CHEMICAL EQUILIBRIA BETWEEN THE ION EXCHANGER AND GAS PHASE. Vladimir Soldatov and Eugeny Kosandrovich
|
|
- Sharon Bradley
- 5 years ago
- Views:
Transcription
1 CHEMICAL EQUILIBRIA BETWEEN THE ION EXCHANGER AND GAS PHASE Vladmr Soldatov ad Eugey Kosadrovch Isttute of Physcal Orgac Chemstry Natoal Academy of Sceces of Belarus, 13, Surgaov St, Msk 2272, Rep. of Belarus. 1
2 Io exchage techologes for ar purfcato (fbrous o exchagers form of fabrcs) Purfcato of dustral ad agrcultural exhaust gases from ozable or catalytcally covertble mpurtes (. g. NH 3, HCl, SO 2, H 2 S, Cl 2, ) Removal malodorous substaces from breathg ar (orgac acds, ames, mercaptaes, ) Idvdual protecto meas for humas lugs ad sk Deep ar purfcato clea rooms producto of mcrochps, precse mechacs, optcs, pharmaceutcals 2
3 Examples of the sorpto processes systems GAS-ION EXCHANGER Sorpto of ammoa ad ames: R - H + + NH 3 R - NH 4 + Sorpto of acds: R + OH - + HCl R + Cl - +H 2 O R + Cl - + HCl R + Cl -. HCl Sorpto of ahydrdes: R + OH - +SO 2 R + 2SO H 2 O R + 2SO SO 2 +H 2 O 2R + HSO 3-2R 2+ SO O 2 2R 2+ SO 4 2-2R + OH - + CO 2 R 2+ CO H 2 O Sorpto of eutral substaces : R + OH - + Cl 2 2RCl + H 2 +O 3
4 Features of practcally mportat gaseous processes wth o exchagers Extremely short cotact tme of the o exchager ad ar reurg applcato of the sorbets form of fbers (fractos of secod) Low cocetrato of the sorbate the ar ( 2 mg/m 3 ) Pletful sde processes (oxdato the sorbate by atmospherc O 2, sorpto competto wth CO 2, ) Extremely strog effect of the ar relatve humdty o the sorpto ad catalyss 4
5 The am of theory To calculate sorpto of a oc sorbate, exemplfed by NH 3, by the o exchager from ts cocetrato ad relatve water humdty the gas phase o the bass of the followg formato: 1. Exchage capacty of the o exchager. 2. The acdty parameters of fuctoal groups pk ad Δpk (obtaable from the ttrato curves) 3. Parameters of sopestc curve W=W(P/P ) (obtaable from the sopestc data) 4. Costat of o exchage eulbrum K + -NH 4 + (obtaable from the expermet o the o dstrbuto) 5
6 Assumptos: 1. The water o exchager presets two forms free water ad hydrate water. 2. Molecular NH 3 s dssolved oly the free water. 3. Dstrbuto of NH 3 betwee gas phase ad the free water s cotrolled by the Hery s law wth the same dstrbuto costat as that for the lud water. 4. Dstrbuto of NH 4+ betwee the teral soluto ad the sorpto ceters of o exchager s cotrolled by o exchage reacto RH + NH 4+ RNH 4 +H + descrbed by the eulbrum coeffcet k = [RNH 4 ][H + ]/([RH] [NH 4+ ]) k = k(x) 6
7 Scheme of theoretcal calculato of ammoa sorpto Ar cotag NH 3 ad H 2 O + o exchager H + - form sorpto of water, formato of hydrates dssoluto of ammoa the free water, ammoa dssocato teracto of ammoa wth the fuctoal group 7
8 8 Evaluato of the amout of hydrato water the o exchager R h K α =,, + = = h h K K α α 1, - umber of water molecules hydrate α relatve humdty P/P h ad R - umber of moles of the hydrate ad o-hydrated groups RH + H 2 O RH H 2 O
9 Evaluato of the amout of the free water We assume the frst approxmato the o exchager s a deal mxture of hydrates ad free water molecules ad the system obeys the Rault s law the followg form: α = X W f W /(X W f W ) where X W, X W ad f W, f W are respectvely mole fractos ad the ratoal actvty coeffcets of free water a partally ad completely swolle o exchager (α = 1): X W = W / ( W +1), X W = W / ( W +1) W s a umber of free water moles per mole of the fuctoal groups 9
10 1 Evaluato of the amout of the free water Choosg the referece state as f W = 1 at X W = X W we obta for the amout of free water the o exchager: f W = α m m s emprcal costat, usually eual to. ) ( - 1 ) (,,, + = h h W h W W W f W α α
11 11 A ew euato for the sopestc curve W(α) = + W h, ) ( - 1 ) ( 1,,, = h h W h W W f W K K W α α α α
12 Comparso of expermetal data for Dowex-5x8 from Boyd ad Soldao wth theoretcal calculato (les) mol H2O/e H L + 12,,2,4,6,8 1, 12,,2,4,6,8 1, 8 Na + 8 K + 4 4,,2,4,6,8 1,,,2,4,6,8 1, 12
