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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.

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.

Open tanks and exterior surfaces of process heating equipment lose heat to the surroundings via convection, radiation, and/or evaporation. A practical way of reducing heat loss is by insulating or covering the surfaces. This paper presents methods to quantify heat loss and energy savings from insulating hot surfaces and open tanks. The methods include radiation and evaporation losses, which are ignored by simplified methods. In addition, thermal mass, such as refractory, conveyor and racking equipment, acts as a heat sink and increases heating energy use in process heating applications. This paper presents lumped capacitance and finite-difference methods for estimating heat loss to thermal mass, and savings from reducing this loss. The methods described above have been incorporated in free software, and are demonstrated using case study examples. The examples demonstrate the magnitude of the potential error from using simplified methods.