Encyclopedia of Electrochemistry, Electrochemical Engineering / Edition 1

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Overview

This 11-volume encyclopedia provides both an easy introduction to all topics related to modern electrochemistry as well as a comprehensive overview of the subject. Unrivalled in its breadth and depth, this standard reference has been created and written by renowned scientists, covering everything from fundamental research to areas of application.

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Product Details

  • ISBN-13: 9783527303977
  • Publisher: Wiley
  • Publication date: 3/30/2007
  • Series: Encyclopedia of Electrochemistry Series
  • Edition number: 1
  • Pages: 922
  • Product dimensions: 6.83 (w) x 9.80 (h) x 2.03 (d)

Meet the Author

Series Editors:
Prof. Allen J. Bard:
Born December 18, 1933, Prof. Bardreceived his early education in the public schools of New York Cityand attended The City College of NY (B.Sc.,1955). He did hisgraduate work at Harvard Univ. with J.J. Lingane (MA, 1956; PhD,1958) in electroanalytical chemistry. In 1958 he joined the facultyof The Univ.of Texas at Austin where he currently holds the NormanHackerman/Welch Regents' Chair in Chemistry. His research interestshave been in the application of electrochemical methods to thestudy of chemical problems and include investigations inelectro-organic chemistry, photoelectrochemistry, electrogeneratedchemiluminescence, and electroanalytical chemistry. He haspublished three books (Electrochemical Methods, with LarryFaulkner, Integrated Chemical Systems, and Chemical Equilibrium)and over 600 papers and chapters while editing the seriesElectroanalytical Chemistry (21 volumes) and the Encyclopedia ofthe Electrochemistry of the Elements (16 volumes) plus co-editingthe monograph, Standard Potentials in Aqueous Solution. He iscurrently editor-in-chief of the Journal of the American Chem.Society. The ISI listing of the "50 most cited chemists from1981-1997" ranks Prof. Bard at number 13 (taken from a total of627,871 chemists surveyed).

Prof. Martin Stratmann: Born 20 April 1954, studiedchemistry at the Ruhr Univ.Bochum and received his diploma in 1980.He finished his PhD in 1982 at the Max Planck Inst.(MPI)für Eisenforschung in Düorf. His professorship in physical chemistryfollowed in 1992 at the Univ.of Düorf with electrochemical studieson metal surfaces covered with ultrathin electrolyte layers. In1994 he took over the Chair in Corrosion Sci.and Surface Engin.atthe Univ.of Erlangen and since 2000 has been a scientific member ofthe MPI and director at the MPI für Eisenforschung Düorf, heading adepartment of interface chemistry and surface engineering. Hisresearch interests concentrate on corrosion relatedelectrochemistry, in particular with emphasis on microscopicaspects and in-situ spectroscopy, electrochemistry at buriedmetal/polymer interfaces - an area where he pioneered novelelectrochemical techniques - atmospheric corrosion, adhesion andsurface chemistry of reactive metal substrates. He has publishedmore than 150 papers and is co-editor of Steel Research andMaterials and Corrosion.

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Table of Contents

Preface.

1 Why Modelling?

1.1 Process and Process Modelling.

1.2 Observations on Some General Aspects of ModellingMethodology.

1.3 The Life-cycle of a Process and Modelling.

1.3.1 Modelling and Research and Development Stage.

1.3.2 Modelling and Conceptual Design Stage.

1.3.3 Modelling and Pilot Stage.

1.3.4 Modelling and Detailed Engineering Stage.

1.3.5 Modelling and Operating Stage.

1.4 Actual Objectives for Chemical Engineering Research.

1.5 Considerations About the Process Simulation.

1.5.1 The Simulation of a Physical Process and AnalogousComputers.

References.

2 On the Classification of Models.

2.1 Fields of Modelling and Simulation in ChemicalEngineering.

2.1.1 Steady-state Flowsheet Modelling and Simulation.

2.1.2 Unsteady-state Process Modelling and Simulation.

2.1.3 Molecular Modelling and Computational Chemistry.

2.1.4 Computational Fluid Dynamics.

2.1.5 Optimisation and Some Associated Algorithms andMethods.

2.1.6 Artificial Intelligence and Neural Networks.

2.1.7 Environment, Health, Safety and Quality Models.

2.1.8 Detailed Design Models and Programs.

2.1.9 Process Control.

2.1.10 Estimation of Parameters.

2.1.11 Experimental Design.

2.1.12 Process Integration.

2.1.13 Process Synthesis.

2.1.14 Data Reconciliation.

2.1.15 Mathematical Computing Software.

2.1.16 Chemometrics.

2.2 Some Observations on the Practical Use of Modelling andSimulation.

2.2.1 Reliability of Models and Simulations.

2.2.2 The Role of Industry as Final User of Modelling andSimulation.

2.2.3 Modelling and Simulation in Innovations.

2.2.4 Role of Modelling in Technology Transfer and KnowledgeManagement.

2.2.5 Role of the Universities in Modelling and SimulationDevelopment.

References.