13 1-sopestc curve of sulfoc o exchagers FIBAN K- 1, H form. The les are calculated from the ew euato wth the followg parameters: = 2; K= 5; m =. -hydrato water; 3 free water 18 mol H2O/e a 4 2 α,,2,4,6,8 1, 13
14 1-sopestc curve of carboxylc o exchagers FIBAN K-4. The les are calculated from the ew euato wth the followg parameters: = ½; K=5; m= hydrato water; 3 free water 4 mol H2O/e b 1 α,,2,4,6,8 1, 14
15 Molecular ammoa dstrbuto betwee the o exchager ad gas phase We assume that ammoa gas ca dssolve oly free water the phase of o exchager. I ths assumpto Hery s law s formulated as: [NH 3 ] R = K H [NH 3 ] G X W where [NH 3 ] R s cocetrato of NH 3 the teral soluto, the mole fracto of free water X W s calculated from sopestc data. Costat K H s assumed to be the same as that for eulbrum of aueous soluto of ammoa wth water soluto. It was calculated from the lterature data ad eualed x 1-5 (mol/kg)/ (mg/m 3 ). 15
16 Determato of amout of RNH 4 + as a fucto of x I order to compute amout of NH 3 boud to the fuctoal groups we apply euato of hydrolyss: RNH 4 + H 2 O RH + NH 4+ + OH - k hyd (x) = (1-X) [NH 4+ ] [OH - ]/(X [H 2 O]), X = [RNH 4 ]/E, (1-X) = [RH]/E E exchage capacty k hyd (x) depeds o degree of hydrolyss (1-X). It ca be determed va the acdty parameters of o exchager obtaable from the ttrato curve 16
17 The acdty parameters Ttrato of the H form of o exchager wth alkal KtOH s descrbed as eulbra RH + Kt + RKt + H + H + + OH - H 2 O Depedece of the eulbrum coeffcet of o exchage Kt + -H + k(x) = X [H +] / ((1 X)[Kt + ]) wth suffcet accuracy ca be descrbed as pk = pk + Δpk (X-1/2) K s the thermodyamc costat of o exchage depedet of the degree of o exchage, Δpk s a costat eual to pk x=1 pk x=. I our works we use K + as a stadard cato for determato of the acdty parameters, Kt + = K +. 17
18 Coecto betwee the acdty parameters ad RNH 4 hydrolyss eulbrum coeffcet Value pk for the case Kt + = NH 4+ s obtaable from drect expermet or from combg costats of o exchage eulbra H + -K + ad K + -NH 4 + Combe euatos of eulbrum coeffcets of hydrolyss ad o exchage wth those for water ad ammoa dssocato, K D : pk hyd = 14 - pk - Δpk (X RNH4+ -1/2) X RNH4+ = [NH 3 ] R K D / (k hyd + K D [NH 3 ] R ) Apply the modfed Hery s euato: X RNH4+ = K H X W [NH 3 ] G K D / (k hyd + K D K H X W [NH 3 ] G ) 18
19 The formato eeded for aprorstc calculato of the ozable sorbate sorpto by the o exchager 1. Dssocato costat of the sorbate 2. The acdty parameters of o exchager (obtaable from the ttrato curves) 3. Isopestc curve of the o exchager the acd form 4. The costat of Hery s law. 19
20 The pathway for a aprorstc calculato of ammoa sorpto by the o exchager 1. Fttg the sopestc data for the o exchage H form to the theoretcal euato ad fdg parameters, K ad m. 2. Fdg the mole fracto of free water the o exchager, X W,at a gve relatve humdty α. 3. Calculato of the relatve mole fracto of ammoa the o exchager usg the fal euato. 2
21 Breakthrough ad sorpto curves of NH 3 (18 mg/m 3 ) for carboxylc acd fbrous o exchager FIBAN K-4 at dfferet relatve humdty of the ar passg through the layer of o exchage fabrc 6 mm thck wth lear flow rate.8 m/s 1 35%; 2 42%; 3 47%; 4 52%; 5 56%; 6 68%; 7 8%; 8 94%. 21
22 Breakthrough ad sorpto curves of NH 3 (18 mg/m 3 ) for sulfoc acd fbrous o exchager FIBAN K-1 at dfferet relatve humdty of the ar passg through the layer of o exchage fabrc 3 mm thck wth lear flow rate.8 m/s α = 7,5%, 15,5%; 51%, 85%. 22
23 4 4 b Е, m-e/g K-4 K C (NH 3 ), mg/m 3 Ammoa sorpto o o exchagers FIBAN K-1 ad K- 4 depedece cocetrato of ammoa at α =.48 for K-1 ad α =.55 for K-4. Symbols expermetal data, the curves are computed 23
24 Е, m-e/g a 1 K-4 1 K-1,,2,4,6,8 1, α Ammoa sorpto o o exchagers FIBAN K-1 ad K- 4 depedece of relatve ar humdty at [NH3] G =18 mg/m 3. Symbols expermetal data, the curves are computed. 24
We have already referred to a certain reaction, which takes place at high temperature after rich combustion.
