Presentation is loading. Please wait.

Presentation is loading. Please wait.

Product Design Property Estimation Chapter 3 Article on Phys. Property Estimation CHEN 4253 Terry A. Ring University of Utah.

Similar presentations

Presentation on theme: "Product Design Property Estimation Chapter 3 Article on Phys. Property Estimation CHEN 4253 Terry A. Ring University of Utah."— Presentation transcript:

1 Product Design Property Estimation Chapter 3 Article on Phys. Property Estimation CHEN 4253 Terry A. Ring University of Utah

2 Types of Properties Thermodynamic Properties Transport Proprieties Kinetic Properties

3 Vapor Pressure of Mixture VOC – Volatile organic content Flash Calc with Process Simulator Hand Calc. –Equation of State –Activity Coefficient Equation Aspen/ProMax –Pick Thermo Package Several are available –Polar liquids vs non-polar –Aqueous vs non-aqueous –High P vs low P Input Components Set up Flash unit with feed streams Set Feed Stream composition Run Calc –Vapor –Liquid –Solid

4 Design Methods Physical Properties –Group Contributions –Thermo package in Process Simulator Process Simulation of Refrigeration cycle –Condenser –Vaporizer –Pump –Valve to flash liquid to vapor

5 Refrigerant Design Large negative Joule-Thompson Coefficient Large Enthalpy of Vaporization High Liquid Heat Capacity Low Pressure -T boil below RT –Vapor Pressure > 1.4 Bar to assure no air leaks High Pressure – Compressor/Condensor –Vapor Pressure < 14 Bar to keep compression ratio less than 10

6 Solubility Parameter Prediction Solubility Parameter –Solubility of liquid in liquid –Solubility of solid in liquid –Solubility of polymer in liquid Group Contributions –Three parameters Dispersive Polar Hydrogen Bonding

7 Flory-Huggins solution theory The result obtained by Flory[1] and Huggins[2] is[1][2] The right-hand side is a function of the number of moles n1 and volume fraction φ1 of solvent (component 1 or a), the number of moles n2 and volume fraction φ2 of polymer (component 2 or b), with the introduction of a parameter chi, χ, to take account of the energy of interdispersing polymer and solvent molecules.functionmolessolventcomponentchi energy Molar volume of polymer segment δ are Hildebrand solubility parameters, δ=√((ΔH vap -RT)/V molar ) δ=√(δ d 2 + δ p 2 + δ h 2 ), linkage to Hansen Solubility parameters

8 Hansen Solubility Parameter Hansen Solubility Parameters were developed by Charles Hansen as a way of predicting if one material will dissolve in another and form a solution [1]. They are based on the idea that like dissolves like where one molecule is defined as being 'like' another if it bonds to itself in a similar way.Charles Hansendissolvesolution[1] Specifically, each molecule is given three Hansen parameters, each generally measured in : The energy from dispersion bonds between moleculesdispersion bonds The energy from polar bonds between moleculespolar bonds The energy from hydrogen bonds between moleculeshydrogen bonds These three parameters can be treated as co-ordinates for a point in three dimensions also known as the Hansen space. The nearer two molecules are in this three dimensional space, the more likely they are to dissolve into each other. To determine if the parameters of two molecules (usually a solvent and a polymer) are within range a value called interaction radius (R0) is given to the substance being dissolved. This value determines the radius of the sphere in Hansen space and it's center is the three Hansen parameters. To calculate the distance (Ra) between Hansen parameters in Hansen space the following formula is used: Combining this with the interaction radius gives the relative energy difference (RED) of the system: RED < 1 the molecules are alike and will dissolve RED = 1 the system will partially dissolve RED > 1 the system will not dissolve See Articles Solvents_Data.pdf

9 Group Contribution Methods Group (bond) Contribution Methods –n i =number of groups of type i in polymer repeat unit or molecule –N= number of group types –A i =group contribution to property p{n} –Mw i = Molecular weight of group I, sometimes another group contribution property –d=exponent for property

10 Group Contribution Methods Polymer Glass Transition Temp. Polymer Molar Volume Polymer Density Polymer Water Absorption –P. 66 of your book

