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Compressed air storage is an important, but often misunderstood, component of compressed air systems. This paper discusses methods to properly size compressed air storage in load-unload systems to avoid short cycling and reduce system energy use. First, key equations relating storage, pressure, and compressed air flow are derived using fundamental thermodynamic relations. Next, these relations are used to calculate the relation between volume of storage and cycle time in load-unload compressors. It is shown that cycle time is minimized when compressed air demand is 50% of compressor capacity. The effect of pressure drop between compressor system and storage on cycle time is discussed. These relations are used to develop guidelines for compressed air storage that minimize energy consumption. These methods are demonstrated in two case study examples.

This paper describes a simple statistical method to statistically disaggregate industrial energy use into production-dependent, weather-dependent and independent components. This simple statistical disaggregation has many uses, including improving model calibration, quantifying non-productive energy use and identifying energy efficiency opportunities. The process is called Lean Energy Analysis (LEA) because of its relationship to Lean Manufacturing, which seeks to reduce non-productive activity. This paper describes the statistical models, discusses the application of the LEA approach to over 40 industrial facilities, and provides case study examples of the benefits.

Energy standard ISO 50001 will require industries to quantify improvement in energy intensity to qualify for certification. This paper describes a four-step method to analyze utility billing, weather, and production data to quantify a company's normalized energy intensity over time. The method uses 3-pararameter change-point regression modeling of utility billing data against weather and production data to derive energy signature equations. The energy signature equation is driven by typical weather and production data to calculate the ‘normal annual consumption’, NAC, and divided by typical production to calculate ‘normalized energy intensity” NEI. These steps are repeated on sequential sets of 12 months of data to generate a series of ‘sliding’ NEIs and regression coefficients. The method removes the effects of changing weather and production levels, so that the change in energy intensity is a sole function of changing energy efficiency.

The automotive industry is the largest industry in the United States in terms of the dollar value of production [1]. U.S. automakers face tremendous pressure from foreign competitors, which have an increasing manufacturing presence in this country. The Big Three North American Original Equipment Manufacturers (OEMs)-General Motors, Ford, and Chrysler-are reacting to declining sales figures and economic strain by working more efficiently and seeking out opportunities to reduce production costs without negatively affecting the production volume or the quality of the product. Successful, cost-effective investment and implementation of the energy efficiency technologies and practices meet the challenge of maintaining the output of high quality product with reduced production costs. Automotive stamping and assembly plants are typically large users of compressed air with annual compressed air utility bills in the range of $2M per year per plant.