ME 41 Day 13 Topcs Chemcal Equlbrum - Theory Chemcal Equlbrum Example #1 Equlbrum Costats Chemcal Equlbrum Example #2 Chemcal Equlbrum of Hot Bured Gas 1. Chemcal Equlbrum We have already referred to a
More informationGeneral Method for Calculating Chemical Equilibrium Composition
AE 6766/Setzma Sprg 004 Geeral Metod for Calculatg Cemcal Equlbrum Composto For gve tal codtos (e.g., for gve reactats, coose te speces to be cluded te products. As a example, for combusto of ydroge wt
More informationApplying the condition for equilibrium to this equilibrium, we get (1) n i i =, r G and 5 i
CHEMICAL EQUILIBRIA The Thermodyamc Equlbrum Costat Cosder a reversble reacto of the type 1 A 1 + 2 A 2 + W m A m + m+1 A m+1 + Assgg postve values to the stochometrc coeffcets o the rght had sde ad egatve
More informationF A. Review1 7/1/2014. How to prepare for exams. Chapter 10 - GASES PRESSURE IS THE FORCE ACTING ON AN OBJECT PER UNIT AREA MEASUREMENT OF PRESSURE
How to prepare for exams 1. Uderstad EXAMLES chapter(s). Work RACICE EXERCISES 3. Work oe problem from each class of problems at ed of chapter 4. Aswer as may questos as tme permts from text web: www.prehall.com/brow
More informationLecture 7. Confidence Intervals and Hypothesis Tests in the Simple CLR Model
Lecture 7. Cofdece Itervals ad Hypothess Tests the Smple CLR Model I lecture 6 we troduced the Classcal Lear Regresso (CLR) model that s the radom expermet of whch the data Y,,, K, are the outcomes. The
More informationSTATISTICAL PROPERTIES OF LEAST SQUARES ESTIMATORS. x, where. = y - ˆ " 1
STATISTICAL PROPERTIES OF LEAST SQUARES ESTIMATORS Recall Assumpto E(Y x) η 0 + η x (lear codtoal mea fucto) Data (x, y ), (x 2, y 2 ),, (x, y ) Least squares estmator ˆ E (Y x) ˆ " 0 + ˆ " x, where ˆ
More informationPREDICTION OF VAPOR-LIQUID EQUILIBRIA OF BINARY MIXTURES USING QUANTUM CALCULATIONS AND ACTIVITY COEFFICIENT MODELS
Joural of Chemstry, Vol. 47 (5), P. 547-55, 9 PREDICTIO OF VAPOR-LIQUID EQUILIBRIA OF BIARY MIXTURES USIG QUATUM CALCULATIOS AD ACTIVITY COEFFICIET MODELS Receved May 8 PHAM VA TAT Departmet of Chemstry,
More informationParameter, Statistic and Random Samples
Parameter, Statstc ad Radom Samples A parameter s a umber that descrbes the populato. It s a fxed umber, but practce we do ot kow ts value. A statstc s a fucto of the sample data,.e., t s a quatty whose
More informationCubic Nonpolynomial Spline Approach to the Solution of a Second Order Two-Point Boundary Value Problem
Joural of Amerca Scece ;6( Cubc Nopolyomal Sple Approach to the Soluto of a Secod Order Two-Pot Boudary Value Problem W.K. Zahra, F.A. Abd El-Salam, A.A. El-Sabbagh ad Z.A. ZAk * Departmet of Egeerg athematcs
More informationFunctions of Random Variables
Fuctos of Radom Varables Chapter Fve Fuctos of Radom Varables 5. Itroducto A geeral egeerg aalyss model s show Fg. 5.. The model output (respose) cotas the performaces of a system or product, such as weght,
More informationQuantitative analysis requires : sound knowledge of chemistry : possibility of interferences WHY do we need to use STATISTICS in Anal. Chem.?
Ch 4. Statstcs 4.1 Quattatve aalyss requres : soud kowledge of chemstry : possblty of terfereces WHY do we eed to use STATISTICS Aal. Chem.? ucertaty ests. wll we accept ucertaty always? f ot, from how
More informationESS Line Fitting
ESS 5 014 17. Le Fttg A very commo problem data aalyss s lookg for relatoshpetwee dfferet parameters ad fttg les or surfaces to data. The smplest example s fttg a straght le ad we wll dscuss that here
More informationBayes Estimator for Exponential Distribution with Extension of Jeffery Prior Information
Malaysa Joural of Mathematcal Sceces (): 97- (9) Bayes Estmator for Expoetal Dstrbuto wth Exteso of Jeffery Pror Iformato Hadeel Salm Al-Kutub ad Noor Akma Ibrahm Isttute for Mathematcal Research, Uverst
More informationAnalysis of Lagrange Interpolation Formula
P IJISET - Iteratoal Joural of Iovatve Scece, Egeerg & Techology, Vol. Issue, December 4. www.jset.com ISS 348 7968 Aalyss of Lagrage Iterpolato Formula Vjay Dahya PDepartmet of MathematcsMaharaja Surajmal
More informationCarbonyl Groups. University of Chemical Technology, Beijing , PR China;
Electroc Supplemetary Materal (ESI) for Physcal Chemstry Chemcal Physcs Ths joural s The Ower Socetes 0 Supportg Iformato A Theoretcal Study of Structure-Solublty Correlatos of Carbo Doxde Polymers Cotag
More informationClass 13,14 June 17, 19, 2015
Class 3,4 Jue 7, 9, 05 Pla for Class3,4:. Samplg dstrbuto of sample mea. The Cetral Lmt Theorem (CLT). Cofdece terval for ukow mea.. Samplg Dstrbuto for Sample mea. Methods used are based o CLT ( Cetral
More informationChapter Business Statistics: A First Course Fifth Edition. Learning Objectives. Correlation vs. Regression. In this chapter, you learn:
Chapter 3 3- Busess Statstcs: A Frst Course Ffth Edto Chapter 2 Correlato ad Smple Lear Regresso Busess Statstcs: A Frst Course, 5e 29 Pretce-Hall, Ic. Chap 2- Learg Objectves I ths chapter, you lear:
More informationChemistry 163B Introduction to Multicomponent Systems and Partial Molar Quantities
Chemstry 163B Itroducto to Multcompoet Systems ad Partal Molar Quattes 1 the problem of partal mmolar quattes mx: 10 moles ethaol C H 5 OH (580 ml) wth 1 mole water H O (18 ml) get (580+18)=598 ml of soluto?
