Supplementary Notes for Chapter 9 Mixture Thermodynamics
|
|
- Kellie Campbell
- 5 years ago
- Views:
Transcription
1 Supplementary Notes for Chapter 9 Mxture Thermodynamcs Key ponts Nne major topcs of Chapter 9 are revewed below: 1. Notaton and operatonal equatons for mxtures 2. PVTN EOSs for mxtures 3. General effects of mxng on heat and work nteractons and state property changes 4. Gbbs-Duhem relatonshp and thermodynamc consstency 5. Mxng functons 6. Ideal gas mxtures and deal solutons 7. Fugacty and actvty concepts 8. Fugacty coeffcents from PVTN EOS property relatonshps 9. Actvty coeffcents from G property relatonshps 10. Ideal reversble work effects n mxng or separatng components 1. Notaton and operatonal equatons for mxtures Partal molar propertes are extensve propertes that determne how derved propertes change as a functon of mole number or composton. For a general property, B, whch could be U, H, S, V, or A, the partal molar B s defned as: B B = (9-8) N TPN,, j [] There are a few mportant relatonshps n Chapter 9 that allow you to calculate partal molar propertes gven the extensve form B of the property or the ntensve form B. An mportant one of these s: B B = B x j (9-53) j 1 x j TPxj,,, Note that for a bnary, two component system that Eq.(9-53) quckly yelds the tangentntercept method of evaluatng partal molar propertes from a graph of B versus x j at constant T and P. For example, see Fgure 9.1 and the dscusson n the text. 2. PVTN EOSs for mxtures Volume and pressure explct equatons of state are commonly used to represent the volumetrc propertes of flud mxtures of both gases and lquds. The key feature that dstngushes a mxture EOS from ts pure component counterpart s the presence of compostonal dependence. Ths dependence expresses tself n the form of so-called mxng rules that ncorporate pure component EOS parameters and weght them [ ] Modfed: 10/16/2003 1
2 proportonally to the concentraton of each component followng a specfc mathematcal recpe. Wth the excepton of the vral equaton of state no rgorous theoretcal approach exsts to specfy a mxng rule recpe. The most common approach s to use some varaton of the Lorentz-Bertlelot rules used by van der Waals and others over a century ago (see Eqs (9-17 and 9-18). In mxtures that exhbt consderable non-dealty, a bnary or hgher order nteracton parameter δ j s ntroduced to capture specfc nteractons between molecules of type and j. Each mxture PVTN EOS wll have some prescrbed recpe for ts mxng rules. For example see the conventons followed for the RK and PR EOS on pages In stuatons where hgh level non-deal effects are present often more complex forms for mxng rules are ntroduced. The Wong-Sandler and Chueh-Prausntz rules dscussed on pages are examples of ths type of mxng rule. 3. General effects of mxng on heat and work nteractons and state property changes There are a few typcal classes of problems that you should be able to solve. These nclude: Calculatng the change n enthalpy as a result of mxng to determne a heat nteracton for mantanng a constant temperature or to follow some prescrbed recpe for temperature, for example, see Problem 9.2. Calculatng a change n volume as a result of mxng to determne a change n densty or the magntude of a work nteracton at constant pressure. 4. Gbbs-Duhem relatonshp and thermodynamc consstency For mxture data and correlatons for any property B, the partal molar quanttes are nterrelated through the use of the general Gbbs-Duhem relatonshp: n B B xdb = dt dp = 1 T + Px, P Tx, Note that B s a functon of T, P, and x for = 1,, n. The Gbbs-Duhem relatonshp can be appled to any partal molar property, such as: H,V, µ, ln ˆφ, ln ˆf,G, ln a, ln γ,etc. For a bnary system, the Gbbs-Duhem relatonshp s frequently used to check thermodynamc consstency of thermodynamc data; for example, actvty coeffcents. Addtonally, for a bnary mxture, f you have a measurement of the partal molar property of one component as a functon of composton you can determne the property for the other component. Modfed: 10/16/2003 2
3 5. Mxng functons n ( 1 ) ( 1 ) B B T,P,x, =,...,n x B T,P,x, =,...,n (9-68) mx = 1 whch s n ntensve form. A smlar expresson results for B mx n extensve form wth x replaced by N. You should be famlar wth the concept of a reference state (+). Strctly speakng, reference states are arbtrary, but n practce several common forms appear for x +, T +, and P +, for example: (1) pure at T and P of the mxture (2) x + 0 at T and P of the mxture that s n an nfnte dluton state where Henry s law behavor s followed (3) a fxed composton of 1 molal or 1 molar concentraton at T and P of the mxture that behaves n some deal manner commonly used for electrolytes (see Chapter 12) 6. Ideal gas mxtures and deal solutons In ths secton of the text, a set of defntons were used to characterze non-deal solutons n terms of a devaton from deal behavor. g deal gas mxture: G =µ = RT ln yp +λ( T ) ID deal soluton: G =µ = RT ln x +Λ ( T,P ) where λ and Λ are constants specfc to component. The key ponts to remember are that partal pressure y P s the deal gas mxture compostonally dependent varable whle for deal condensed phase soluton t s the mole fracton x. 7. Fugacty and actvty concepts For a real mxture or soluton we ntroduce the followng models: real flud mxture: G = µ = RT ln ˆf +λ real soluton: G =µ = RT ln a +µ ( T,P,x ) whch lead to the followng defntons of the fugacty coeffcent and actvty coeffcent ˆφ and γ, respectvely: and ˆ ˆf φ ---- represents a devaton from deal gas/flud mxture behavor yp ˆf f ˆ γ = ---- represents a devaton from deal soluton behavor a ˆ f + x Modfed: 10/16/2003 3