3 Mathematical Modelling Based on TransportPhenomena.

3.1 Algorithm for the Development of a Mathematical Model of aProcess.

3.1.1 Some Observations about the Start of the Research.

3.1.2 The Limits of Modelling Based on Transport Phenomena.

3.2 An Example: From a Written Description to a Simulator.

3.3 Chemical Engineering Flow Models.

3.3.1 The Distribution Function and the Fundamental FlowModels.

3.3.2 Combined Flow Models.

3.3.3 The Slip Flow Effect on the Efficiency of a MechanicallyMixed Reactor in a Permanent Regime.

3.3.4 Dispersion Flow Model.

3.3.5 Examples.

3.3.5.1 Mechanically Mixed Reactor for Reactions in LiquidMedia.

3.3.5.2 Gas Flow in a Fluidized Bed Reactor.

3.3.5.3 Flow in a Fixed Bed Catalytic Reactor.

3.3.6 Flow Modelling using Computational Fluid Dynamics.

3.4 Complex Models and Their Simulators.

3.4.1 Problem of Heating in a Zone Refining Process.

3.4.2 Heat Transfer in a Composite Medium.

3.4.3 Fast Chemical Reaction Accompanied by Heat and MassTransfer.

3.5 Some Aspects of Parameters Identification in MathematicalModelling.

3.5.1 The Analytical Method for Identifying the Parameters of aModel.

3.5.1.1 The Pore Radius and Tortuosity of a Porous Membrane forGas Permeation.

3.5.2 The Method of Lagrange Multiplicators.

3.5.2.1 One Geometrical Problem.

3.5.3 The Use of Gradient Methods for the Identification ofParameters.

3.5.3.1 Identification of the Parameters of a Model by theSteepest Slope Method.

3.5.3.2 Identifying the Parameters of an Unsteady StatePerfectly Mixed Reactor.

3.5.4 The Gauss–Newton Gradient Technique.

3.5.4.1 The Identification of Thermal Parameters for the Case ofthe Cooling of a Cylindrical Body.

3.5.4.2 Complex Models with One Unknown Parameter.

3.5.5 Identification of the Parameters of a Model by the MaximumLikelihood Method.

3.5.5.1 The Kalman Filter Equations.

3.5.5.2 Example of the Use of the Kalman Filter.

3.6 Some Conclusions.

References.

4 Stochastic Mathematical Modelling.

4.1 Introduction to Stochastic Modelling.

4.1.1 Mechanical Stirring of a Liquid.

4.1.2 Numerical Application.

4.2 Stochastic Models by Probability Balance.

4.2.1 Solid Motion in a Liquid Fluidized Bed.

4.3 Mathematical Models of Continuous and DiscretePolystochastic Processes.

4.3.1 Polystochastic Chains and Their Models.

4.3.1.1 Random Chains and Systems with Complete Connections.

4.3.2 Continuous Polystochastic Process.

4.3.3 The Similarity between theFokker–Plank–Kolmogorov Equation and the PropertyTransport Equation.

4.3.3.1 Stochastic Differential Equation Systems for Heat andMass Molecular Transport.

4.4 Methods for Solving Stochastic Models.

4.4.1 The Resolution of Stochastic Models by Means of AsymptoticModels.

4.4.1.1 Stochastic Models Based on Asymptotic PolystochasticChains.

4.4.1.2 Stochastic Models Based on Asymptotic PolystochasticProcesses.

4.4.1.3 Asymptotic Models Derived from Stochastic Models withDifferential Equations.

4.4.2 Numerical Methods for Solving Stochastic Models.

4.4.3 The Solution of Stochastic Models with AnalyticalMethods.

4.5 Use of Stochastic Algorithms to Solve OptimizationProblems.

4.6 Stochastic Models for Chemical Engineering Processes.

4.6.1 Liquid and Gas Flow in a Column with a Mobile PackedBed.

4.6.1.1 Gas Hold-up in a MWPB.

4.6.1.2 Axial Mixing of Liquid in a MWPB.

4.6.1.3 The Gas Fraction in a Mobile Flooded Packed Bed.

4.6.2 Species Movement and Transfer in a Porous Medium.

4.6.2.1 Liquid Motion Inside a Porous Medium.

4.6.2.2 Molecular Species Transfer in a Porous Solid.

4.6.3 Stochastic Models for Processes with DiscreteDisplacement.

4.6.3.1 The Computation of the Temperature State of a HeatExchanger.

4.6.3.2 Cellular Stochastic Model for a Countercurrent Flow withRecycling.

References.