More informationbest estimate (mean) for X uncertainty or error in the measurement (systematic, random or statistical) best
Error Aalyss Preamble Wheever a measuremet s made, the result followg from that measuremet s always subject to ucertaty The ucertaty ca be reduced by makg several measuremets of the same quatty or by mprovg
More informationChapter 5 Properties of a Random Sample
Lecture 6 o BST 63: Statstcal Theory I Ku Zhag, /0/008 Revew for the prevous lecture Cocepts: t-dstrbuto, F-dstrbuto Theorems: Dstrbutos of sample mea ad sample varace, relatoshp betwee sample mea ad sample
More informationL5 Polynomial / Spline Curves
L5 Polyomal / Sple Curves Cotets Coc sectos Polyomal Curves Hermte Curves Bezer Curves B-Sples No-Uform Ratoal B-Sples (NURBS) Mapulato ad Represetato of Curves Types of Curve Equatos Implct: Descrbe a
More informationLecture 9: Tolerant Testing
Lecture 9: Tolerat Testg Dael Kae Scrbe: Sakeerth Rao Aprl 4, 07 Abstract I ths lecture we prove a quas lear lower boud o the umber of samples eeded to do tolerat testg for L dstace. Tolerat Testg We have
More informationStatistics of Random DNA
Statstcs of Radom DNA Aruma Ray Aaro Youg SUNY Geeseo Bomathematcs Group The Am To obta the epectato ad varaces for the ethalpy chage ΔH, etropy chage ΔS ad the free eergy chage ΔG for a radom -mer of
More informationEvaluating Polynomials
Uverst of Nebraska - Lcol DgtalCommos@Uverst of Nebraska - Lcol MAT Exam Expostor Papers Math the Mddle Isttute Partershp 7-7 Evaluatg Polomals Thomas J. Harrgto Uverst of Nebraska-Lcol Follow ths ad addtoal
More informationChemistry 163B Introduction to Multicomponent Systems and Partial Molar Quantities
Chemstry 163 Itroducto to Multcompoet Systems ad Partal Molar Quattes 1 the problem of partal mmolar quattes mx: 10 moles ethaol C H 5 OH (580 ml) wth 1 mole water H O (18 ml) get (580+18)=598 ml of soluto?
More informationA Helmholtz energy equation of state for calculating the thermodynamic properties of fluid mixtures
A Helmholtz eergy equato of state for calculatg the thermodyamc propertes of flud mxtures Erc W. Lemmo, Reer Tller-Roth Abstract New Approach based o hghly accurate EOS for the pure compoets combed at
More informationPROJECTION PROBLEM FOR REGULAR POLYGONS
Joural of Mathematcal Sceces: Advaces ad Applcatos Volume, Number, 008, Pages 95-50 PROJECTION PROBLEM FOR REGULAR POLYGONS College of Scece Bejg Forestry Uversty Bejg 0008 P. R. Cha e-mal: sl@bjfu.edu.c
More informationTESTS BASED ON MAXIMUM LIKELIHOOD
ESE 5 Toy E. Smth. The Basc Example. TESTS BASED ON MAXIMUM LIKELIHOOD To llustrate the propertes of maxmum lkelhood estmates ad tests, we cosder the smplest possble case of estmatg the mea of the ormal
More informationhp calculators HP 30S Statistics Averages and Standard Deviations Average and Standard Deviation Practice Finding Averages and Standard Deviations
HP 30S Statstcs Averages ad Stadard Devatos Average ad Stadard Devato Practce Fdg Averages ad Stadard Devatos HP 30S Statstcs Averages ad Stadard Devatos Average ad stadard devato The HP 30S provdes several
More informationC-1: Aerodynamics of Airfoils 1 C-2: Aerodynamics of Airfoils 2 C-3: Panel Methods C-4: Thin Airfoil Theory
ROAD MAP... AE301 Aerodyamcs I UNIT C: 2-D Arfols C-1: Aerodyamcs of Arfols 1 C-2: Aerodyamcs of Arfols 2 C-3: Pael Methods C-4: Th Arfol Theory AE301 Aerodyamcs I Ut C-3: Lst of Subects Problem Solutos?
