Chemical Engineering: Modeling, Simulation and Similitude
Throughout, the book adopts a very practical approach, addressing actual problems and projects likely to be encountered by the reader, as well as fundamentals and solution strategies for complex problems. It is thus equally useful for student and professional engineers and chemists involved in industrial process and production plant design, construction or upgrading.
1 Why Modelling?
1.1 Process and Process Modelling.
1.2 Observations on Some General Aspects of Modelling Methodology.
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 Analogous Computers.
2 On the Classification of Models.
2.1 Fields of Modelling and Simulation in Chemical Engineering.
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 and Methods.
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.
2.2 Some Observations on the Practical Use of Modelling and Simulation.
2.2.1 Reliability of Models and Simulations.
2.2.2 The Role of Industry as Final User of Modelling and Simulation.
2.2.3 Modelling and Simulation in Innovations.
2.2.4 Role of Modelling in Technology Transfer and Knowledge Management.
2.2.5 Role of the Universities in Modelling and Simulation Development.
3 Mathematical Modelling Based on Transport Phenomena.
3.1 Algorithm for the Development of a Mathematical Model of a Process.
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 Flow Models.
3.3.2 Combined Flow Models.
3.3.3 The Slip Flow Effect on the Efficiency of a Mechanically Mixed Reactor in a Permanent Regime.
3.3.4 Dispersion Flow Model.
220.127.116.11 Mechanically Mixed Reactor for Reactions in Liquid Media.
18.104.22.168 Gas Flow in a Fluidized Bed Reactor.
22.214.171.124 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 Mass Transfer.
3.5 Some Aspects of Parameters Identification in Mathematical Modelling.
3.5.1 The Analytical Method for Identifying the Parameters of a Model.
126.96.36.199 The Pore Radius and Tortuosity of a Porous Membrane for Gas Permeation.
3.5.2 The Method of Lagrange Multiplicators.
188.8.131.52 One Geometrical Problem.
3.5.3 The Use of Gradient Methods for the Identification of Parameters.
184.108.40.206 Identification of the Parameters of a Model by the Steepest Slope Method.
220.127.116.11 Identifying the Parameters of an Unsteady State Perfectly Mixed Reactor.
3.5.4 The Gauss–Newton Gradient Technique.
18.104.22.168 The Identification of Thermal Parameters for the Case of the Cooling of a Cylindrical Body.
22.214.171.124 Complex Models with One Unknown Parameter.
3.5.5 Identification of the Parameters of a Model by the Maximum Likelihood Method.
126.96.36.199 The Kalman Filter Equations.
188.8.131.52 Example of the Use of the Kalman Filter.
3.6 Some Conclusions.
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 Discrete Polystochastic Processes.
