Engineering Refrigerants-Presentation

Engineering Refrigerants-Presentation - INCORPORATING...

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Unformatted text preview: INCORPORATING INCORPORATING PRODUCT DESIGN INTO THE THE DEVELOPMENT OF NEW REFRIGERANTS REFRIGERANTS By: Isaac Anderson and Christopher DiGiulio [1] Vapor-Compression Refrigeration. Answers.com. 4/27/2007 <http://www.answers.com/topic/vapor-compression-refrigeration>. PRESENTATION OUTLINE PRESENTATION 1. Background 2. The Use of Consumer Preference Functions The in Refrigerant Design in 3. Design of New Refrigerants Based on Design Consumer Preference Functions and the Chosen Market Chosen A. Discussion of Group Contribution Theory B. Enumeration Modeling in Excel using Group Enumeration Contribution Theory Contribution 4. Conclusions 5. Recommendations BACKGROUND BACKGROUND HISTORY OF REFRIGERATION HISTORY • Refrigeration goes back Refrigeration to ancient times to – Stored Ice Stored – Evaporative Evaporative Processes Processes • In 1805, Oliver Evans In proposed the use of a volatile fluid in a closed cycle to freeze water into ice ice HISTORY HISTORY OF REFRIGERATION REFRIGERATION • Evan’s theories most likely theories influenced Jacob Perkins and Richard Trevithick and • They proposed an air-cycle cycle system in 1828, but it wasn’t system built either built • Actual refrigerants were Actual introduced in the 1830s with the invention of the vapor compression system by Perkins by [2] Calm, James M. and David A. Didion. "Trade-Offs in Refrigerant Selections: Past, Present and Future." Int. J. Refrig., 21, 308 (1998). HISTORY HISTORY OF REFRIGERATION REFRIGERATION • In 1928, Midgely, Henne, and McNary of GM In pioneered work to obtain molecules with desirable properties using systematic design desirable – They synthesized all 15 combinations of They one carbon with various combinations of chlorine, fluorine, and hydrogen. – They finally chose They dichlorodifluoromethane (Freon) as having the most desirable characteristics, thus introducing the first chlorofluorocarbons REFRIGERANTS FROM 1830REFRIGERANTS PRESENT • First Generation – Generally solvents, fuels Generally or volatile components (whatever worked) (whatever • Second Generation – CFCs were introduced • Third Generation – Shift to HCFCs • Fourth Generation – Focused on refrigerants Focused that do no contribute to global warming global [3] Calm, James M. and Glenn C. Hourahan. Refrigerant Data Update. January 2007. Heating/Piping/Air Conditioning Engineering. Feb. 7, 2007 <http://www.hpac.com/Issue/Article/44475/Refrigerant Data Update>. REFRIGERANT PHASE-OUT REFRIGERANT • 1987 – Montreal Protocol established Montreal requirements that began the world wide phase-out of CFCs phase • 1992 – Montreal Protocol established phase-out out for HCFCs for PHASE-OUT SCHEDULE FOR PHASE OUT HCFCS HCFCS • 2003 2003 – The amount of all HCFCs that can be produced The nationwide must be reduced by 35.0% • 2010 – The amount of all HCFCs that can be produced The nationwide must be reduced by 65.0% • 2015 – The amount of all HCFCs that can be produced The nationwide must be reduced by 90.0% nationwide • 2020 – The amount of all HCFCs that can be produced The nationwide must be reduced by 99.5% nationwide • 2030 – No HCFCs can be produced PHASE-OUT SCHEDULE FOR PHASE OUT HCFCS HCFCS THE THE USE OF CONSUMER PREFERENCE PREFERENCE FUNCTIONS IN REFRIGERANT DESIGN REFRIGERANT CONSUMER CONSUMER PREFERENCE FUNCTIONS AND DEMAND FUNCTIONS • In the design of the potential refrigerants, consumer preference functions