4 8. Fugacty coeffcent relatonshps from PVTN EOS property models There are two basc approaches one nvolvng pressure explct EOSs lke the PR or RKS, and the other for volume explct EOSs, such as Vral or Correspondng States formulatons, ncludng compressblty expansons n densty or molar volume or smlarly structured equatons. Equatons (9-142) and (9-143) provde convenent forms for pressure explct EOS models for mxture and pure components, respectvely, whle Eqs(9-129) and (9-127) work for volume explct EOS models. For example, for component n a mxture: or RT V ln ˆ P RT φ = dv RT ln Z N V T,V,Nj [] RT ln ˆφ =+ P 0 RT V P dp At ths pont you should know how to calculate the fugacty or fugacty coeffcent for a pure component usng a pressure- or volume-explct EOS or for a component n a bnary mxture usng a sutable PVTN EOS that has been properly formulated wth mxng rules for ts constants (for example, the a mx and b mx constants n the RKS EOS) n terms of pure component propertes and a bnary nteracton parameter, eg δ j. Beng able to do ths provdes a powerful tool for calculatons requred later n the course, for example to estmate the vapor pressure of a pure component you would equate ˆφ for the lqud and vapor phases at a gven temperature by estmatng the P and usng the EOS to calculate lqud and vapor volumes (denstes) untl the fugacty coeffcents for each phase were equal. 9. Actvty coeffcents from G property relatonshps The actvty coeffcent s defned n terms of the partal molar excess Gbbs free energy of mxng and can be drectly related to the fugacty usng the defnton of actvty: G RT ln γ = G = N [] T,P,Nj + where γ a ˆ ˆ x = f f x To evaluate the partal dervatve we need an expresson for G : ID G Gmx G = f T, P, N ( = 1,..., n) whch represents the dfference between the actual enthalpy of mxng and the enthalpy of mxng for an deal soluton at the same T, P and composton as the actual mxture. Modfed: 10/16/2003 4
5 Typcally, one has access to a model that gves G = G / N as a functon of T, P, and x, and wth Eq. (9-53) one can easly obtan γ for each component, e.g. G RT ln γ = G x j x j T,P,x j, In addton, the Gbbs-Duhem equaton can be appled to calculate the other actvty coeffcent gven a set of data for one component (e.g. f γ solvent s known as a functon of composton then γ solute can be estmated by ntegratng the Gbbs-Duhem relatonshp). Conversely, f both actvty coeffcents of a bnary mxture are known then the Gbbs- Duhem equaton can be used to check the thermodynamc consstency of a gven set of data. For example, the slope and area tests have been developed specfcally for ths purpose (see pages and Fg. 9.3). A key ssue here s how to deal wth the standard or reference state condton (+), as that wll have a drect effect on the magntude and behavor of γ. If a symmetrc reference state s used then ˆ f + = f( pure ) and the Lews and Randall rule s followed as x goes to 1.0 wth γ approachng 1.0. Alternatvely, a unsymmetrcal reference state can be used where the nfnte dluton behavor as defned by Henry s Law determnes that γ ** approaches 1.0 as x goes to 0. Ths s frequently called the McMllan-Mayer reference condton. Another popular alternatve commonly employed for electrolytes s to use a 1 molal soluton at T and P of the mxture where the mxture follows Henry s Law n dlute solutons as x or m 0 (see Fg. 9.9 for example). The dscusson n the text from pages 360 to 365 should be carefully revewed to see how the actvty coeffcent s related to fugacty behavor for dfferent reference state condtons. Partcular attenton should be pad to Fgures Ideal reversble work assocated wth mxng or separatng components Under sothermal condtons the reversble work s equal to the net change n Gbbs free energy assocated wth the process, more generally the reversble work can be related drectly to the change n avalablty usng the methods ntroduced n Chapter 14. Examples 9.8 and 9.9 should be revewed to see how the concepts are employed. The approach s qute straghtforward f you are dealng wth a steady state process. Thngs are a bt more complcated f the system or process condtons are changng wth tme, but the general concept remans unchanged. [ ] Modfed: 10/16/2003 5
Introduction to Vapor/Liquid Equilibrium, part 2. Raoult s Law:
CE304, Sprng 2004 Lecture 4 Introducton to Vapor/Lqud Equlbrum, part 2 Raoult s Law: The smplest model that allows us do VLE calculatons s obtaned when we assume that the vapor phase s an deal gas, and
More informationOpen Systems: Chemical Potential and Partial Molar Quantities Chemical Potential
Open Systems: Chemcal Potental and Partal Molar Quanttes Chemcal Potental For closed systems, we have derved the followng relatonshps: du = TdS pdv dh = TdS + Vdp da = SdT pdv dg = VdP SdT For open systems,
More informationIf two volatile and miscible liquids are combined to form a solution, Raoult s law is not obeyed. Use the experimental data in Table 9.
