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Strategies to Reduce Monthly Natural Gas Consumption in HVAC Systems

This study aims to achieve a 20% reduction in monthly natural gas consumption for HVAC systems between December and March. Key strategies include setting back temperatures and enhancing insulation. The research utilizes various analysis tools such as process maps, cause and effect matrices, and design of experiments to identify critical factors impacting energy consumption. Data analyses include heating degree days, heater on-time calculations, and heat loss assessments. Results indicate optimal temperature settings and corresponding costs for various days, providing a foundation for energy efficiency improvements.

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Strategies to Reduce Monthly Natural Gas Consumption in HVAC Systems

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  1. Reduce Monthly Natural Gas Consumption – HVAC Mohammad Shams, SeyedAlirezaTabatabaei, RoozbehHojatpanah, SiavashFarahmand, ShahriarAhmadiGhoohaki Department of Mechanical Engineering, IUPUI ME 414 Thermal-Fluid Systems Design Fall 2010, Professor John Toksoy

  2. Y Statement • Reduce monthly natural gas consumption by 20% for the months of Dec thru Mar • Set back temperature • Insulation improvements

  3. The Funneling Effect 30+ Inputs All X’s • Process Maps MEASURE 10 - 15 1st “Hit List” • C&E Matrix • Failure Modes and Effects Analysis • Multi-Vari Studies 8 - 10 ANALYZE Screened List IMPROVE • Design of Experiments (DOE) 4 - 8 Found Critical X’s CONTROL 3 - 6 • Control Plans Controlling Critical X’s Critical Input Variables

  4. Does the Setup Measure Energy Consumption Accurately • For each of the 24 hr data set • Calculate the heater on time • Calculate heating degree days • Plot HDD vs. heater on time • Does it show a linear behavior

  5. Analysis • Heater on Time vs HDD

  6. Analysis • Heater on Time vs HDD

  7. Analysis • Heater on Time vs HDD

  8. Analysis • Error Percent of Heater on Time vs HDD

  9. Therms Analysis

  10. Measurement Error • Error due to long signal wires • Filter design • Impact on mean value

  11. Initial Capability • Initial capability • What is the current energy usage as baseline where savings will be calculated from • Degree day comparison • Past 5 to 10 years gas and electric bills (kWhr used not $$$) • Calculate heat loss from the house using the excel analysis tool • Make the tool more general to include individual rooms • Run transient heat transfer analysis using the Matlab tool and compare to measured data

  12. Energy Cost Estimation

  13. Results • The cost for one day at these temperatures is: $0.33 • The optimal day time temperature is: 51 Degrees F • The total running time per day is: 97 Minutes Jan 22nd

  14. Results • The cost for one day at these temperatures is: $0.13 • The optimal day time temperature is: 59 Degrees F • The total running time per day is: 38 Minutes Jan 28th

  15. Results The cost for one day at these temperatures is: $0.18 The optimal day time temperature is: 47 Degrees F The total running time per day is: 53 Minutes Jan 29th

  16. Results Feb 3rd • The cost for one day at these temperatures is: $0.24 • The optimal day time temperature is: 48 Degrees F • The total running time per day is: 71 Minutes

  17. Results The cost for one day at these temperatures is: $0.28 The optimal day time temperature is: 50 Degrees F The total running time per day is: 83 Minutes Feb 4th

  18. Results The cost for one day at these temperatures is: $0.27 The optimal day time temperature is: 50 Degrees F The total running time per day is: 79 Minutes Feb 5th

  19. Results The cost for one day at these temperatures is: $0.22 The optimal day time temperature is: 49 Degrees F The total running time per day is: 64 Minutes Feb 6th

  20. Mohammad Shams, SeyedAlirezaTabatabaei, RoozbehHojatpanah, SiavashFarahmand, ShahriarAhmadiGhoohaki Heat Exchanger Design Department of Mechanical Engineering, IUPUI ME 414 Thermal-Fluid Systems Design Fall 2010, Professor John Toksoy

  21. Design Parameters • Process Fluid • Water • Inlet 45ºC • Outlet 25ºC • City Water • Inlet 20ºC • Optimal Length Less Than 7 meters

  22. Effective Variables • Due to previous iterations these nine were the variables that had the greatest effect on Weight, Length, Q, and ∆P’s

  23. Generated Matlab File

  24. Initial Inputs Generating

  25. Plots of Main Effect

  26. Pareto Charts for Optimization • Shell side pressure drop- Shell I/D had the greatest effect • Heat Exchanger overall weight- Shell I/D and Tube Length • Tube pressure drop- Mass flow rate through the tubes, Shell I/D and Tube Length • Shell side pressure drop- Shell I/D had the greatest effect • Heat Exchanger overall weight- Shell I/D and Tube Length • Tube pressure drop- Mass flow rate through the tubes, Shell I/D and Tube Length

  27. Pareto Charts for Optimization

  28. Optimization Plot

  29. Optimization Results

  30. Adjusted Optimized Results

  31. Questions ?

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