were used to evaluate refrigerant properties functions • Consumer preference functions can also be used to solve for the demand Consumer (d1) of a new refrigerant when it is in competition with an existing (d refrigerant refrigerant • In the following equation, β is the only variable dependent on consumer In is preference functions preference α φ (d1 ) = p1d1 − β Where: Where: ρ Y − p1 d1 p2 p2 1− ρ d1ρ = 0 Y is the market potential is P is the price D is the demand ρ was set to a constant value of 0.76 is the consumer awareness of our product β relative consumer preference The subscript 1 refers to the new product The subscript 2 refers to the existing comparison product DEVELOPMENT DEVELOPMENT OF α Alpha is the consumer Alpha awareness of the new refrigerant as a function of time. of Alpha vs. Time 1 0.9 0.8 0.7 0.6 Alpha • 0.5 0.4 0.3 0.2 0.1 0 • The plots on the right The show the values of alpha that were used when calculating the demand calculating 0 1 2 3 4 5 6 7 8 9 10 6 7 8 9 10 Time (Years) Alpha vs. Time 1 0.9 0.8 0.7 • The bottom figure The illustrates the effect of increases advertising increases Alpha 0.6 0.5 0.4 0.3 0.2 0.1 0 0 1 2 3 4 5 Time (Years) DEVELOPMENT DEVELOPMENT OF β • The equation used to derive β is as follows The β= H 2 (competitio n preference ) H1 • Where Hi is the respective consumer is preference of each product preference H i = ∑ ω i yi Where : ωi is the weight of refrigerant property Where is yi is the property score of each refrigerant property is CONSUMER CONSUMER PREFERENCE FUNCTIONS FUNCTIONS • Consumer preference functions Consumer predict consumer reactions to different refrigerant design properties properties • The consumer preference The functions were estimated using expected consumer reactions to refrigerant properties WHAT WHAT MAKES A GOOD REFRIGERANT? REFRIGERANT? • Safe: non-toxic, nonflammable, and toxic, nonexplosive nonexplosive • Environmentally friendly: low ODP, low GWP • Compatible with existing refrigeration Compatible materials materials • Desirable thermodynamic characteristics: high Desirable latent heat, low compression ratio, low specific heat of liquid heat • Stable at operating temperatures CONSUMER CONSUMER PREFERENCE FUNCTIONS FUNCTIONS • Based on 6 characteristics – – – – – – Flammability Explosiveness Toxicity Global Warming Potential Ozone Depletion Potential Coefficient of Performance • The outlook for discovery or synthesis of ideal The refrigerants is extremely unlikely. Trade-offs refrigerants offs among desired objectives are necessary to achieve balanced solutions4 achieve [4] Calm, James M. and David A. Didion. "Trade-Offs in Refrigerant Selections: Past, Present and Future." Int. J. Refrig., 21, 308 (1998). SURVEY 1 SURVEY Expected consumer response 7 8 8 9 10 10 ESTIMATING ESTIMATING ωi 20% 18% 16% 14% 12% 10% 8% ` 6% 4% 2% Ozone Depletion Potential Global Warm ing Potential Explosion Potential Toxicity Flam m ability 0% Coefficient of P erform ance • It can be seen that efficiency is the most is important property to consumers to Weighted Consumer Preferences w i % (where Σ w i =100% ) • This figure represents This the expected weights of the refrigerant properties properties SURVEY 2 • Each of the properties from Survey 1 are examined Each individually individually • This Survey provides a correlation between consumer This satisfaction and design properties • Example – “Donut Design” Example P r o p e r t y S c o r e v s. C o nc . S u g a r Property Property Score vs. vs. Sweetness Consumer Satisfaction Sw eetness 1.00 1.00 Property Score 