More informationAssignment 5/MATH 247/Winter Due: Friday, February 19 in class (!) (answers will be posted right after class)
Assgmet 5/MATH 7/Wter 00 Due: Frday, February 9 class (!) (aswers wll be posted rght after class) As usual, there are peces of text, before the questos [], [], themselves. Recall: For the quadratc form
More informationLesson 3. Group and individual indexes. Design and Data Analysis in Psychology I English group (A) School of Psychology Dpt. Experimental Psychology
17/03/015 School of Psychology Dpt. Expermetal Psychology Desg ad Data Aalyss Psychology I Eglsh group (A) Salvador Chacó Moscoso Susaa Saduvete Chaves Mlagrosa Sáchez Martí Lesso 3 Group ad dvdual dexes
More informationEvaluation of uncertainty in measurements
Evaluato of ucertaty measuremets Laboratory of Physcs I Faculty of Physcs Warsaw Uversty of Techology Warszawa, 05 Itroducto The am of the measuremet s to determe the measured value. Thus, the measuremet
More informationSimple Linear Regression - Scalar Form
Smple Lear Regresso - Scalar Form Q.. Model Y X,..., p..a. Derve the ormal equatos that mmze Q. p..b. Solve for the ordary least squares estmators, p..c. Derve E, V, E, V, COV, p..d. Derve the mea ad varace
More informationMEASURES OF DISPERSION
MEASURES OF DISPERSION Measure of Cetral Tedecy: Measures of Cetral Tedecy ad Dsperso ) Mathematcal Average: a) Arthmetc mea (A.M.) b) Geometrc mea (G.M.) c) Harmoc mea (H.M.) ) Averages of Posto: a) Meda
More information: At least two means differ SST
Formula Card for Eam 3 STA33 ANOVA F-Test: Completely Radomzed Desg ( total umber of observatos, k = Number of treatmets,& T = total for treatmet ) Step : Epress the Clam Step : The ypotheses: :... 0 A
More informationEconometric Methods. Review of Estimation
Ecoometrc Methods Revew of Estmato Estmatg the populato mea Radom samplg Pot ad terval estmators Lear estmators Ubased estmators Lear Ubased Estmators (LUEs) Effcecy (mmum varace) ad Best Lear Ubased Estmators
More informationIntroduction to local (nonparametric) density estimation. methods
Itroducto to local (oparametrc) desty estmato methods A slecture by Yu Lu for ECE 66 Sprg 014 1. Itroducto Ths slecture troduces two local desty estmato methods whch are Parze desty estmato ad k-earest
More information{ }{ ( )} (, ) = ( ) ( ) ( ) Chapter 14 Exercises in Sampling Theory. Exercise 1 (Simple random sampling): Solution:
Chapter 4 Exercses Samplg Theory Exercse (Smple radom samplg: Let there be two correlated radom varables X ad A sample of sze s draw from a populato by smple radom samplg wthout replacemet The observed
More informationPermutation Tests for More Than Two Samples
Permutato Tests for ore Tha Two Samples Ferry Butar Butar, Ph.D. Abstract A F statstc s a classcal test for the aalyss of varace where the uderlyg dstrbuto s a ormal. For uspecfed dstrbutos, the permutato
More information18.657: Mathematics of Machine Learning
8.657: Mathematcs of Mache Learg Lecturer: Phlppe Rgollet Lecture 3 Scrbe: James Hrst Sep. 6, 205.5 Learg wth a fte dctoary Recall from the ed of last lecture our setup: We are workg wth a fte dctoary
More informationApplication of Calibration Approach for Regression Coefficient Estimation under Two-stage Sampling Design
Authors: Pradp Basak, Kaustav Adtya, Hukum Chadra ad U.C. Sud Applcato of Calbrato Approach for Regresso Coeffcet Estmato uder Two-stage Samplg Desg Pradp Basak, Kaustav Adtya, Hukum Chadra ad U.C. Sud
More informationAnalyzing Two-Dimensional Data. Analyzing Two-Dimensional Data
/7/06 Aalzg Two-Dmesoal Data The most commo aaltcal measuremets volve the determato of a ukow cocetrato based o the respose of a aaltcal procedure (usuall strumetal). Such a measuremet requres calbrato,
More informationNaïve Bayes MIT Course Notes Cynthia Rudin
Thaks to Şeyda Ertek Credt: Ng, Mtchell Naïve Bayes MIT 5.097 Course Notes Cytha Rud The Naïve Bayes algorthm comes from a geeratve model. There s a mportat dstcto betwee geeratve ad dscrmatve models.
More informationUNIVERSITY OF OSLO DEPARTMENT OF ECONOMICS
UNIVERSITY OF OSLO DEPARTMENT OF ECONOMICS Postpoed exam: ECON430 Statstcs Date of exam: Jauary 0, 0 Tme for exam: 09:00 a.m. :00 oo The problem set covers 5 pages Resources allowed: All wrtte ad prted
More informationSummary of the lecture in Biostatistics
Summary of the lecture Bostatstcs Probablty Desty Fucto For a cotuos radom varable, a probablty desty fucto s a fucto such that: 0 dx a b) b a dx A probablty desty fucto provdes a smple descrpto of the
More informationLecture 8: Linear Regression
Lecture 8: Lear egresso May 4, GENOME 56, Sprg Goals Develop basc cocepts of lear regresso from a probablstc framework Estmatg parameters ad hypothess testg wth lear models Lear regresso Su I Lee, CSE
More informationMass Transport. Laboratory 3
Laboratory 3 HWR 431/531 3-1 Itroducto: I the prevous laboratory we derved a expresso descrbg the flow of groudwater through a porous medum kow as Darcy's Law: Q = dh KA dl where Q = volumetrc dscharge
More informationThe Mathematical Appendix
The Mathematcal Appedx Defto A: If ( Λ, Ω, where ( λ λ λ whch the probablty dstrbutos,,..., Defto A. uppose that ( Λ,,..., s a expermet type, the σ-algebra o λ λ λ are defed s deoted by ( (,,...,, σ Ω.
More informationModule 7: Probability and Statistics
Lecture 4: Goodess of ft tests. Itroducto Module 7: Probablty ad Statstcs I the prevous two lectures, the cocepts, steps ad applcatos of Hypotheses testg were dscussed. Hypotheses testg may be used to
More information[ L] υ = (3) [ L] n. Q: What are the units of K in Eq. (3)? (Why is units placed in quotations.) What is the relationship to K in Eq. (1)?
Chem 78 Spr. M. Wes Bdg Polyomals Bdg Polyomals We ve looked at three cases of lgad bdg so far: The sgle set of depedet stes (ss[]s [ ] [ ] Multple sets of depedet stes (ms[]s, or m[]ss All or oe, or two-state
More informationENGI 4421 Propagation of Error Page 8-01
ENGI 441 Propagato of Error Page 8-01 Propagato of Error [Navd Chapter 3; ot Devore] Ay realstc measuremet procedure cotas error. Ay calculatos based o that measuremet wll therefore also cota a error.
