Better Than a Crystal Ball

Computer modeling lets you forecast compressed air energy savings, reducing the risk of a poor decision

By Jeff Yarnall, P.E

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Strategies to control compressed air costs are becoming increasingly important. Recent articles in Chemical Processing make excellent points about controlling costs in the operation of systems essential to the production process.1 This article discusses several energy-saving options, as well as the application of computer modeling for determining the cost-effectiveness and assessing the performance of energy-efficiency measures.

Electrical energy once largely was regarded as an inexpensive input for a compressed air system. Questionable design practices could be tolerated as long as production was not affected negatively.

When concerns with the air system did arise, energy costs usually were not considered relevant. Typically, the quickest and least-expensive upfront solution was implemented. If production was happy with the short-term results, the air system once again could be ignored.

Fast forward to today's business environment. Electrical costs no longer can be ignored. Decisions involving capital improvements to an air system have far-reaching cost implications in terms of future energy consumption.

Plant operators no longer can afford the risk associated with a poor decision. Essential considerations for a system enhancement should include:

Achieving energy efficiency.

Conserving capital.

Generating reasonable cost savings today.

Maintaining peak performance, given the uncertainty of the future demands, while focusing on maximizing production output and delivering a quality product.

Foresee savings

Don't have a crystal ball to predict the savings associated with a proposed system change? You might want to consider computer modeling.

Computer modeling can forecast future savings with reasonable accuracy, reducing the risk of a poor decision. Until recently, compressed-air-system modeling primarily involved the use of compressor capacity and power curves to predict energy savings. For static or constant-demand profiles, these simplified curves can yield acceptable results, provided the user understands their assumptions and limitations.

Instead of making comparisons at any particular flow rate, time-dependent or deterministic models evaluate the potential energy savings at all flow rates and with a particular series of demands. It is important to show how the system responds to a series of changing flow demands, not just a particular flow rate. The more dynamic the system, the greater the need to consider time-dependent sequences. The following questions might need to be addressed in a dynamic air system:

Do high flow rates occur for long periods of time or are they just transient events? How do they affect the system?

Are high flow demands transient and short term or of extended duration?

Do differences in production runs cause compressed air flow patterns to change?

Do pressure drop problems require investigation?

Are constraints present that will prevent certain equipment from operating?

Computer modeling requires a series of tradeoffs among accuracy, complexity and desired results. Microsoft Excel or other commercially available spreadsheet software can be used to program a computer model that can be executed quickly.

The key questions in designing a time-dependent model are:

What characteristics or results are to be identified by the model?

What time interval is required to accurately illuminate these characteristics?

What and how much data are required?

What constraints related to data processing or data storage are present?

Each of the questions involves a decision about what time interval (Dt ) the model should use. Selection of a time interval depends on the individual system and how the results will be interpreted.

Systems that are relatively static can use longer duration intervals and provide enough detail for meaningful results. For instance, a hydroelectric reservoir system might need only daily stream inflow averages to determine storage and release levels. A heating, ventilation and air conditioning (HVAC) system might need only 15-minute temperature averages to determine when to switch from unoccupied to occupied status each morning.

These systems can be considered static in that their storage is very large (e.g., a hydroelectric reservoir) or the demands on the system (HVAC) change slowly. Compressed air systems lack either of these characteristics.

Consider a typical system with 2,000 gallons (gal.) of storage and a 1,000-cubic-foot-per-minute (cfm) compressor with an average system demand of 700 cfm. If the compressor experiences a fault and shuts down, the air pressure will drop 10 pounds per square inch gauge (psig) in approximately 16 seconds (sec.). Pressure and flow in an air system can change rapidly and, in many cases, data collection intervals greater than 15 sec. might be too long to provide results with enough clarity.

Modeling in action

A plant compressed air system that was audited for two weeks in October 2002 provides a good example for modeling purposes. Before "what if" scenarios can be modeled, a baseline or snapshot of the existing system is needed ," otherwise it will be difficult to determine actual savings. Alternative #1 is the "null" scenario and represents the existing pressure, power and flow profile of the system.

The system block diagram and demand profile for October 15, 2002 (9 a.m. to noon) are shown in Fig. 1 and Fig. 2, respectively. From the graph and interviews with plant personnel, several characteristics of the system become evident:

The plant operates two shifts. The day shift is from 6:30 a.m. to 2:30 p.m. The swing shift is from 3:30 p.m. to midnight.

Lunch and break times are indicated by sustained low flow.

The demand profile is very dynamic.

The system is pressurized 24/7 to maintain air pressure in the fire system.

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