9.9 Real Solutons Exhbt Devatons from Raoult s Law If two volatle and mscble lquds are combned to form a soluton, Raoult s law s not obeyed. Use the expermental data n Table 9.3: Physcal Chemstry 00 Pearson
More information(1) The saturation vapor pressure as a function of temperature, often given by the Antoine equation:
CE304, Sprng 2004 Lecture 22 Lecture 22: Topcs n Phase Equlbra, part : For the remander of the course, we wll return to the subject of vapor/lqud equlbrum and ntroduce other phase equlbrum calculatons
More informationLecture. Polymer Thermodynamics 0331 L Chemical Potential
Prof. Dr. rer. nat. habl. S. Enders Faculty III for Process Scence Insttute of Chemcal Engneerng Department of Thermodynamcs Lecture Polymer Thermodynamcs 033 L 337 3. Chemcal Potental Polymer Thermodynamcs
More informationSolution Thermodynamics
Soluton hermodynamcs usng Wagner Notaton by Stanley. Howard Department of aterals and etallurgcal Engneerng South Dakota School of nes and echnology Rapd Cty, SD 57701 January 7, 001 Soluton hermodynamcs
More informationSolution Thermodynamics
CH2351 Chemcal Engneerng Thermodynamcs II Unt I, II www.msubbu.n Soluton Thermodynamcs www.msubbu.n Dr. M. Subramanan Assocate Professor Department of Chemcal Engneerng Sr Svasubramanya Nadar College of
More informationA Self-Consistent Gibbs Excess Mixing Rule for Cubic Equations of State: derivation and fugacity coefficients
A Self-Consstent Gbbs Excess Mxng Rule for Cubc Equatons of State: dervaton and fugacty coeffcents Paula B. Staudt, Rafael de P. Soares Departamento de Engenhara Químca, Escola de Engenhara, Unversdade
More informationEnergy, Entropy, and Availability Balances Phase Equilibria. Nonideal Thermodynamic Property Models. Selecting an Appropriate Model
Lecture 4. Thermodynamcs [Ch. 2] Energy, Entropy, and Avalablty Balances Phase Equlbra - Fugactes and actvty coeffcents -K-values Nondeal Thermodynamc Property Models - P-v-T equaton-of-state models -
More informationThe ChemSep Book. Harry A. Kooijman Consultant. Ross Taylor Clarkson University, Potsdam, New York University of Twente, Enschede, The Netherlands
The ChemSep Book Harry A. Koojman Consultant Ross Taylor Clarkson Unversty, Potsdam, New York Unversty of Twente, Enschede, The Netherlands Lbr Books on Demand www.bod.de Copyrght c 2000 by H.A. Koojman
More informationI wish to publish my paper on The International Journal of Thermophysics. A Practical Method to Calculate Partial Properties from Equation of State
I wsh to publsh my paper on The Internatonal Journal of Thermophyscs. Ttle: A Practcal Method to Calculate Partal Propertes from Equaton of State Authors: Ryo Akasaka (correspondng author) 1 and Takehro
More informationNUMERICAL DIFFERENTIATION
NUMERICAL DIFFERENTIATION 1 Introducton Dfferentaton s a method to compute the rate at whch a dependent output y changes wth respect to the change n the ndependent nput x. Ths rate of change s called the
More informationy i x P vap 10 A T SOLUTION TO HOMEWORK #7 #Problem
SOLUTION TO HOMEWORK #7 #roblem 1 10.1-1 a. In order to solve ths problem, we need to know what happens at the bubble pont; at ths pont, the frst bubble s formed, so we can assume that all of the number
More informationName: SID: Discussion Session:
Name: SID: Dscusson Sesson: Chemcal Engneerng Thermodynamcs 141 -- Fall 007 Thursday, November 15, 007 Mdterm II SOLUTIONS - 70 mnutes 110 Ponts Total Closed Book and Notes (0 ponts) 1. Evaluate whether
More information3. Be able to derive the chemical equilibrium constants from statistical mechanics.
Lecture #17 1 Lecture 17 Objectves: 1. Notaton of chemcal reactons 2. General equlbrum 3. Be able to derve the chemcal equlbrum constants from statstcal mechancs. 4. Identfy how nondeal behavor can be
More informationCHEMICAL REACTIONS AND DIFFUSION
CHEMICAL REACTIONS AND DIFFUSION A.K.A. NETWORK THERMODYNAMICS BACKGROUND Classcal thermodynamcs descrbes equlbrum states. Non-equlbrum thermodynamcs descrbes steady states. Network thermodynamcs descrbes
More informationNon-Ideality Through Fugacity and Activity
Non-Idealty Through Fugacty and Actvty S. Patel Deartment of Chemstry and Bochemstry, Unversty of Delaware, Newark, Delaware 19716, USA Corresondng author. E-mal: saatel@udel.edu 1 I. FUGACITY In ths dscusson,
More informationAppendix II Summary of Important Equations
W. M. Whte Geochemstry Equatons of State: Ideal GasLaw: Coeffcent of Thermal Expanson: Compressblty: Van der Waals Equaton: The Laws of Thermdynamcs: Frst Law: Appendx II Summary of Important Equatons
More informationThermodynamics General
Thermodynamcs General Lecture 1 Lecture 1 s devoted to establshng buldng blocks for dscussng thermodynamcs. In addton, the equaton of state wll be establshed. I. Buldng blocks for thermodynamcs A. Dmensons,
More informationThermodynamics II. Department of Chemical Engineering. Prof. Kim, Jong Hak
Thermodynamcs II Department of Chemcal Engneerng Prof. Km, Jong Hak Soluton Thermodynamcs : theory Obectve : lay the theoretcal foundaton for applcatons of thermodynamcs to gas mxture and lqud soluton
More informationEquation of State Modeling of Phase Equilibrium in the Low-Density Polyethylene Process
Equaton of State Modelng of Phase Equlbrum n the Low-Densty Polyethylene Process H. Orbey, C. P. Boks, and C. C. Chen Ind. Eng. Chem. Res. 1998, 37, 4481-4491 Yong Soo Km Thermodynamcs & Propertes Lab.
