Publication Date

2015-09-04

Availability

Embargoed

Embargo Period

2017-09-03

Degree Type

Dissertation

Degree Name

Doctor of Philosophy (PHD)

Department

Industrial Engineering (Engineering)

Date of Defense

2015-08-28

First Committee Member

Shihab Asfour

Second Committee Member

Vincent Omachonu

Third Committee Member

Murat Erkoc

Fourth Committee Member

Moataz Eltoukhy

Abstract

Compressed air is the most expensive source of energy in manufacturing facilities and is responsible for over 10% of industrial energy usage in the U.S. Air compressors are only 11-13% efficient, making correct operation, sizing and location critical to energy conservation and optimization. Correct location and sizing of air compressors is a complex problem that requires consideration of multiple variables and uncertainties. The current method of locating and sizing air compressors is non-scientific and is largely based on unverified assumptions, availability of space and equipment, and convenience of location. This leads to significant increases in energy cost to manufactures. To resolve this, the authors propose a mathematical framework to identify the optimal air compressor location in a dynamic manufacturing facility. The introduced framework will minimize air leaks, pressure drops, and kW demand of the compressor, as well as correctly size the air compressor at each zone by determining the machine load profile and matching air supply and demand in the facility. The model will also consider location inconvenience and compressor noise in its decision process by allowing users to rate their preference level at each zone. Ultimately the goal is to propose a novel framework that finds the optimal air compressor location and size in a manufacturing facility while considering the user’s preference for location and actual air losses in the system. The proposed model will also take into account air demand, air leaks, and air pressure set point variations.

Keywords

Compressed-Air System; Energy Efficiency; Location Optimization; Air Leakage Reduction; Pressure Drop Reduction; Capacity Optimization; Sensitivity Analysis; Simulation-optimization; Multi-Objective

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