More informationEstimation of Stress- Strength Reliability model using finite mixture of exponential distributions
Iteratoal Joural of Computatoal Egeerg Research Vol, 0 Issue, Estmato of Stress- Stregth Relablty model usg fte mxture of expoetal dstrbutos K.Sadhya, T.S.Umamaheswar Departmet of Mathematcs, Lal Bhadur
More informationChapter 8. Inferences about More Than Two Population Central Values
Chapter 8. Ifereces about More Tha Two Populato Cetral Values Case tudy: Effect of Tmg of the Treatmet of Port-We tas wth Lasers ) To vestgate whether treatmet at a youg age would yeld better results tha
More informationSolving Constrained Flow-Shop Scheduling. Problems with Three Machines
It J Cotemp Math Sceces, Vol 5, 2010, o 19, 921-929 Solvg Costraed Flow-Shop Schedulg Problems wth Three Maches P Pada ad P Rajedra Departmet of Mathematcs, School of Advaced Sceces, VIT Uversty, Vellore-632
More informationUNIVERSITY OF OSLO DEPARTMENT OF ECONOMICS
UNIVERSITY OF OSLO DEPARTMENT OF ECONOMICS Exam: ECON430 Statstcs Date of exam: Frday, December 8, 07 Grades are gve: Jauary 4, 08 Tme for exam: 0900 am 00 oo The problem set covers 5 pages Resources allowed:
More informationCHAPTER VI Statistical Analysis of Experimental Data
Chapter VI Statstcal Aalyss of Expermetal Data CHAPTER VI Statstcal Aalyss of Expermetal Data Measuremets do ot lead to a uque value. Ths s a result of the multtude of errors (maly radom errors) that ca
More informationX ε ) = 0, or equivalently, lim
Revew for the prevous lecture Cocepts: order statstcs Theorems: Dstrbutos of order statstcs Examples: How to get the dstrbuto of order statstcs Chapter 5 Propertes of a Radom Sample Secto 55 Covergece
More informationHomework Assignment Number Eight Solutions
D Keer MSE 0 Dept o Materals Scece & Egeerg Uversty o Teessee Kovlle Homework Assgmet Number Eght Solutos Problem Fd the soluto to the ollowg system o olear algebrac equatos ear () Soluto: s Sce ths s
More informationBayes (Naïve or not) Classifiers: Generative Approach
Logstc regresso Bayes (Naïve or ot) Classfers: Geeratve Approach What do we mea by Geeratve approach: Lear p(y), p(x y) ad the apply bayes rule to compute p(y x) for makg predctos Ths s essetally makg
More informationCH E 374 Computational Methods in Engineering Fall 2007
CH E 7 Computatoal Methods Egeerg Fall 007 Sample Soluto 5. The data o the varato of the rato of stagato pressure to statc pressure (r ) wth Mach umber ( M ) for the flow through a duct are as follows:
More informationENGI 3423 Simple Linear Regression Page 12-01
ENGI 343 mple Lear Regresso Page - mple Lear Regresso ometmes a expermet s set up where the expermeter has cotrol over the values of oe or more varables X ad measures the resultg values of aother varable
More information9 U-STATISTICS. Eh =(m!) 1 Eh(X (1),..., X (m ) ) i.i.d
9 U-STATISTICS Suppose,,..., are P P..d. wth CDF F. Our goal s to estmate the expectato t (P)=Eh(,,..., m ). Note that ths expectato requres more tha oe cotrast to E, E, or Eh( ). Oe example s E or P((,
More informationMATH 247/Winter Notes on the adjoint and on normal operators.
MATH 47/Wter 00 Notes o the adjot ad o ormal operators I these otes, V s a fte dmesoal er product space over, wth gve er * product uv, T, S, T, are lear operators o V U, W are subspaces of V Whe we say
More informationDensities and viscosities of aqueous ternary mixtures of 2-amino-2-methyl-1-propanol with ethylenediamine from to K
Destes ad vscostes of aqueous terary mxtures of 2-amo-2-methyl-1-propaol wth ethyleedame from 303.15 to 343.15 K Rega V. trolzo R&D Ceter for Membrae Techology ad Departmet of Chemcal geerg Chug Yua Chrsta
More informationSTA302/1001-Fall 2008 Midterm Test October 21, 2008
STA3/-Fall 8 Mdterm Test October, 8 Last Name: Frst Name: Studet Number: Erolled (Crcle oe) STA3 STA INSTRUCTIONS Tme allowed: hour 45 mutes Ads allowed: A o-programmable calculator A table of values from
More informationUNIT 4 CONTROL CHARTS FOR DEFECTS
UNIT 4 CONTROL CHARTS FOR DEFECTS Structure 4.1 Itroducto Objectves 4.2 Cotrol Charts for of Defects (c-chart) 4.3 Cotrol Charts for of Defects per Ut (u-chart) 4.4 Comparso betwee Cotrol Charts for Varables
More informationEcon 388 R. Butler 2016 rev Lecture 5 Multivariate 2 I. Partitioned Regression and Partial Regression Table 1: Projections everywhere
Eco 388 R. Butler 06 rev Lecture 5 Multvarate I. Parttoed Regresso ad Partal Regresso Table : Projectos everywhere P = ( ) ad M = I ( ) ad s a vector of oes assocated wth the costat term Sample Model Regresso
More informationarxiv:math/ v1 [math.gm] 8 Dec 2005