0.90 0.80 0.60 0.40 0.20 0.80 0.70 0.60 0.50 0.40 0.30 0.20 0.10 0.00 0.00 0 Not Sweet Semi Sweet Sweet Very Sweet Inedible Sweetness 0.4 0.8 Conc. of Sugar (g/cm 3) 1.2 1.6 SURVEY SURVEY 2 – EFFICIENCY Property Score vs. Efficiency Consumer Satisfaction vs. Efficiency Property Score vs. ∆ H ve /C p 0.90 0.80 0.80 0.70 0.70 Property Score 1.00 0.90 Property Score 1.00 0.60 0.50 0.40 0.30 0.60 0.50 0.40 0.30 0.20 0.20 0.10 0.10 0.00 0.00 0 Not Efficient Marginally Efficient Efficient Highly Efficient Extremely Efficient 0.5 1 1.5 ∆Hve/Cp 2 2.5 3 SURVEY SURVEY 2 – FLAMMABILITY Property Score vs. Flash Point Property Score vs. vs. Flammability Consumer SatisfactionFlammability 1.00 0.90 0.90 0.80 0.80 Property Score 1.00 Property Score 0.70 0.60 0.50 0.40 0.30 0.70 0.60 0.50 0.40 0.30 0.20 0.20 0.10 0.10 0.00 0 0.00 Extremely Flammable Highly Flammable Flammable Slightly Flammable Non Flammable 50 100 150 Flash Point (F) 200 250 SURVEY SURVEY 2 – TOXICITY Property Score vs. Toxicity Consumer Satisfaction vs. Toxicity Property Score vs. LD50 Conc. 0.90 0.80 0.80 0.70 0.70 Property Score 1.00 0.90 Property Score 1.00 0.60 0.50 0.40 0.30 0.60 0.50 0.40 0.30 0.20 0.20 0.10 0.10 0.00 0.00 Extremely Toxic Highly Toxic Moderately Toxic Slightly Toxic Very Slightly Toxic Non Toxic 0 500 1000 1500 LD50 (mg/kg) 2000 2500 3000 SURVEY EXPLOSION SURVEY 2 – EXPLOSION POTENTIAL POTENTIAL Property Score vs. LEL Property Score vs. Explosion Potential Consumer Satisfaction vs. Explosion Potential 0.90 0.80 0.80 0.70 0.70 Property Score 1.00 0.90 Property Score 1.00 0.60 0.50 0.40 0.30 0.60 0.50 0.40 0.30 0.20 0.20 0.10 0.10 0.00 0.00 Extremely Risk High Risk Moderate Risk Low Risk No Risk 0 2 4 6 8 LEL (%) 10 12 14 16 SURVEY GLOBAL SURVEY 2 – GLOBAL WARMING POTENTIAL POTENTIAL Property Score vs. GWP Property Score vs.Satisfaction vs. GWP Consumer Global Warming Potential 0.90 0.80 0.80 0.70 0.70 Property Score 1.00 0.90 Property Score 1.00 0.60 0.50 0.40 0.30 0.60 0.50 0.40 0.30 0.20 0.20 0.10 0.10 0.00 0.00 0 No GWP Very Slight GWP Slight GWP Moderate GWP High GWP Extremely High GWP 1000 2000 3000 GWP (Rel. CO2) 4000 5000 6000 SURVEY GLOBAL SURVEY 2 – GLOBAL WARMING POTENTIAL POTENTIAL SURVEY OZONE SURVEY 2 – OZONE DEPLETION POTENTIAL POTENTIAL Property Score vs. ODP Property Score vs. Ozone Depletion Potential Consumer Satisfaction vs. ODP 0.90 0.80 0.80 0.70 0.70 Property Score 1.00 0.90 Property Score 1.00 0.60 0.50 0.40 0.60 0.50 0.40 0.30 0.30 0.20 0.20 0.10 0.10 0.00 0.00 No ODP Very Slight ODP Slight ODP Moderate ODP High ODP Extremely High ODP 0 2 4 6 8 10 ODP (Rel. to CCl 3F) 12 14 16 SOLVING SOLVING FOR THE MARKET POTENTIAL (Y) POTENTIAL • Before the market potential can be solved for, a Before market must be defined market • Possible Markets for New Refrigerants Possible include: include: – Air conditioning for homes and commercial Air buildings buildings – Air conditioning of personal cars, trucks and Air sport utility vehicles sport – Refrigerated transportation (food, animals, Refrigerated beer, medical supplies etc.) beer, – Refrigerators and freezers – Industrial operations CENTRAL CENTRAL AIR/HOUSEHOLD UNITS UNITS • In 2006, the number In of total housing units in the US was 124.4 Million Units Million • The number of air The conditioners sold in the US is estimated to reach 10.7 million units in 2008 units 5US Air Conditioners 2004. AUTOMOBILES SOLD IN 2005 AUTOMOBILES • The automotive The industry offers higher numbers of units sold per year units Number of cars purchased in 2005 (by country) 18.0 16.0 U n i ts i n M i l