More informationTransfer Functions. Convenient representation of a linear, dynamic model. A transfer function (TF) relates one input and one output: ( ) system
Transfer Functons Convenent representaton of a lnear, dynamc model. A transfer functon (TF) relates one nput and one output: x t X s y t system Y s The followng termnology s used: x y nput output forcng
More information4.2 Chemical Driving Force
4.2. CHEMICL DRIVING FORCE 103 4.2 Chemcal Drvng Force second effect of a chemcal concentraton gradent on dffuson s to change the nature of the drvng force. Ths s because dffuson changes the bondng n a
More informationUniversity of Washington Department of Chemistry Chemistry 452/456 Summer Quarter 2014
Lecture 16 8/4/14 Unversty o Washngton Department o Chemstry Chemstry 452/456 Summer Quarter 214. Real Vapors and Fugacty Henry s Law accounts or the propertes o extremely dlute soluton. s shown n Fgure
More informationMcCabe-Thiele Diagrams for Binary Distillation
McCabe-Thele Dagrams for Bnary Dstllaton Tore Haug-Warberg Dept. of Chemcal Engneerng August 31st, 2005 F V 1 V 2 L 1 V n L n 1 V n+1 L n V N L N 1 L N L 0 VN+1 Q < 0 D Q > 0 B FIGURE 1: Smplfed pcture
More information...Thermodynamics. If Clausius Clapeyron fails. l T (v 2 v 1 ) = 0/0 Second order phase transition ( S, v = 0)
If Clausus Clapeyron fals ( ) dp dt pb =...Thermodynamcs l T (v 2 v 1 ) = 0/0 Second order phase transton ( S, v = 0) ( ) dp = c P,1 c P,2 dt Tv(β 1 β 2 ) Two phases ntermngled Ferromagnet (Excess spn-up
More informationMA 323 Geometric Modelling Course Notes: Day 13 Bezier Curves & Bernstein Polynomials
MA 323 Geometrc Modellng Course Notes: Day 13 Bezer Curves & Bernsten Polynomals Davd L. Fnn Over the past few days, we have looked at de Casteljau s algorthm for generatng a polynomal curve, and we have
More information( ) 1/ 2. ( P SO2 )( P O2 ) 1/ 2.
Chemstry 360 Dr. Jean M. Standard Problem Set 9 Solutons. The followng chemcal reacton converts sulfur doxde to sulfur troxde. SO ( g) + O ( g) SO 3 ( l). (a.) Wrte the expresson for K eq for ths reacton.
More informationThe Multiple Classical Linear Regression Model (CLRM): Specification and Assumptions. 1. Introduction
ECONOMICS 5* -- NOTE (Summary) ECON 5* -- NOTE The Multple Classcal Lnear Regresson Model (CLRM): Specfcaton and Assumptons. Introducton CLRM stands for the Classcal Lnear Regresson Model. The CLRM s also
More informationLecture 12: Discrete Laplacian
Lecture 12: Dscrete Laplacan Scrbe: Tanye Lu Our goal s to come up wth a dscrete verson of Laplacan operator for trangulated surfaces, so that we can use t n practce to solve related problems We are mostly
More informationProcess Modeling. Improving or understanding chemical process operation is a major objective for developing a dynamic process model
Process Modelng Improvng or understandng chemcal process operaton s a major objectve for developng a dynamc process model Balance equatons Steady-state balance equatons mass or energy mass or energy enterng
More informationNAME and Section No.
