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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.

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.

This paper presents a general method for measuring plant-wide industrial energy savings and demonstrates the method using a case study from an actual industrial energy assessment. The method uses regression models to characterize baseline energy use. It takes into account changes in weather and production, and can use sub-metered data or whole plant utility billing data. In addition to calculating overall savings, the method is also able to disaggregate savings into components, which provides additional insight into the effectiveness of the individual savings measures. Although the method incorporates search techniques and multi-variable least-squares regression, it is easily implemented using data analysis software. The case study compared expected, unadjusted and weather-adjusted savings from six recommendations to reduce fuel use. The study demonstrates the importance of adjusting for weather variation between the pre- and post-retrofit periods.