4.3.1 Polystochastic Chains and Their Models.
184.108.40.206 Random Chains and Systems with Complete Connections.
4.3.2 Continuous Polystochastic Process.
4.3.3 The Similarity between the Fokker–Plank–Kolmogorov Equation and the Property Transport Equation.
220.127.116.11 Stochastic Differential Equation Systems for Heat and Mass Molecular Transport.
4.4 Methods for Solving Stochastic Models.
4.4.1 The Resolution of Stochastic Models by Means of Asymptotic Models.
18.104.22.168 Stochastic Models Based on Asymptotic Polystochastic Chains.
22.214.171.124 Stochastic Models Based on Asymptotic Polystochastic Processes.
126.96.36.199 Asymptotic Models Derived from Stochastic Models with Differential Equations.
4.4.2 Numerical Methods for Solving Stochastic Models.
4.4.3 The Solution of Stochastic Models with Analytical Methods.
4.5 Use of Stochastic Algorithms to Solve Optimization Problems.
4.6 Stochastic Models for Chemical Engineering Processes.
4.6.1 Liquid and Gas Flow in a Column with a Mobile Packed Bed.
188.8.131.52 Gas Hold-up in a MWPB.
184.108.40.206 Axial Mixing of Liquid in a MWPB.
220.127.116.11 The Gas Fraction in a Mobile Flooded Packed Bed.
4.6.2 Species Movement and Transfer in a Porous Medium.
18.104.22.168 Liquid Motion Inside a Porous Medium.
22.214.171.124 Molecular Species Transfer in a Porous Solid.
4.6.3 Stochastic Models for Processes with Discrete Displacement.
126.96.36.199 The Computation of the Temperature State of a Heat Exchanger.
188.8.131.52 Cellular Stochastic Model for a Countercurrent Flow with Recycling.
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.
184.108.40.206 A Particular Application of the Confidence Interval to a Mean Value.
220.127.116.11 An Actual Example of the Calculation of the Confidence Interval for the Variance.
5.2.3 Statistical Hypotheses and Their Checking.
5.3 Correlation Analysis.
5.4 Regression Analysis.
5.4.1 Linear Regression.
18.104.22.168 Application to the Relationship between the Reactant Conversion and the Input Concentration for a CSR.
5.4.2 Parabolic Regression.
5.4.3 Transcendental Regression.
5.4.4 Multiple Linear Regression.
22.214.171.124 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 the Response Surface: Sequential Experimental Planning.
5.5.4 Second Order Orthogonal Plan.
126.96.36.199 Second Order Orthogonal Plan, Example of the Nitration of an Aromatic Hydrocarbon.
5.5.5 Second Order Complete Plan.
5.5.6 Use of Simplex Regular Plan for Experimental Research.
188.8.131.52 SRP Investigation of a Liquid–Solid Extraction in Batch.
5.5.7 On-line Process Analysis by the EVOP Method.
184.108.40.206 EVOP Analysis of an Organic Synthesis.
220.127.116.11 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.
18.104.22.168 Interaction Analysis for a CFE 2n Plan.
22.214.171.124 Interaction Analysis Using a High Level Factorial Plan.
126.96.36.199 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.
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 Dimensional Analysis.
6.3.1 Dimensional Analysis for Mass Transfer by Natural Convection 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.
188.8.131.52 Variables Imposed by the Geometry of the System.
184.108.40.206 Variables Imposed by the Properties of the Materials.
220.127.116.11 Dynamic Internal Effects.
18.104.22.168 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 Interface Property Transfer Relationship.
6.6.3 Physical Interpretation of the Nu, Pr, Sh and Sc Numbers.
6.6.4 Dimensionless Groups for Interactive Processes.
6.6.5 Common Dimensionless Groups in Chemical Engineering.
6.7 Particularization of the Relationship of Dimensionless Groups Using Experimental Data.
6.7.1 One Dimensionless Group Problem.
6.7.2 Data Correlation for Problems with Two Dimensionless Groups.
6.7.3 Data Correlation for Problems with More than Two Dimensionless 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 for Significant Model Uncertainties.
22.214.171.124 Impact of Uncertainties and the Necessity for a Control System Design.
6.9 Some Important Particularities of Chemical Engineering Laboratory Models.
José Sanchez Marcano, born in 1958, graduated from Simon Bolivar University in 1980 in chemistry and process engineering, receiving his Doctorat d'Etat in 1987 from the University of Aix-Marseille III in the field of catalysis and petrochemistry. That same year he took up a position in the Department of Operations and Projects at PEQUIVEN, gaining a postdoctoral position two years later at IRC in Lyon where he worked on catalytic oxidation processes. In 1991 he moved to the CNRS at the Laboratory of process engineering and automatics, working on catalytic membrane reactors and modeling and subsequently in 1994 to the Institut Europ饮 des Membranes in Montpellier, France. Since 2002 he has been a research director and additionally leads the group on Membrane Process Engineering at IEM. His main research interest covers catalytic membrane reactors, gas separation, membrane contactors as well as modeling and simulation of membrane processes. Dr. Sanchez Marcano has more than 60 publications and four patents to his name.
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