Chemstry 391 Fall 2007 Exam I KEY (Monday September 17) 1. (25 Ponts) ***Do 5 out of 6***(If 6 are done only the frst 5 wll be graded)*** a). Defne the terms: open system, closed system and solated system
More informationModule 3: Element Properties Lecture 1: Natural Coordinates
Module 3: Element Propertes Lecture : Natural Coordnates Natural coordnate system s bascally a local coordnate system whch allows the specfcaton of a pont wthn the element by a set of dmensonless numbers
More informationELASTIC WAVE PROPAGATION IN A CONTINUOUS MEDIUM
ELASTIC WAVE PROPAGATION IN A CONTINUOUS MEDIUM An elastc wave s a deformaton of the body that travels throughout the body n all drectons. We can examne the deformaton over a perod of tme by fxng our look
More informationChemical Equilibrium. Chapter 6 Spontaneity of Reactive Mixtures (gases) Taking into account there are many types of work that a sysem can perform
Ths chapter deals wth chemcal reactons (system) wth lttle or no consderaton on the surroundngs. Chemcal Equlbrum Chapter 6 Spontanety of eactve Mxtures (gases) eactants generatng products would proceed
More informationa for save as PDF Chemistry 163B Introduction to Multicomponent Systems and Partial Molar Quantities
a for save as PDF Chemstry 163B Introducton to Multcomponent Systems and Partal Molar Quanttes 1 the problem of partal mmolar quanttes mx: 10 moles ethanol C 2 H 5 OH (580 ml) wth 1 mole water H 2 O (18
More informationModule 3 LOSSY IMAGE COMPRESSION SYSTEMS. Version 2 ECE IIT, Kharagpur
Module 3 LOSSY IMAGE COMPRESSION SYSTEMS Verson ECE IIT, Kharagpur Lesson 6 Theory of Quantzaton Verson ECE IIT, Kharagpur Instructonal Objectves At the end of ths lesson, the students should be able to:
More informationOsmotic pressure and protein binding
Osmotc pressure and proten bndng Igor R. Kuznetsov, KochLab Symposum talk 5/15/09 Today we take a closer look at one of the soluton thermodynamcs key ponts from Steve s presentaton. Here t s: d[ln(k off
More informationLecture 8. Chapter 7. - Thermodynamic Web - Departure Functions - Review Equations of state (chapter 4, briefly)
Lecture 8 Chapter 5 - Thermodynamc Web - Departure Functons - Revew Equatons of state (chapter 4, brefly) Chapter 6 - Equlbrum (chemcal potental) * Pure Component * Mxtures Chapter 7 - Fugacty (chemcal
More informationGeneral Thermodynamics for Process Simulation. Dr. Jungho Cho, Professor Department of Chemical Engineering Dong Yang University
General Thermodynamcs for Process Smulaton Dr. Jungho Cho, Professor Department of Chemcal Engneerng Dong Yang Unversty Four Crtera for Equlbra μ = μ v Stuaton α T = T β α β P = P l μ = μ l1 l 2 Thermal
More informationVapor-Liquid Equilibria for Water+Hydrochloric Acid+Magnesium Chloride and Water+Hydrochloric Acid+Calcium Chloride Systems at Atmospheric Pressure
Chnese J. Chem. Eng., 4() 76 80 (006) RESEARCH OES Vapor-Lqud Equlbra for Water+Hydrochlorc Acd+Magnesum Chlorde and Water+Hydrochlorc Acd+Calcum Chlorde Systems at Atmospherc Pressure ZHAG Yng( 张颖 ) and
More informationInductance Calculation for Conductors of Arbitrary Shape
CRYO/02/028 Aprl 5, 2002 Inductance Calculaton for Conductors of Arbtrary Shape L. Bottura Dstrbuton: Internal Summary In ths note we descrbe a method for the numercal calculaton of nductances among conductors
More informationChapter 18, Part 1. Fundamentals of Atmospheric Modeling
Overhead Sldes for Chapter 18, Part 1 of Fundamentals of Atmospherc Modelng by Mark Z. Jacobson Department of Cvl & Envronmental Engneerng Stanford Unversty Stanford, CA 94305-4020 January 30, 2002 Types
More informationElectrical double layer: revisit based on boundary conditions
Electrcal double layer: revst based on boundary condtons Jong U. Km Department of Electrcal and Computer Engneerng, Texas A&M Unversty College Staton, TX 77843-318, USA Abstract The electrcal double layer
More informationNAME and Section No. it is found that 0.6 mol of O
NAME and Secton No. Chemstry 391 Fall 7 Exam III KEY 1. (3 Ponts) ***Do 5 out of 6***(If 6 are done only the frst 5 wll be graded)*** a). In the reacton 3O O3 t s found that.6 mol of O are consumed. Fnd
More informationNumerical Solution of Ordinary Differential Equations
Numercal Methods (CENG 00) CHAPTER-VI Numercal Soluton of Ordnar Dfferental Equatons 6 Introducton Dfferental equatons are equatons composed of an unknown functon and ts dervatves The followng are examples
More informationCHEMICAL ENGINEERING
Postal Correspondence GATE & PSUs -MT To Buy Postal Correspondence Packages call at 0-9990657855 1 TABLE OF CONTENT S. No. Ttle Page no. 1. Introducton 3 2. Dffuson 10 3. Dryng and Humdfcaton 24 4. Absorpton
More information3.1 Expectation of Functions of Several Random Variables. )' be a k-dimensional discrete or continuous random vector, with joint PMF p (, E X E X1 E X
Statstcs 1: Probablty Theory II 37 3 EPECTATION OF SEVERAL RANDOM VARIABLES As n Probablty Theory I, the nterest n most stuatons les not on the actual dstrbuton of a random vector, but rather on a number
More informationNumerical Heat and Mass Transfer
Master degree n Mechancal Engneerng Numercal Heat and Mass Transfer 06-Fnte-Dfference Method (One-dmensonal, steady state heat conducton) Fausto Arpno f.arpno@uncas.t Introducton Why we use models and
More informationEcon107 Applied Econometrics Topic 3: Classical Model (Studenmund, Chapter 4)
I. Classcal Assumptons Econ7 Appled Econometrcs Topc 3: Classcal Model (Studenmund, Chapter 4) We have defned OLS and studed some algebrac propertes of OLS. In ths topc we wll study statstcal propertes
More informationV T for n & P = constant
Pchem 365: hermodynamcs -SUMMARY- Uwe Burghaus, Fargo, 5 9 Mnmum requrements for underneath of your pllow. However, wrte your own summary! You need to know the story behnd the equatons : Pressure : olume
More informationThermodynamics. Section 4
Secton 4 Thermodynamcs Hendrck C. Van Ness, D.Eng., Howard. Isermann Department of Chemcal Engneerng, Rensselaer olytechnc Insttute; Fellow, Amercan Insttute of Chemcal Engneers; Member, Amercan Chemcal
More informationThe Geometry of Logit and Probit
The Geometry of Logt and Probt Ths short note s meant as a supplement to Chapters and 3 of Spatal Models of Parlamentary Votng and the notaton and reference to fgures n the text below s to those two chapters.
