Search Results

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 cooling of equipment and products is an integral part of many manufacturing processes. This paper describes typical process cooling systems used in manufacturing and the approximate cost of cooling for each system. The paper then describes the inside out approach to energy efficiency, which recommends sequential evaluation of end use, distribution and primary energy conversion systems, as it relates to process cooling. General methods for improving the energy efficiency of cooling processes, organized according to the inside-out approach, are described. These methods include adding insulation and heat exchangers, improving process control, avoiding mixing, employing variable-speed and low friction pumping systems, and using cooling towers in place of chillers. The fundamental equations for estimating savings, and examples, are presented for these methods.

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.

Mechanical stamping presses and hydraulic systems consume a large portion of their rated power while idle. This paper discusses a method to control press and hydraulic system operations with the goal of reducing energy consumption during idle. First, key equations relating fraction of rated power to load are derived using measured data. Next, these relations are used to determine the “energy payback period”-the length of an idle period required to justify a system shutoff. It is shown that energy use is reduced when the machines are turned off if they will idle for a period of time longer than their energy payback period. This is demonstrated in two case study examples from industry.

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.

Much energy is lost through excess air flow in and out of process heating equipment. Energy saving opportunities from managing air flow include minimizing combustion air, preheating combustion air, minimizing ventilation air, and reconfiguring openings to reduce leakage. This paper identifies these opportunities and presents methods to quantify potential energy savings from implementing these energy-savings measures. Case study examples are used to demonstrate the methods and the potential energy savings. The method for calculating savings from minimizing combustion air accounts for improvement in efficiency from increased combustion temperature and decreased combustion gas mass flow rate. The method for calculating savings from preheating inlet combustion air consists of fundamental heat exchanger and combustion efficiency equations. This method accounts for the reduction of combustion air flow as fuel input declines, which is often neglected in many commonly-used methods.