arxv:math/05272v [math.gm] 8 Dec 2005 A GENERALIZATION OF AN INEQUALITY FROM IMO 2005 NIKOLAI NIKOLOV The preset paper was spred by the thrd problem from the IMO 2005. A specal award was gve to Yure Boreko
More informationThe number of observed cases The number of parameters. ith case of the dichotomous dependent variable. the ith case of the jth parameter
LOGISTIC REGRESSION Notato Model Logstc regresso regresses a dchotomous depedet varable o a set of depedet varables. Several methods are mplemeted for selectg the depedet varables. The followg otato s
More informationThe E vs k diagrams are in general a function of the k -space direction in a crystal
vs dagram p m m he parameter s called the crystal mometum ad s a parameter that results from applyg Schrödger wave equato to a sgle-crystal lattce. lectros travelg dfferet drectos ecouter dfferet potetal
More informationChapter 4 (Part 1): Non-Parametric Classification (Sections ) Pattern Classification 4.3) Announcements
Aoucemets No-Parametrc Desty Estmato Techques HW assged Most of ths lecture was o the blacboard. These sldes cover the same materal as preseted DHS Bometrcs CSE 90-a Lecture 7 CSE90a Fall 06 CSE90a Fall
More information3. Basic Concepts: Consequences and Properties
: 3. Basc Cocepts: Cosequeces ad Propertes Markku Jutt Overvew More advaced cosequeces ad propertes of the basc cocepts troduced the prevous lecture are derved. Source The materal s maly based o Sectos.6.8
More informationHandout #8. X\Y f(x) 0 1/16 1/ / /16 3/ / /16 3/16 0 3/ /16 1/16 1/8 g(y) 1/16 1/4 3/8 1/4 1/16 1
Hadout #8 Ttle: Foudatos of Ecoometrcs Course: Eco 367 Fall/05 Istructor: Dr. I-Mg Chu Lear Regresso Model So far we have focused mostly o the study of a sgle radom varable, ts correspodg theoretcal dstrbuto,
More informationEVALUATION OF UNCERTAINITY IN MEASUREMENTS
Adrzej Kubaczyk Laboratory of Physcs I Faculty of Physcs Warsaw Uversty of Techology EVALUATION OF UNCERTAINITY IN MEASUREMENTS (Studet s gude to Physcs Laboratores) Chapter - Itroducto Iteratoal Stadard
More informationRademacher Complexity. Examples
Algorthmc Foudatos of Learg Lecture 3 Rademacher Complexty. Examples Lecturer: Patrck Rebesch Verso: October 16th 018 3.1 Itroducto I the last lecture we troduced the oto of Rademacher complexty ad showed
More informationHeat-Integrated Distillation Columns -Analysis and Modellingwith. Advanced Distillation as Supporting Subject
Norwega Uversty Of Scece ad Techology Faculty of Egeerg ad Techology Uversty of Belgrade Faculty of Mechacal Egeerg Isttute for Eergy Techology The log-term co-operatve project Master Degree Program: Sustaable
More informationSimple Linear Regression
Statstcal Methods I (EST 75) Page 139 Smple Lear Regresso Smple regresso applcatos are used to ft a model descrbg a lear relatoshp betwee two varables. The aspects of least squares regresso ad correlato
More informationMS exam problems Fall 2012
MS exam problems Fall 01 (From: Rya Mart) 1. (Stat 401) Cosder the followg game wth a box that cotas te balls two red, three blue, ad fve gree. A player selects two balls from the box at radom, wthout
More informationMu Sequences/Series Solutions National Convention 2014
Mu Sequeces/Seres Solutos Natoal Coveto 04 C 6 E A 6C A 6 B B 7 A D 7 D C 7 A B 8 A B 8 A C 8 E 4 B 9 B 4 E 9 B 4 C 9 E C 0 A A 0 D B 0 C C Usg basc propertes of arthmetc sequeces, we fd a ad bm m We eed
More informationConvergence of the Desroziers scheme and its relation to the lag innovation diagnostic
Covergece of the Desrozers scheme ad ts relato to the lag ovato dagostc chard Méard Evromet Caada, Ar Qualty esearch Dvso World Weather Ope Scece Coferece Motreal, August 9, 04 o t t O x x x y x y Oservato
More information9.1 Introduction to the probit and logit models
EC3000 Ecoometrcs Lecture 9 Probt & Logt Aalss 9. Itroducto to the probt ad logt models 9. The logt model 9.3 The probt model Appedx 9. Itroducto to the probt ad logt models These models are used regressos
More informationENGI 4421 Joint Probability Distributions Page Joint Probability Distributions [Navidi sections 2.5 and 2.6; Devore sections
ENGI 441 Jot Probablty Dstrbutos Page 7-01 Jot Probablty Dstrbutos [Navd sectos.5 ad.6; Devore sectos 5.1-5.] The jot probablty mass fucto of two dscrete radom quattes, s, P ad p x y x y The margal probablty
More informationRisk management of hazardous material transportation
Maagemet of atural Resources, Sustaable Developmet ad Ecologcal azards 393 Rs maagemet of hazardous materal trasportato J. Auguts, E. Uspuras & V. Matuzas Lthuaa Eergy Isttute, Lthuaa Abstract I recet
More informationMean is only appropriate for interval or ratio scales, not ordinal or nominal.