More information10.34 Numerical Methods Applied to Chemical Engineering Fall Homework #3: Systems of Nonlinear Equations and Optimization
10.34 Numercal Methods Appled to Chemcal Engneerng Fall 2015 Homework #3: Systems of Nonlnear Equatons and Optmzaton Problem 1 (30 ponts). A (homogeneous) azeotrope s a composton of a multcomponent mxture
More information9 Derivation of Rate Equations from Single-Cell Conductance (Hodgkin-Huxley-like) Equations
Physcs 171/271 - Chapter 9R -Davd Klenfeld - Fall 2005 9 Dervaton of Rate Equatons from Sngle-Cell Conductance (Hodgkn-Huxley-lke) Equatons We consder a network of many neurons, each of whch obeys a set
More informationThis column is a continuation of our previous column
Comparson of Goodness of Ft Statstcs for Lnear Regresson, Part II The authors contnue ther dscusson of the correlaton coeffcent n developng a calbraton for quanttatve analyss. Jerome Workman Jr. and Howard
More informationTHE SUMMATION NOTATION Ʃ
Sngle Subscrpt otaton THE SUMMATIO OTATIO Ʃ Most of the calculatons we perform n statstcs are repettve operatons on lsts of numbers. For example, we compute the sum of a set of numbers, or the sum of the
More informationLecture 7: Boltzmann distribution & Thermodynamics of mixing
Prof. Tbbtt Lecture 7 etworks & Gels Lecture 7: Boltzmann dstrbuton & Thermodynamcs of mxng 1 Suggested readng Prof. Mark W. Tbbtt ETH Zürch 13 März 018 Molecular Drvng Forces Dll and Bromberg: Chapters
More informationCOMPARISON OF SOME RELIABILITY CHARACTERISTICS BETWEEN REDUNDANT SYSTEMS REQUIRING SUPPORTING UNITS FOR THEIR OPERATIONS
Avalable onlne at http://sck.org J. Math. Comput. Sc. 3 (3), No., 6-3 ISSN: 97-537 COMPARISON OF SOME RELIABILITY CHARACTERISTICS BETWEEN REDUNDANT SYSTEMS REQUIRING SUPPORTING UNITS FOR THEIR OPERATIONS
More informationPETE 310 Lectures # 24 & 25 Chapter 12 Gas Liquid Equilibrium
ETE 30 Lectures # 24 & 25 Chapter 2 Gas Lqud Equlbrum Thermal Equlbrum Object A hgh T, Object B low T Intal contact tme Intermedate tme. Later tme Mechancal Equlbrum ressure essels Vale Closed Vale Open
More informationGasometric Determination of NaHCO 3 in a Mixture
60 50 40 0 0 5 15 25 35 40 Temperature ( o C) 9/28/16 Gasometrc Determnaton of NaHCO 3 n a Mxture apor Pressure (mm Hg) apor Pressure of Water 1 NaHCO 3 (s) + H + (aq) Na + (aq) + H 2 O (l) + CO 2 (g)
More informationPhase equilibria Introduction General equilibrium conditions
.5 hase equlbra.5. Introducton A gven amount of matter (usually called a system) can be characterzed by unform ntensve propertes n ts whole volume or only n some of ts parts; a porton of matter wth unform
More informationChapter 13: Multiple Regression
Chapter 13: Multple Regresson 13.1 Developng the multple-regresson Model The general model can be descrbed as: It smplfes for two ndependent varables: The sample ft parameter b 0, b 1, and b are used to
More informationMarginal Effects in Probit Models: Interpretation and Testing. 1. Interpreting Probit Coefficients
ECON 5 -- NOE 15 Margnal Effects n Probt Models: Interpretaton and estng hs note ntroduces you to the two types of margnal effects n probt models: margnal ndex effects, and margnal probablty effects. It
More informationLINEAR REGRESSION ANALYSIS. MODULE IX Lecture Multicollinearity
LINEAR REGRESSION ANALYSIS MODULE IX Lecture - 31 Multcollnearty Dr. Shalabh Department of Mathematcs and Statstcs Indan Insttute of Technology Kanpur 6. Rdge regresson The OLSE s the best lnear unbased
More informationModule 2. Random Processes. Version 2 ECE IIT, Kharagpur
Module Random Processes Lesson 6 Functons of Random Varables After readng ths lesson, ou wll learn about cdf of functon of a random varable. Formula for determnng the pdf of a random varable. Let, X be
More informationLectures - Week 4 Matrix norms, Conditioning, Vector Spaces, Linear Independence, Spanning sets and Basis, Null space and Range of a Matrix
Lectures - Week 4 Matrx norms, Condtonng, Vector Spaces, Lnear Independence, Spannng sets and Bass, Null space and Range of a Matrx Matrx Norms Now we turn to assocatng a number to each matrx. We could
More informationPart II Thermodynamic properties Properties of Pure Materials Chapter 8
Part II Thermodynamc propertes Propertes of Pure Materals Chapter 8 Prmary objectve s to evaluate changes n state n terms of prmtve and measurable propertes Secondary objectve s to connect molecular propertes
More informationMODELING THE HIGH-PRESSURE BEHAVIOR OF BINARY MIXTURES OF CARBON DIOXIDE+ALKANOLS USING AN EXCESS FREE ENERGY MIXING RULE
