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Cooling Tower Monitoring and Optimization Boker Wright, Dan Lewis, Tina Foley and Brandon Wissel

Boker Wright, Dan Lewis, Tina Foley and Brandon Wissel

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Page 1: Boker Wright, Dan Lewis, Tina Foley and Brandon Wissel

Cooling Tower Monitoring and Optimization

Boker Wright, Dan Lewis, Tina Foley and Brandon Wissel

Page 2: Boker Wright, Dan Lewis, Tina Foley and Brandon Wissel

Problem

Problem Specification

- Develop a method to monitor the real time efficiency of a set of cooling towers

- Provide recommendations to improve the overall efficiency of the set using the real time analysis.

Design Constraints• Non-intrusive

– No changes to the structure– No interference

• Durable – weather conditions – resist damage bearing

multiple take-downs

• Portable– The system fits in 1 or 2

suitcases– 4 to 8 hour set up

Page 3: Boker Wright, Dan Lewis, Tina Foley and Brandon Wissel

Cooling Tower Basics• Passes water and air over “fill”

surface area

• Cools through evaporation

• Cools fluid to near wet bulb temperature

• Why use cooling towers?• Human comfort• Industrial process• Power generation

• Resources• Water• Electricity• Chemical

Page 4: Boker Wright, Dan Lewis, Tina Foley and Brandon Wissel

Our Approach• Electricity usage vs. Heat rejected

• Conforms to industry standard ASME PTC and CTI codes

• Instrumentation/placement

• Wireless technology utilized

• Recommendations based on most efficient cells

Page 5: Boker Wright, Dan Lewis, Tina Foley and Brandon Wissel

Instrumentation and Measurements Required

Measurement required

Ambient Air Temperatures

Dry bulb Wet bulb

Cold Water Flow

Water temperatures

Hot Water Cold Water

Fan Power

Ambient Relative humidity at inlet (weather station) and RH at exhaust

Atmospheric pressure

Monitoring System

100Ω Platinum

RTD

Magnetic Mount

RTD

Current Transduce

r

Wireless DAQ

System

Weather Station

Ultrasonic Flow-meter

Page 6: Boker Wright, Dan Lewis, Tina Foley and Brandon Wissel

LabVIEW• Monitors and displays real time

data

• Analyzes stored data

• Displays aggregate data for trend identification

• Modular programing with sub-VI’s for ease of modification

Page 7: Boker Wright, Dan Lewis, Tina Foley and Brandon Wissel

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