l i o n s 14.0 12.0 10.0 • Advantage of the Advantage automotive market automotive 8.0 6.0 4.0 – Higher Volume 2.0 0.0 USA Japan China UK ESTIMATING ESTIMATING THE MARKET POTENTIAL OF THE AUTO MARKET POTENTIAL • In this study, the automotive In market was chosen because it offers higher volume in sales offers • The market potential was The estimated by multiplying automotive sales by an average refrigerant volume required of 24oz. 24oz. • The answer obtained was The approximately 14.2 thousand metric tons per year metric COMPARING COMPARING THIS VALUE TO DATA DATA PROVIDED BY THE UNFCCC UNFCCC • The market potential was then compared to information The obtained from United Nations Framework Convention on Climate Change (UNFCCC). The table is on the following Climate (UNFCCC). slide slide • The UNFCCC provides the amounts of varying The refrigerants produced by the participating countries refrigerants – These countries are Australia, Colombia, the European These Union and its member states, Japan, Switzerland and the USA. USA. • Using the aforementioned estimation, it was calculated Using that the US accounts for 1/10th R-134a production cited that 134a by UNFCCC by • This value seems reasonable given the volume of This automobile purchased each year automobile PRODUCTION PRODUCTION & RELEASE DATA PROVIDED BY THE UNFCCC PROVIDED HOLE IN THE OZONE HOLE DESIGN DESIGN OF NEW REFRIGERANTS BASED BASED ON CONSUMER PREFERENCE PREFERENCE FUNCTIONS AND THE CHOSEN MARKET THE METHODS METHODS FOR EXAMINING REFRIGERANTS REFRIGERANTS Analysis from a list • Only known molecules Only can be considered can • Extensive database is Extensive necessary for complete analysis analysis • Limited to molecules Limited already identified as refrigerants refrigerants Group contribution Group theory theory • Allows for the Allows consideration of unknown molecules unknown • No need for extensive No databases databases • Generalized approach DISCUSSION DISCUSSION OF GROUP CONTRIBUTION THEORY CONTRIBUTION GROUP GROUP CONTRIBUTION THEORY THEORY • Developed by observation of existing Developed molecules as a way to predict the basic characteristics of ANY molecule characteristics • Uses characteristics of each functional Uses group to estimate the characteristics of a molecule formed from the functional groups groups MOLECULE MOLECULE MADE OF FUNCTIONAL GROUPS FUNCTIONAL • Specific groups of Specific atoms within a molecule molecule • Responsible for the Responsible chemical make-up of chemical up the molecule the • Example: Example: 1,1,1,2-Tetrafluoroethane – 3 different different functional group types types – 6 total groups FUNCTIONAL GROUPS FUNCTIONAL Acyclic Acyclic Groups Groups Cyclic Cyclic Groups Groups Halogen Halogen Groups Groups Oxygen Oxygen Groups Groups Nitrogen Nitrogen Groups Groups Sulfur Sulfur Groups Groups -CH3 R-CH2-R -F -OH -NH2 -SH -CH2- 2R>CH-R -Cl -O- >NH -S- >CH- 2R>C<2R -Br R-O-R 2R>NH R-S-R >C< R=CH-R -I >CO >N- =CH2 R=C<2R 2R>CO R=N- =CH- -CHO -CN =C< -COOH -NO2 =C= -COO=O = represents a double bond, bonding site -- represents a single bond, bonding site R represents a ring bonding site ENUMERATION ENUMERATION MODELING IN EXCEL EXCEL USING GROUP CONTRIBUTION THEORY CONTRIBUTION ENUMERATION ENUMERATION VS. OPTIMIZATION OPTIMIZATION Enumeration Optimization Model • No initial guess required • Calculates every possible Calculates option option • Complete confidence in Complete solution solution • Very time consuming – requires hours or days requires for processing for • Requires a good initial Requires guess guess • Calculates