Brazlan Journal of Chemcal Engneerng ISSN 0104-6632 Prnted n Brazl Vol. 21, No. 04, pp. 659-666, October - December 04 MODELING THE HIGH-PRESSURE BEHAVIOR OF BINARY MIXTURES OF CARBON DIOXIDE+ALKANOLS
More informationAssignment 4. Adsorption Isotherms
Insttute of Process Engneerng Assgnment 4. Adsorpton Isotherms Part A: Compettve adsorpton of methane and ethane In large scale adsorpton processes, more than one compound from a mxture of gases get adsorbed,
More information1) Silicon oxide has a typical surface potential in an aqueous medium of ϕ,0
1) Slcon oxde has a typcal surface potental n an aqueous medum of ϕ, = 7 mv n 5 mm l at ph 9. Whch concentraton of catons do you roughly expect close to the surface? What s the average dstance between
More informationCSci 6974 and ECSE 6966 Math. Tech. for Vision, Graphics and Robotics Lecture 21, April 17, 2006 Estimating A Plane Homography
CSc 6974 and ECSE 6966 Math. Tech. for Vson, Graphcs and Robotcs Lecture 21, Aprl 17, 2006 Estmatng A Plane Homography Overvew We contnue wth a dscusson of the major ssues, usng estmaton of plane projectve
More informationComputation of Phase Equilibrium and Phase Envelopes
Downloaded from orbt.dtu.dk on: Sep 24, 2018 Computaton of Phase Equlbrum and Phase Envelopes Rtschel, Tobas Kasper Skovborg; Jørgensen, John Bagterp Publcaton date: 2017 Document Verson Publsher's PDF,
More informationDifference Equations
Dfference Equatons c Jan Vrbk 1 Bascs Suppose a sequence of numbers, say a 0,a 1,a,a 3,... s defned by a certan general relatonshp between, say, three consecutve values of the sequence, e.g. a + +3a +1
More informationj) = 1 (note sigma notation) ii. Continuous random variable (e.g. Normal distribution) 1. density function: f ( x) 0 and f ( x) dx = 1
Random varables Measure of central tendences and varablty (means and varances) Jont densty functons and ndependence Measures of assocaton (covarance and correlaton) Interestng result Condtonal dstrbutons
More informationLecture 3. Ax x i a i. i i
18.409 The Behavor of Algorthms n Practce 2/14/2 Lecturer: Dan Spelman Lecture 3 Scrbe: Arvnd Sankar 1 Largest sngular value In order to bound the condton number, we need an upper bound on the largest
More informationInner Product. Euclidean Space. Orthonormal Basis. Orthogonal
Inner Product Defnton 1 () A Eucldean space s a fnte-dmensonal vector space over the reals R, wth an nner product,. Defnton 2 (Inner Product) An nner product, on a real vector space X s a symmetrc, blnear,
More informationCHAPTER 5 NUMERICAL EVALUATION OF DYNAMIC RESPONSE
CHAPTER 5 NUMERICAL EVALUATION OF DYNAMIC RESPONSE Analytcal soluton s usually not possble when exctaton vares arbtrarly wth tme or f the system s nonlnear. Such problems can be solved by numercal tmesteppng
More informationis the calculated value of the dependent variable at point i. The best parameters have values that minimize the squares of the errors
Multple Lnear and Polynomal Regresson wth Statstcal Analyss Gven a set of data of measured (or observed) values of a dependent varable: y versus n ndependent varables x 1, x, x n, multple lnear regresson
More informationINTRODUCTION TO CHEMICAL PROCESS SIMULATORS
INTRODUCTION TO CHEMICAL PROCESS SIMULATORS DWSIM Chemcal Process Smulator A. Carrero, N. Qurante, J. Javaloyes October 2016 Introducton to Chemcal Process Smulators Contents Monday, October 3 rd 2016
More information( ) Phase equilibrium Some basic principles for phase calculations
Chapter From Fundamentals to Propertes 6 Table. Total propertes from an excess approach V U H A G S Pure component Real mxture Ideal mxture Mxng contrbuton Excess property = * v + 0 + * v v (.0) = * u
More informationMore metrics on cartesian products
More metrcs on cartesan products If (X, d ) are metrc spaces for 1 n, then n Secton II4 of the lecture notes we defned three metrcs on X whose underlyng topologes are the product topology The purpose of
More informationAdvanced Circuits Topics - Part 1 by Dr. Colton (Fall 2017)
Advanced rcuts Topcs - Part by Dr. olton (Fall 07) Part : Some thngs you should already know from Physcs 0 and 45 These are all thngs that you should have learned n Physcs 0 and/or 45. Ths secton s organzed
More informationLinear Regression Analysis: Terminology and Notation
ECON 35* -- Secton : Basc Concepts of Regresson Analyss (Page ) Lnear Regresson Analyss: Termnology and Notaton Consder the generc verson of the smple (two-varable) lnear regresson model. It s represented
More informationx = , so that calculated
