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Energy Simulation Using EnergyPlus™ for Building and Process Energy Balance

  • Seog-Chan Oh
  • Alfred J. Hildreth
Chapter
Part of the Springer Series in Advanced Manufacturing book series (SSAM)

Abstract

Recently, the importance of balancing building and process energy in manufacturing is growing because both comfortable working environment and energy efficiency have emerged as important element for advancing the manufacturing system. One way of achieving the balance is to optimize the operation of HVAC system (Heating, Ventilating and Air Conditioning System) in such a way that temperatures and states of heating and cooling are optimized. In this chapter, plant energy simulation models are developed by customizing EnergyPlus™ (below written as EnergyPlus) and two new HVAC control approaches such as air conditioning economizer and dynamic mist control are evaluated with the developed energy models. The simulation results reveal that (1) the use of air conditioning economizer can save 8.4 % yearly cooling energy compared to the business-as-usual case without compromising the working quality for a selected example location; (2) the application of dynamic mist control system can save significant cooling and heating energy for machining plants in three selected example locations, at the same time, keeping worker health protection foremost. This chapter also provides a short instruction to EnergyPlus. EnergyPlus was originally developed as a public domain software package to estimate energy consumptions of a building complex. Therefore, its applications are limited to commercial buildings, not industrial facilities. In order to use it for manufacturing facilities, its expansion is required. With an example of a room with welding equipment, the instruction provides step by step guidance toward understanding the details of manufacturing process simulation.

Keywords

Thermal Comfort Building Energy Ventilation Strategy Weather Scenario Insulation Board 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.TroyUSA
  2. 2.RochesterUSA

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