only options Calculates which lead to a more likely solution likely • Almost complete Almost confidence in solution confidence • Less time consuming – Less requires a few hours for requires processing processing WHAT WHAT IS THE ENUMERATION METHOD? METHOD? • Also known as the exhaustive method or brute force Also method method • Take into account every possible combination of Take functional groups functional -CH3 n-CH3 = 0 -CH2- n-CH2-= 0 >CH- 012012012012012012012012012 n>CH-= 1 1 Iterations for 3x3 = 27 2 0 2 1 2 0 1 2 VBA CODE VBA VBA CODE VBA VBA CODE VBA REFRIGERANT MODELING REFRIGERANT Our design 1. Design Basic Thermodynamic Design Optimization Optimization 2. Include structural constraints 3. Include physical constraints 4. Find information on existing Find molecules molecules 5. Select molecules for further research FLOW FLOW OF VARIABLES FROM GROUP GROUP CONTRIBUTION THEORY THEORY HOW HOW TO USE GROUP CONTRIBUTION THEORY CONTRIBUTION N Tb = 198.21 + ∑ ni * Tbi i =1 Tc = Pc = Tb N (0.584 + 0.965 * ∑ ni * Tci − ∑ ni * Tci i =1 i =1 N 2 1 N N 0.113 + 0.0032 * ∑ ni * ai − ∑ ni * Pci i =1 i =1 N C p 0 a = ∑ ni * C p 0 ai i =1 2 N − 37.93 + ∑ ni * C p 0bi − 0.21 * Tavg i =1 N 2 + ∑ ni * C p 0 ci − 3.91 * 10 − 4 * Tavg i =1 N 3 + ∑ ni * C p 0 di − 2.06 *10 −7 * Tavg i =1 Tb=Boiling temperature Tbi=contribution of group i to Tbi=contribution boiling temperature boiling Tc=critical temperature Tci= contribution of group i to critical Tci= temperature temperature ai=number of atoms in group i Pci= contribution of group i to critical Pci= pressure pressure Tavg= average temperature (user Tavg= defined) defined) Cp0ai=a contribution to heat capacity Cp0bi=b contribution to heat Cp0bi= capacity Cp0ci=c contribution to heat Cp0ci= capacity Cp0di=d contribution to heat Cp0di= capacity at average temperature *** Equations 1-4 are the only ones *** are dependent upon values of ni dependent THERMODYNAMIC THERMODYNAMIC EQUATIONS EQUATIONS • Liquid Heat Capacity at Tavg (1−Tavgr)1/ 3 1.742 1 0.45 Cpla = * Cp0a + 8.314* 1.45+ + 0.25*ω *17.11+ 25.2 * + (1−Tavgr) 4.1868 1− Tavgr Tavgr • Heat of vaporization at boiling temperature (Riedel Heat Method) Method) ln(Pc ) − 1.013 ∆H vb = 1.093 * R * Tc * Tbr * 0.930 − Ttemperature • Heat of vaporization at evaporation br ∆H ve 1 − Tevp Tc = ∆H vb * 1− T T b c 0.38 PRESSURE EQUATIONS PRESSURE • Vapor pressure at condensing Vapor ln (Pvpcr [ −G 2 )= * 1 − T cndr + k * (3 + T cndr )(1 − T cndr T cndr )3 ] Pvpc = Pvpcr * Pc • Vapor pressure at evaporation ln (Pvper [ −G 2 )= * 1 − T evpr + k * (3 + T evpr )(1 − T evpr T evpr Pvpe = Pvper * Pc )] 3 GROUP GROUP COMBINATION CONSTRAINTS CONSTRAINTS 1. 1. 2. 3. Structural feasibility Size and molecular weight Vapor pressure STRUCTURAL FEASIBILITY STRUCTURAL • Even number of groups with odd number of Even bonding sites bonding • 2 “-CH3” groups or 1 “-CH3” group and 1 “>CHgroups group ” group • Groups must be able to connect to form Groups ONE molecule ONE • 2 “-CH3” groups cannot connect with 2 “-F” groups groups to make ONE molecule • Total number of bonding sites should be Total even even • 2 bonding sites make 1 bond STRUCTURAL STRUCTURAL FEASIBILITY CON’D CON • Number of each bonding type should be even Number • 2 “=CH2” groups, 2 “=O” groups, or 1 “=CH2” with 1 groups, groups, with “=C=” with 1 “=O” group with • Mixed bonding types should have a transition Mixed group group • 1 “=CH2” and 1 “-F” requires 1 “=CH-” group and requires • Every branch should have an edge (end cap) • Example: 1 “>C<” group have 4 branches which will Example: group require 4 groups with only 1 bonding site such as “-F” MOLECULAR SIZE MOLECULAR • Minimum number of groups is 2 • Maximum number of groups is 10 – Max groups by type: one bond = 7 – C-CH-C(F)7=10 one two bonds = 8 – CH3-(CH2)8-CH3=10 two =10 three bonds = 4 – (CH)4(F)6=10 four bonds = 2 – (C)3(F)8=11 four • Our results show that the maximum number of Our groups used is 9 groups • Typically larger molecules have higher boiling Typically points making them unfit for refrigeration points VAPOR PRESSURE VAPOR • Minimum vapor pressure at evaporation Minimum temperature of 1 bar temperature – Atmospheric pressure • Maximum vapor pressure at Maximum condensation of 10 bar condensation – Mechanical compressibility factor – Multi-stage compressor (cost prohibitive) • Heat capacity must be positive – Negative heat capacity is not possible Negative physically physically “MAKING” A MOLECULE • n-F = 2 • n-Cl = 2 Cl • n>C< = 1 >C< • Freon • n-CH3 = 2 • n-CH2- = 2 • Butane • n-CH3 = 2 CH3 • n>CH- = 1 • n-CH2- = 1 • n-OH = 1 OH • iso-butanol • n-CH3 = 3 CH3 • n>CH- = 1 • iso-butane • n=CH2 = 1 =CH2 • n=C< = 1 =C< • n-O- = 1 • n-F =2 • ?? MOLECULE MAKER MOLECULE n= sCH3 sCH2s ssCHs ssCss dCH2 dCHs dCss dCd rCH2r rrCHr rrCrr drCHr drCrr sF sCl sBr sI sOH sOs rOr ssCO rrCO sCHO sCOOH sCOOs doO sNH2 ssNH rrNH ssNs drNs sCN sNO2 sSH sSs rSr 2 2 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6 Pvpe Pvpc heatcap Physical Constraints FALSE FALSE TRUE Structural Constraints Ynt true oddfree true connected true nofree true bond type true singlefree true double free true triple free true single cyclic free true double cyclic free true mixed true end cap false real structure Binary Variables Yss Ydd Ytt Ysr Ydr Yr Ysd Yst Yssr Ysdr Ydsr Y0m Ym5 Ym1 Ym6 Ym2 Ym7 Ym3 Ym8 Ym4 Ym9 0 0 1 0 1 1 1 1 1 1 1 1 1 1 1 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 1 0 1 0 1 0 1 0 Thermodynamic Properties Tavg K Tevp K Tcnd K N groups K Tboil K Tcrit bar Pcrit J/molK heatcap Tb reduced K Tavg reduced Tcnd reduced Tevp reduced alpha beta omega J/molK heat capacity kJ/mol enthalpy of vaporization boil temp kJ/mol enthalpy of vaporization evap temp h G ka Pvpcr Pvper bar Pvpc bar Pvpe t= iterations 27 294 272 317 6 334.61 499.73 35.01 0.131 0.670 0.588 0.633 0.544 -0.942 -2.734 0.345 45.325 29.502 33.335 7.179 3.790 0.561 0.015 0.002 0.533 0.070 0.000 RESULTS FROM EXCEL RESULTS • 3,692,945 possible solutions (16 hours) • 649 solutions • 566 structurally feasible solutions (passed the 566 filter, but not feasible) filter, • Since each solution was evaluated by Since referencing online databases the process for finding molecules was extremely tedious finding • are included in optimization function are calculations for comparison calculations β VALUES Chemical Formula CH2=CH-F CH3 -CH=CH2 CH2=CF2 CH2=CH-Cl CH2=CFCl CH2=C=CH-F CH2=CH-O-F CH2=CH-C(=O)-F Cl2 F-Br CH2=CCH3F CH2=CH-CH2-F CH2=C=CF2 CH2=C=C=C=O FSH CH2=C(-F)-O-F FNH2 cyc(CH=CH-CH2) cyc(CH=CH-O) O=CH-Br R134a H = Σxiyij β = H2/H1 0.61 0.61 0.51 0.50 0.49 0.40 0.40 0.40 0.40 0.39 0.39 0.39 0.39 0.39 0.39 0.39 0.39 0.39 0.39 0.38 0.85 Rank 1.39 1.39 1.68 1.69 1.75 2.13 2.14 2.14 2.15 2.17 2.17 2.18 2.18 2.19 2.19 2.19 2.19 2.19 2.20 2.22 1.00 2 4 8 1 3 5 6 20 7 9 10 11 12 13 14 15 16 17 18 19 - • Using the preceding six quantitative plots, a value for β Using for each possible refrigerant can be defined LIMITATIONS LIMITATIONS OF GROUP CONTRIBUTION THEORY CONTRIBUTION • Actual data compared with data found using functional Actual group theory abs(Tb(theory)-Tb(act))= ∆Tb abs(T )= • Average ∆Tb = 39.7K Average • Examples: Examples: 3-fluoro-1-propene, min ∆Tb=1.2K propene, 1-fluoro-2-propanone, max ∆Tb=89.9K max ERRO R Tboil Tcrit Pcrit max 47% 32% 36% min 0.4% 1.1% 0.5% average 16% 14% 12% LIMITATIONS LIMITATIONS OF FUNCTIONAL GROUP THEORY GROUP • Errors reveal inconsistencies with Errors functional group theory functional • Possibly good refrigerants were likely Possibly excluded from our findings excluded RECOMMENDATIONS RECOMMENDATIONS RECOMMENDATIONS RECOMMENDATIONS 1. 