Stat 4, secton Sngle Factor ANOVA notes by Tm Plachowsk n chapter 8 we conducted hypothess tests n whch we compared a sngle sample s mean or proporton to some hypotheszed value Chapter 9 expanded ths to
More information#64. ΔS for Isothermal Mixing of Ideal Gases
#64 Carnot Heat Engne ΔS for Isothermal Mxng of Ideal Gases ds = S dt + S T V V S = P V T T V PV = nrt, P T ds = v T = nr V dv V nr V V = nrln V V = - nrln V V ΔS ΔS ΔS for Isothermal Mxng for Ideal Gases
More informationLaboratory 3: Method of Least Squares
Laboratory 3: Method of Least Squares Introducton Consder the graph of expermental data n Fgure 1. In ths experment x s the ndependent varable and y the dependent varable. Clearly they are correlated wth
More informationFormal solvers of the RT equation
Formal solvers of the RT equaton Formal RT solvers Runge- Kutta (reference solver) Pskunov N.: 979, Master Thess Long characterstcs (Feautrer scheme) Cannon C.J.: 970, ApJ 6, 55 Short characterstcs (Hermtan
More informationχ x B E (c) Figure 2.1.1: (a) a material particle in a body, (b) a place in space, (c) a configuration of the body
Secton.. Moton.. The Materal Body and Moton hyscal materals n the real world are modeled usng an abstract mathematcal entty called a body. Ths body conssts of an nfnte number of materal partcles. Shown
More informationCinChE Problem-Solving Strategy Chapter 4 Development of a Mathematical Model. formulation. procedure
nhe roblem-solvng Strategy hapter 4 Transformaton rocess onceptual Model formulaton procedure Mathematcal Model The mathematcal model s an abstracton that represents the engneerng phenomena occurrng n
More informationLecture 10 Support Vector Machines II
Lecture 10 Support Vector Machnes II 22 February 2016 Taylor B. Arnold Yale Statstcs STAT 365/665 1/28 Notes: Problem 3 s posted and due ths upcomng Frday There was an early bug n the fake-test data; fxed
More informationReview of Classical Thermodynamics
Revew of Classcal hermodynamcs Physcs 4362, Lecture #1, 2 Syllabus What s hermodynamcs? 1 [A law] s more mpressve the greater the smplcty of ts premses, the more dfferent are the knds of thngs t relates,
More informationImplicit Integration Henyey Method
Implct Integraton Henyey Method In realstc stellar evoluton codes nstead of a drect ntegraton usng for example the Runge-Kutta method one employs an teratve mplct technque. Ths s because the structure
More informationOutline. Unit Eight Calculations with Entropy. The Second Law. Second Law Notes. Uses of Entropy. Entropy is a Property.
Unt Eght Calculatons wth Entropy Mechancal Engneerng 370 Thermodynamcs Larry Caretto October 6, 010 Outlne Quz Seven Solutons Second law revew Goals for unt eght Usng entropy to calculate the maxmum work
More informationModule 3: The Whole-Process Perspective for Thermochemical Hydrogen
"Thermodynamc Analyss of Processes for Hydrogen Generaton by Decomposton of Water" by John P. O'Connell Department of Chemcal Engneerng Unversty of Vrgna Charlottesvlle, VA 2294-4741 A Set of Energy Educaton
More informationModule 1 : The equation of continuity. Lecture 1: Equation of Continuity
1 Module 1 : The equaton of contnuty Lecture 1: Equaton of Contnuty 2 Advanced Heat and Mass Transfer: Modules 1. THE EQUATION OF CONTINUITY : Lectures 1-6 () () () (v) (v) Overall Mass Balance Momentum
More informationPhysics 53. Rotational Motion 3. Sir, I have found you an argument, but I am not obliged to find you an understanding.
Physcs 53 Rotatonal Moton 3 Sr, I have found you an argument, but I am not oblged to fnd you an understandng. Samuel Johnson Angular momentum Wth respect to rotatonal moton of a body, moment of nerta plays
More informationUniversity of Washington Department of Chemistry Chemistry 452/456 Summer Quarter 2014
Lecture 12 7/25/14 ERD: 7.1-7.5 Devoe: 8.1.1-8.1.2, 8.2.1-8.2.3, 8.4.1-8.4.3 Unversty o Washngton Department o Chemstry Chemstry 452/456 Summer Quarter 2014 A. Free Energy and Changes n Composton: The
More informationWeek 9 Chapter 10 Section 1-5
Week 9 Chapter 10 Secton 1-5 Rotaton Rgd Object A rgd object s one that s nondeformable The relatve locatons of all partcles makng up the object reman constant All real objects are deformable to some extent,
More informationSingular Value Decomposition: Theory and Applications
Sngular Value Decomposton: Theory and Applcatons Danel Khashab Sprng 2015 Last Update: March 2, 2015 1 Introducton A = UDV where columns of U and V are orthonormal and matrx D s dagonal wth postve real
More information