Mea Same as ordary average Sum all the data values ad dvde by the sample sze. x = ( x + x +... + x Usg summato otato, we wrte ths as x = x = x = = ) x Mea s oly approprate for terval or rato scales, ot
More informationLecture 12 APPROXIMATION OF FIRST ORDER DERIVATIVES
FDM: Appromato of Frst Order Dervatves Lecture APPROXIMATION OF FIRST ORDER DERIVATIVES. INTRODUCTION Covectve term coservato equatos volve frst order dervatves. The smplest possble approach for dscretzato
More informationResearch Journal of Chemical Sciences ISSN X Vol. 5(6), 64-72, June (2015)
Research Joural of Chemcal Sceces ISSN 3-606X Vol. 5(6), 64-7, Jue (05) Res. J. Chem. Sc. Vapor-Lqud Equlbrum Data Predcto by Advaced Group Cotrbuto Methods for a Bary System of Cyclopetyl methyl ether
More informationA New Measure of Probabilistic Entropy. and its Properties
Appled Mathematcal Sceces, Vol. 4, 200, o. 28, 387-394 A New Measure of Probablstc Etropy ad ts Propertes Rajeesh Kumar Departmet of Mathematcs Kurukshetra Uversty Kurukshetra, Ida rajeesh_kuk@redffmal.com
More informationPart 4b Asymptotic Results for MRR2 using PRESS. Recall that the PRESS statistic is a special type of cross validation procedure (see Allen (1971))
art 4b Asymptotc Results for MRR usg RESS Recall that the RESS statstc s a specal type of cross valdato procedure (see Alle (97)) partcular to the regresso problem ad volves fdg Y $,, the estmate at the
More informationPeriodic Table of Elements. EE105 - Spring 2007 Microelectronic Devices and Circuits. The Diamond Structure. Electronic Properties of Silicon
EE105 - Srg 007 Mcroelectroc Devces ad Crcuts Perodc Table of Elemets Lecture Semcoductor Bascs Electroc Proertes of Slco Slco s Grou IV (atomc umber 14) Atom electroc structure: 1s s 6 3s 3 Crystal electroc
More informationChapter Statistics Background of Regression Analysis
Chapter 06.0 Statstcs Backgroud of Regresso Aalyss After readg ths chapter, you should be able to:. revew the statstcs backgroud eeded for learg regresso, ad. kow a bref hstory of regresso. Revew of Statstcal
More information1. BLAST (Karlin Altschul) Statistics
Parwse seuece algmet global ad local Multple seuece algmet Substtuto matrces Database searchg global local BLAST Seuece statstcs Evolutoary tree recostructo Gee Fdg Prote structure predcto RNA structure
More information1 Onto functions and bijections Applications to Counting
1 Oto fuctos ad bectos Applcatos to Coutg Now we move o to a ew topc. Defto 1.1 (Surecto. A fucto f : A B s sad to be surectve or oto f for each b B there s some a A so that f(a B. What are examples of
More informationCollocation Extraction Using Square Mutual Information Approaches. Received December 2010; revised January 2011
Iteratoal Joural of Kowledge www.jklp.org ad Laguage Processg KLP Iteratoal c2011 ISSN 2191-2734 Volume 2, Number 1, Jauary 2011 pp. 53-58 Collocato Extracto Usg Square Mutual Iformato Approaches Huaru
More informationMeasures of Dispersion
Chapter 8 Measures of Dsperso Defto of Measures of Dsperso (page 31) A measure of dsperso s a descrptve summary measure that helps us characterze the data set terms of how vared the observatos are from
More informationLiquid-Liquid Equilibria for Ternary Mixtures of (Water + Carboxylic Acid+ MIBK), Experimental, Simulation, and Optimization
World Academy of Scece, Egeerg ad Techology teratoal Joural of Chemcal ad Molecular Egeerg Vol:7, No:6, 03 Lqud-Lqud Equlbra for Terary Mtures of (Water + Carboylc Acd+ MBK, Epermetal, Smulato, ad Optmzato
More informationHomework 1: Solutions Sid Banerjee Problem 1: (Practice with Asymptotic Notation) ORIE 4520: Stochastics at Scale Fall 2015
Fall 05 Homework : Solutos Problem : (Practce wth Asymptotc Notato) A essetal requremet for uderstadg scalg behavor s comfort wth asymptotc (or bg-o ) otato. I ths problem, you wll prove some basc facts
More informationIdea is to sample from a different distribution that picks points in important regions of the sample space. Want ( ) ( ) ( ) E f X = f x g x dx
Importace Samplg Used for a umber of purposes: Varace reducto Allows for dffcult dstrbutos to be sampled from. Sestvty aalyss Reusg samples to reduce computatoal burde. Idea s to sample from a dfferet
More information#A27 INTEGERS 13 (2013) SOME WEIGHTED SUMS OF PRODUCTS OF LUCAS SEQUENCES
#A27 INTEGERS 3 (203) SOME WEIGHTED SUMS OF PRODUCTS OF LUCAS SEQUENCES Emrah Kılıç Mathematcs Departmet, TOBB Uversty of Ecoomcs ad Techology, Akara, Turkey eklc@etu.edu.tr Neşe Ömür Mathematcs Departmet,
More informationComparison of Dual to Ratio-Cum-Product Estimators of Population Mean
Research Joural of Mathematcal ad Statstcal Sceces ISS 30 6047 Vol. 1(), 5-1, ovember (013) Res. J. Mathematcal ad Statstcal Sc. Comparso of Dual to Rato-Cum-Product Estmators of Populato Mea Abstract
More information