed a problem in terms of system response during high-peak times. Therefore an optimization was required.
If the surface pressure fell below a certain point, the mass was increased in the shaft columns to build up pressure at the drill points. The shaft columns only had capacity for a certain flow rate and if this flow rate was exceeded, the effect of auto compression would be lost. The reservoir acted as a damper between supply and consumption and the system pressure could be lost if vanes were not opened fast enough (due to the Moore controller responding too slow). This might have led to the starting of additional compressors which would in turn increase the network's energy consumption. In order to address the problem, the speed of vane control could be increased to minimize the pressure lost in the system.
OBJECTIVE OF SIMULATION
The objective of the simulation was to simulate different vane positions to assess the pressure profile and the response of the compressors and the ring pressure.
FLOWNEX MODEL
The Flownex model of the system can be seen in Figure 1.
DESCRIPTION OF SIMULATION
Pipes and compressors are the main elements that were used to simulate the pressure changes and the performance of the compressors within the ring. An iterative solution was done for the proper optimization of vane control speed by using the built-in optimization tool in Flownex. During the optimization phase, the energy input is minimized to produce the largest amount of air (kg/kWh – optimization).
RESULTS
Flownex allowed engineers to compare energy usage of existing and proposed system configurations. They then expanded the study by simulating multiple operating ranges for more than one configuration. The results showed savings ranging from 1 – 2.5 MW facilitating an annual saving of 24 GWh.
CONCLUSION
The optimization of a compressed air network was demonstrated in this example. By using the built-in optimization tool, an iterative process was used to determine the savings achievable from running different vane positions for the kWh optimization.