11 Liquid Surface Tension/Wetting Group Contribution Method Contact Angle – Young’s Equation cos Θ = (γ SV - γ SL )/ γ LV Wetting when Θ => 0 Predicting Liquid surface tension γ LV =[ρ L M w -1 Σ(N i P i )] 4 P i =Parachor Value of group –Surface tension in [dyne/cm] –Density [gm/cm^3] –Mw [gm/mole] Liquid Mixtures surface tension based upon mole fraction, X i γ LV = Σ γ LV_i X i

12 Parachor Values Tables from Ring, Fundamentals of Ceramic Powder Processing, Academci Press 1999. CH2=CH O CH3 GroupsP i C 34.8 H to C 617.1 O to ether 120 Double Bond123.2 γ LV =[ρ L M w -1 Σ(N i P i )] 4

13 Select Surfactants for Dispersion Lower Surface tension of a liquid –Detergency Hydrophilic-lipophilic Balance-HLB –HLB = 7+ ΣH i – ΣL i Stabilized Suspension –HLB surfactant = HLB particle Tables from Ring, Fundamentals of Ceramic Powder Processing, Academic Press 1999.

14 Group Contributions - HLB Tables from Ring, Fundamentals of Ceramic Powder Processing, Academic Press 1999. TiO 2

15 Drago E and C Used to predict the Heat of mixing, ΔH AB –Acid (A) – Base (B) Interactions –Good for non-polar solvents –E = Electrostatic Contributions –C = Covalent Contributions

16 Acids

17 Bases Can be predicted from Infrared or NMR peak shifts due to mixing See Wettability By John C. Berg

18 Wetting - Good Method Work of Adhesion between to materials, W a AB = -(γ SV -γ SL ) – γ LV Energy to replace solid-vapor and liquid-vapor interfaces with liquid-vapor interface. –Predicted by Liquid

19 Wetting –Fowkes (Drago) Method Work of Adhesion –N = moles of interaction functional groups per unit area –f = factor to convert enthalpy to work

20 Transport Properties Molecular Dynamics Calculations –Intermolecular Forces Lennard-Jones Potentials between Atoms –Location of Atoms in Molecule –Molecules Free to move –Monte Carlo Methods –Statistical Analysis Molecular Structure Determined From Otimization Drug Molecule Binding D AB = 2 /t Gives Upper and Lower Bounds of Property

21 Drug/Enzyme Target Development

22 Bio Concentration BioConcentration factor=BCF log BCF = 0.76 log K ow -0.23 –K ow =octanol/water partition factor –K ow =X o_w /X w_o =(γ ∞ o_w Mw o )/(γ ∞ w_o Mw w ) Easily get this from a liquid-liquid Flash calc. Toxicity –LC 50 =lethal concentration when 50% are dead –log LC 50 = -0.87 log K ow - 0.11 p. 73 of your book

23 Kinetic Parameter Prediction Flash Point –T f =0.683 T boil -119K Explosive Potential depends upon the flash point T boil from flash calc. p. 73 of your book

24 Many Desired Properties of a Product 1) Determine list of desired properties 2) Use desired properties to determine –Figure of Merit Grouping of Important Qualities for a product and/or its use. –Minimized Deviations from Ideal Property Values Minimize Σ (A i -A desired ) 2 for various properties, A i, for product formulations. [p. 49] Often minimization is carried out with upper and lower bounds on specific properties or in comparison with competitor’s product

25 Minimization Problem x,y,z are property axes Minimize Σ (A i -A desired ) 2 With constraints of –|A 1 -A 1,desired | < 0.05 A 1,desired –|A 2 -A 2,desired | < 0.1 A 2,desired

26 Overview Property Estimation –Use Thermo-package in Process Simulator –Use Hansen solubility parameters –Use Group Contribution Methods –Use statistical mechanics

Download ppt "Product Design Property Estimation Chapter 3 Article on Phys. Property Estimation CHEN 4253 Terry A. Ring University of Utah."

Similar presentations

Ads by Google