Quantifying Savings From Improved Boiler Operation

On/off operation and excess combustion air reduce boiler energy efficiency. This paper presents methods to quantify energy savings from switching to modulation control mode and reducing excess air in natural gas fired boilers. The methods include calculation of combustion temperature, calculation of the relationship between internal convection coefficient and gas flow rate, and calculation of overall heat transfer assuming a parallel-flow heat exchanger model. The method for estimating savings from changing from on/off to modulation control accounts for purge and drift losses through the boiler and the improved heat transfer within the boiler due to the reduced combustion gas flow rate. The method for estimating savings from reducing excess combustion air accounts for the increased combustion temperature, reduced internal convection coefficient and increased residence time of combustion gasses in the boiler. Measured boiler data are used to demonstrate the accuracy of the methods

Energy Efficient Process Heating: Managing Air Flow

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. The method for calculating savings from reducing forced ventilation in ovens accounts for flow rate of ventilation air and air temperature when entering and exhausting the oven. The method for calculating savings from reconfiguring oven openings accounts for flow rate of air entering and exiting the oven due to buoyancy forces

Improving Compressed Air Energy Efficiency in Automotive Plants: Practical Examples and Implementation

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. This paper focuses on practical methods that the authors have researched, analyzed and implemented to improve compressed air system efficiency in automobile manufacturing facilities. It describes typical compressed air systems in automotive stamping and assembly plants, and compares these systems to best practices. The paper then presents a series of examples, organized using the method of inside-out approach, which strategically identifies the energy savings in the compressed air system by first minimizing end-use demand, then minimizing distribution losses, and finally making improvements to primary energy conversion equipment, the air compressor plant

Energy Efficient Process Heating: Insulation and Thermal Mass

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

Optimizing Compressed Air Storage for Energy Efficiency

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

Simulating Energy Efficient Control of Multiple-Compressor Compressed Air Systems

In many industrial facilities it is common for more than one air compressor to be operating simultaneously to meet the compressed air demand. The individual compressor set-points and how these compressors interact and respond to the facility demand have a significant impact on the compressed air system total power consumption and efficiency. In the past, compressors were staged by cascading the pressure band of each compressor in the system. Modern automatic sequencers now allow more intelligent and efficient staging of air compressors. AirSim, a compressed air simulation tool, is now able to simulate multiple-compressor systems with pressure band and automatic sequencer controls. AirSim can simulate a current compressed air system and a proposed system with changes to the equipment and/or controls. Thus, quickly and accurately, users can calculate the energy and cost savings expected from many proposed compressed air system upgrades

Lean Energy Analysis: Identifying, Discovering and Tracking Energy Savings Potential

Energy in manufacturing facilities is used for direct production of goods, space conditioning, and general facility support such as lighting. This paper presents a methodology, called lean energy analysis, LEA, for graphically and statistically analyzing plant energy use in terms of these major end uses. The LEA methodology uses as few as 60 easily obtainable data points. Multivariable change-point models of electricity and natural gas use as functions of outdoor air temperature and production data are developed. The statistical models are used to subdivide plant energy use into facility, space-conditioning and production-related components. These breakdowns suggest the savings potential from reducing non-production and space-conditioning energy use. In addition, graphical analysis of the statistical models and data promotes the discovery of energy saving opportunities. Finally, the models can be used to predict energy use for energy budgeting, measure savings, determine cost structures, and for diagnostic purposes. Case study examples demonstrate the lean energy analysis method and its application

Measuring Plant-Wide Energy Savings

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. It also demonstrated the limitations of the engineering models when used to estimate savings

Industrial Assessment Center

The University of Dayton (UD) performed energy assessments, trained students and supported USDOE objectives. In particular, the UD Industrial Assessment Center (IAC) performed 96 industrial energy assessment days for mid-sized manufacturers. The average identified and implemented savings on each assessment were $261,080 per year and$54,790 per year. The assessments served as direct training in industrial energy efficiency for 16 UD IAC students. The assessments also served as a mechanism for the UD IAC to understand manufacturing energy use and improve upon the science of manufacturing energy efficiency. Specific research results were published in 16 conference proceedings and journals, disseminated in 22 additional invited lectures, and shared with the industrial energy community through the UD IAC website

Estimating Industrial Building Energy Savings using Inverse Simulation

Estimating energy savings from retrofitting existing building systems is traditionally a time intensive process, accomplished by developing a detailed building simulation model, running the model with actual weather data, calibrating the model to actual energy use data, modifying the model to include the proposed changes, then running the base and proposed models with typical weather data to estimate typical energy savings. This paper describes a less time-intensive method of estimating energy savings in industrial buildings using actual monthly energy consumption and weather data. The method begins by developing a multivariate three-parameter changepoint regression model of facility energy use. Next, the change in model parameters is estimated to reflect the proposed energy saving measure. Energy savings are then estimated as the difference between the base and proposed models driven with typical weather data. Use of this method eliminates the need for estimating building parameters, system performance, and operating practices since they are included in the inverse simulation model. It also eliminates the need for model calibration since the inverse model is derived from actual energy use data.The paper describes the development of statistical inverse energy signature models and how to modify the models to estimate savings. Expected savings from inverse simulation are compared to savings predicted by detailed hourly simulation, and sources of error are discussed. Finally, the method is demonstrated in a case study example from the industrial sector. Limitations of the approach for complex building systems and the uncertainty of estimated savings are discussed