5 Statistical Models in Chemical Engineering.

5.1 Basic Statistical Modelling.

5.2 Characteristics of the Statistical Selection.

5.2.1 The Distribution of Frequently Used Random Variables.

5.2.2 Intervals and Limits of Confidence.

5.2.2.1 A Particular Application of the Confidence Interval to aMean Value.

5.2.2.2 An Actual Example of the Calculation of the ConfidenceInterval for the Variance.

5.2.3 Statistical Hypotheses and Their Checking.

5.3 Correlation Analysis.

5.4 Regression Analysis.

5.4.1 Linear Regression.

5.4.1.1 Application to the Relationship between the ReactantConversion and the Input Concentration for a CSR.

5.4.2 Parabolic Regression.

5.4.3 Transcendental Regression.

5.4.4 Multiple Linear Regression.

5.4.4.1 Multiple Linear Regressions in Matrix Forms.

5.4.5 Multiple Regression with Monomial Functions.

5.5 Experimental Design Methods.

5.5.1 Experimental Design with Two Levels (2k Plan).

5.5.2 Two-level Experiment Plan with Fractionary Reply.

5.5.3 Investigation of the Great Curvature Domain of theResponse Surface: Sequential Experimental Planning.

5.5.4 Second Order Orthogonal Plan.

5.5.4.1 Second Order Orthogonal Plan, Example of the Nitrationof an Aromatic Hydrocarbon.

5.5.5 Second Order Complete Plan.

5.5.6 Use of Simplex Regular Plan for Experimental Research.

5.5.6.1 SRP Investigation of a Liquid–Solid Extraction inBatch.

5.5.7 On-line Process Analysis by the EVOP Method.

5.5.7.1 EVOP Analysis of an Organic Synthesis.

5.5.7.2 Some Supplementary Observations.

5.6 Analysis of Variances and Interaction of Factors.

5.6.1 Analysis of the Variances for a Monofactor Process.

5.6.2 Analysis of the Variances for Two Factors Processes.

5.6.3 Interactions Between the Factors of a Process.

5.6.3.1 Interaction Analysis for a CFE 2n Plan.

5.6.3.2 Interaction Analysis Using a High Level FactorialPlan.

5.6.3.3 Analysis of the Effects of Systematic Influences.

5.7 Use of Neural Net Computing Statistical Modelling.

5.7.1 Short Review of Artificial Neural Networks.

5.7.2 Structure and Threshold Functions for Neural Networks.

5.7.3 Back-propagation Algorithm.

5.7.4 Application of ANNs in Chemical Engineering.

References.

6 Similitude, Dimensional Analysis and Modelling.

6.1 Dimensional Analysis in Chemical Engineering.

6.2 Vaschy–Buckingham Pi Theorem.

6.2.1 Determination of Pi Groups.

6.3 Chemical Engineering Problems Particularized by DimensionalAnalysis.

6.3.1 Dimensional Analysis for Mass Transfer by NaturalConvection in Finite Space.

6.3.2 Dimensional Analysis Applied to Mixing Liquids.

6.4 Supplementary Comments about Dimensional Analysis.

6.4.1 Selection of Variables.

6.4.1.1 Variables Imposed by the Geometry of the System.

6.4.1.2 Variables Imposed by the Properties of theMaterials.

6.4.1.3 Dynamic Internal Effects.

6.4.1.4 Dynamic External Effects.

6.5 Uniqueness of Pi Terms.

6.6 Identification of Pi Groups Using the Inspection Method.

6.7 Common Dimensionless Groups and Their Relationships.

6.7.1 Physical Significance of Dimensionless Groups.

6.6.2 The Dimensionless Relationship as Kinetic InterfaceProperty Transfer Relationship.

6.6.3 Physical Interpretation of the Nu, Pr, Sh and ScNumbers.

6.6.4 Dimensionless Groups for Interactive Processes.

6.6.5 Common Dimensionless Groups in Chemical Engineering.

6.7 Particularization of the Relationship of DimensionlessGroups Using Experimental Data.

6.7.1 One Dimensionless Group Problem.

6.7.2 Data Correlation for Problems with Two DimensionlessGroups.

6.7.3 Data Correlation for Problems with More than TwoDimensionless Groups.

6.8 Physical Models and Similitude.

6.8.1 The Basis of the Similitude Theory.

6.8.2 Design Aspects: Role of CSD in Compensating forSignificant Model Uncertainties.

6.8.2.1 Impact of Uncertainties and the Necessity for a ControlSystem Design.

6.9 Some Important Particularities of Chemical EngineeringLaboratory Models.

References.

Index.

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