2. 2. 3. 3. Develop correlations to relate data obtained from models to Develop consumer preference functions. Relationships could be developed to relate properties that can be found from the empirical data to those exclusive to an individual molecule. Link spreadsheets to databases to quickly search through Link molecules. Not all properties can be examined from a molecule’s empirical formula or structure. Many molecule empirical databases, in periodicals, for potential refrigerant molecules are available for possible refrigerants. These databases could eliminate error caused by property estimation. A large scale survey needs to be performed. A large scale large random survey is needed to find actual consumer preferences to refrigerant properties. RECOMMENDATIONS RECOMMENDATIONS 3. More structural constraints need to be More developed. Some molecular structures pass the filters in the iterative method, but do not exist in reality. exist 4. Considering refrigerant blends would create Considering many more options for refrigerant solutions. many 5. Laboratory study - The laboratory setting Laboratory The offers the benefit of being able to measure properties for synthesized refrigerants. In this way, more accurate correlations for group contributions or efficiency could be developed. developed. QUESTIONS? QUESTIONS? WORKS CITED WORKS 1. 2. 3. 4. 5. 6. 7. 8. History of the Refrigerator. History.com. 2/7/2007 History <http://www.history.com/exhibits/modern/fridge.html>. Refrigerant Data Update. January 2007. Heating/Piping/Air January Conditioning Engineering. Feb. 7, 2007 <http://www.hpac.com/Issue/Article/44475/Refrigerant_Data_Update>. What you Should Know about Refrigerants when Purchasing or What Repairing a Residential A/C System or Heat Pump. Jan. 29, 2007. Environmental Protection Agency. Jan. 29th, 2007 Environmental 2007 <http://www.epa.gov/ozone/title6/phaseout/22phaseout.html>. HCFC Phaseout Schedule. April 16th, 2006. Environmental Protection HCFC April 2006. th Agency. Feb. 5 , 2007 Agency. 2007 <http://www.epa.gov/ozone/title6/phaseout/hcfc.html>. Air Conditioners 2004. July 2004. Snapshot International. Feb. 11th, Air July 2007 <http://dx.doi.org/10.1337/us080034>. 2007 American Housing Survey for the United States: 2005. August 2006. August U.S. Department of Housing and Urban Development and the US Department of Commerce. Feb 11th, 2007 Department 2007 <http://www.census.gov/prod/2006pubs/h150-05.pdf>. Trends in Residential Air-Conditioning Usage from 1978 to 1997. July July th 24, 2000. US Department of Energy. Feb 11 , 2007 24, 2007 <http://www.eia.doe.gov/emeu/consumptionbriefs/recs/actrends/recs_ac_t rends.html>. The Properties of Gases and Liquids 5th Edition. O’Connell, J., B. Poling Edition. Connell, and J. Pransnite.McGraw-Hill, New York, 2001. Chapter 2 (78-89). and APPENDIX APPENDIX CREATING CREATING MORE ACCURATE CONSUMER CONSUMER PREFERENCE FUNCTIONS FUNCTIONS • To accurately predict the refrigerant To market, a large scale survey needs to be performed performed • The large scale survey will eliminate The inaccuracies