# Calculating the true savings when reducing air system pressure

## Over time many of the corrective actions put forward to reduce compressed air energy consumption have been simplified with the intention of encouraging action.

Energy conservation measures associated with compressed air have received a significant amount of attention over the years, mostly due to a reasonably short financial return compared with other energy consuming equipment. Over time many of the corrective actions put forward to reduce compressed air energy consumption have been simplified with the intention of encouraging action.

Although this is done with the best of intentions, sometimes simplifications and generalizations do not necessarily lead to positive results.

### Pressure reduction

The benefits of lowering system pressure can be attributed to two separate actions: reduced pressure at the compressor and a reduction in pressure delivered to production equipment. Each has value and can be implemented quite easily, but the savings associated must be calculated accurately before attempting to invoke an action that is normally not openly supported by production.

Reducing the pressure delivered to compressed air consuming equipment and processes will reduce the volume of air consumed by the system. The energy savings associated with network pressure results from a decrease in demand but will only be realized if the compressors reduce power as result of the demand change.

The portion of system compressed air demand that is associated with operating at a higher than necessary pressure is referred to as artificial demand. This is a relative value associated with the current system pressure and a target pressure. In the context of this article, the terms *demand, supply,* and* volume *are all referring to a volume with respect to time (flow).

If volume is discussed as a static value (hydraulic volume) it will be stated as such. Supply is flow from the compressors, and demand is the flow of air consumed by various production and process constituents.

### Calculating artificial demand

To calculate the artificial demand, divide the density of the air at the target pressure by the density of the air at the current pressure and then multiply by the current demand. This will represent the demand at the reduced pressure with the difference between current and proposed demand being the artificial demand.

Another way to calculate artificial demand is to divide the absolute pressures in place of density. Absolute pressure is the gauge pressure plus atmospheric pressure. However, if all calculations are being performed relative to standard conditions (scfm), then the atmospheric pressure at standard conditions must be used.

Currently, the values for standard conditions (scfm) are 14.5 psia, 68 F, 0% RH. Therefore, a 100 psig (gauge pressure) value would equal 114.5 psia (absolute). Using different conditions in compressed air calculations is a common error. As an example, one cannot measure volume flow using a mass flow meter calibrated in scfm yet perform storage calculations based on an atmospheric pressure of 13.9 psia.

If standard conditions are measured, it also extremely important for any compressors added to the system to be specified in scfm based on site conditions.

### Potential calculation errors

When calculating artificial demand, accurately determining the current pressure and what percentage of the demand is influenced by the change is critical. If a piece of production equipment is regulated at a pressure below the target pressure and reducing pressure in the system does not change the pressure on the consumer side of that regulator, compressed air consumption for that piece of production equipment will not change.

Inversely, if a reduction in system pressure causes the pressure on the consumer side of the regulator to drop, the volume consumed by that application will be reduced. The reduction in flow for that application will be based on the change in pressures after the regulator, not on the system pressure. Because artificial demand is a relative value, a 1 psi reduction in pressure will have a larger impact on volume in a 40 psi application than a 100 psi application.

Localized pressure is one of the reasons generalized calculations for artificial demand can be smaller or larger than the implemented results. Consequently, artificial demand calculations should take into consideration point-of-use (localized) pressure changes, not just the average system pressure change based on the largest measured demand.

If pressure changes occur at point-of-use applications, the artificial demand equation would need to be summed for all different conditions. More specifically, artificial demand would need to be calculated for each unique pressure application based on the localized pressure change and the localized volume. This can be very difficult to accomplish and measure, but is also the reason many people simply apply the general calculation for the entire demand and hope it works.

Another important issue is to accurately determine the current or initial pressure. A common error is to use the highest observed pressure at the compressor discharge as opposed to the average system pressure in the network. At the discharge of the compressor, the air pressure is the highest because there are no friction losses associated with moving the air through filters, dryers, and pipe. The appropriate pressure used in the calculation is based on where the air is being consumed.

Discharge of the compressor represents where the air is being supplied. Also, if average demand is utilized for the volume, then average pressure should be used for the calculation, not the highest pressure. Typically, demand changes with pressure for compressors with controls that utilize proportional logic where volume output is adjusted as a function of pressure.

Assuming a constant supply volume and a fixed number of compressed air consumers operating, if demand does not change as a function of system pressure, there is no artificial demand. Figure 1 is an example of how demand changes as a function of system pressure.

For this specific example, one compressor is operating using a load/no-load control where the compressor loads to full capacity at a lower pressure setpoint and then unloads at an upper setpoint at which time the compressor output goes to zero. When this data was recorded, plant demand was at a steady state. For the graph in Figure 1, pressure is on the vertical axis and time is on the horizontal axis. When the compressor is loaded, 100% of the compressor output is going into the system. A percentage of that air is being consumed by compressed air users, and the surplus air is held in the system as inventory.

This is analogous to a stack of boxes kept in inventory. As you put more boxes onto the pile as inventory, the stack of boxes gets higher. As you start to use up inventory, the stack becomes smaller as boxes are taken away. Pressure changes in a compressed air system in a similar fashion, where pressure rises as excess air is stored in the system, and then decreases as air is removed.

Whenever there is a difference between the rate of air entering the system and the rate of air leaving, the pressure will change. When the compressor reaches the unload setpoint (pressure), the compressor unloads and output goes to zero. At this time demand is greater than the supply and pressure falls as compressed air is removed from inventory. Looking at the graph, one can see the pressure rising and falling over time as the compressor loads and unloads.

### A view of artificial demand

The graph in Figure 1 has a blue and red line moving together but separated by 0.65 psi. This is because pressures on the graph were recorded in two different locations, illustrating the difference between the pressure where air enters the distribution network and the pressure at the farthest end of the facility. In this example the pressure loss across the system is only 0.65 psi. Had the pressure difference across the network been significantly larger, pressure and load would need to be calculated for regions of the network since assuming demand is evenly distributed along the pressure gradient could create significant error in the artificial demand calculation.

The volume of surplus compressed air influences how quickly the pressure rises when the compressor is loaded. As the volume of surplus compressed air decreases, the rate of pressure rise in the network decreases. Notice that the shape of this graph is not linear. Had it been linear (straight lines, not curved) that would indicate the system has no artificial demand. The reason for the curve is associated with an increase in demand as the pressure rises. As the pressure increases, the total demand from the system increases.

Since the amount of compressed air consumers has not changed, the difference is the artificial demand. To illustrate the difference, two lines are drawn tangent to the curve. Notice the change in slope on the curve as the pressure increases. For this system at this system pressure, all of the compressed air consumers were influenced by the same pressure. This can be confirmed mathematically because the difference in demand based on the two tangents to the curve equals the calculated artificial demand based on the total demand and the two pressure points. This in turn illustrates one easier way to determine artificial demand for a given system.

Note that if the unload pressure setpoint for this compressor were raised another 10 psi, the compressor would run fully loaded without unloading because the sum of artificial demand and production needs would be equal to the total supply from the compressor.

*Mark Krisa is a business manager at Ingersoll Rand, leading its compressed air audit program. A graduate with a degree in engineering science from the University of Western Ontario in Canada, Krisa has worked in the compressed air industry for over 20 years. His experience in the industry is diverse, ranging from compressor service technician through engineering and compressed air system auditor. He can be reached by e-mail at mark_krisa(at)irco.com.*

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