present in the survey prepared and presented previously prepared • These inaccuracies stem from the These inherent bias in a small survey inherent MORE MORE THERMODYNAMIC EQUATIONS EQUATIONS Pc 6.09648 6 + 1.28862 * ln (Tbr ) − 0.169347 * Tbr + Tbr 1.013 α = −5.97214 − ln β = 15.2518 − α ω= β 15.6875 6 − 13.4721 * ln (Tbr ) + 0.43577 * Tbr Tbr Tbr * ln(Pc 1.013) h= 1 − Tbr G = 0.4835 + 0.4605 * h k= h G − (1 + Tbr ) (3 + Tbr )(1 − Tbr )2 THERMODYNAMICS THERMODYNAMICS • Functional group theory can be used to Functional determine many important characteristics characteristics • Temperature-Entropy data are needed Entropy to accurately determine efficiency, but cannot be determined from functional group theory group • ∆H/Cp is used to estimate efficiency GAMS CODE GAMS GAMS CODE GAMS GAMS CODE GAMS GAMS CODE GAMS SYSTEMATIC DESIGN SYSTEMATIC ENVIRONMENTAL EFFECTS ENVIRONMENTAL • Ozone depletion potential – Only heavy halogens are known to Only contribute contribute ODP = 0.05013 (n Cl ) 1.510 * exp(-3.858 / τ ) • Global warming potential – Requires experiments to find radiative Requires efficiency and time dependent decay efficiency OBTAINING OBTAINING VALUES FOR THE OBJECTIVE FUNCTION OBJECTIVE y = 176.5x - 29.778 2 Vapor Pressure vs. LEL R = 0.0351 • Flammability and Flammability explosiveness explosiveness 35000 30000 Vapor Pressure (mmHg) 25000 – Based on lower Based explosive limit explosive – Based on vapor pressure – No strong correlation 20000 15000 10000 5000 0 0.5 0.7 0.9 1.1 1.3 1.5 1.7 1.9 2.1 • Toxicity Toxicity -5000 LEL (% ) Flash Point vs. LEL th rd 6 Order Polynomial 3 Order Polynomial Linear y = -66.281x + 68.752 3 2 y = 50.242x - 2 31x + 275.14x - 92.641 2 6 R = 0.4087 5 4 3 2 y = -1196.1x + 9953.1x - 34677x + 65066x - 69409x + 39657x - 9368.3 2 2 R = 0.5702 R = 0.4151 40 20 0 0 0.5 1 1.5 o Flash Point ( C) -20 -40 -60 -80 -100 -120 LEL (% ) 2 2.5 – Based on experimental Based results results – Found only for existing Found molecules molecules CORRELATION CORRELATION WITH MOLECULAR WEIGHT MOLECULAR dH/Cp 1.3 y = -0.0095x + 1.749 2 R = 0.9276 1.1 average comparison 0.9 0.7 0.5 0.3 50 75 100 125 molecular wieght 150 CORRELATION ∆H/CP CORRELATION WITH COP COP Correlation of dH/Cp with COP 2 1.8 1.6 1.4 dH/Cp 1.2 dH/Cp 1 Poly. (dH/Cp) 0.8 0.6 0.4 0.2 0 0 1 2 3 COP (PRO/II) 4 5 EVALUATING EVALUATING THE COP FOR NEW REFRIGERANTS NEW • The following journal publications The describe in detail how the COP for new refrigerants can be evaluated refrigerants 1. Fleming, John S., Alex C. Bwalya and William Dempster. Fleming, “The testing and evaluation of trial refrigerants: Part 1. The System description.” International Journal of Energy System International Research 24.14 (2000): 1217-1241. 24.14 2. Fleming, John S., Alex C. Bwalya and William Dempster. Fleming, “The testing and evaluation of trial refrigerants: Part 2. The The practical use of measured data.” International The International Journal of Energy Research 24.14 (2000): 1243-1256. 24.14 EVALUATING EVALUATING THE COP FOR NEW REFRIGERANTS NEW • Following a similar procedure, outlined in Following these two articles, would provide the COP for the tested refrigerants the • Then a correlation could be developed to relate Then the COP to functional group contributions the • This correlation would allow for a more This rigorous analysis of all the possible refrigerants refrigerants • The refrigerants could then be ranked and The analyzed using more accurate consumer preference functions preference ...
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