Muutke küpsiste eelistusi

Process Modelling and Simulation in Chemical, Biochemical and Environmental Engineering [Pehme köide]

  • Formaat: Paperback / softback, 424 pages, kõrgus x laius: 234x156 mm, kaal: 453 g, 21 Tables, black and white; 85 Illustrations, black and white
  • Ilmumisaeg: 30-Mar-2017
  • Kirjastus: CRC Press
  • ISBN-10: 1138075086
  • ISBN-13: 9781138075085
Teised raamatud teemal:
  • Pehme köide
  • Hind: 70,08 €*
  • * hind on lõplik, st. muud allahindlused enam ei rakendu
  • Tavahind: 93,44 €
  • Säästad 25%
  • Raamatu kohalejõudmiseks kirjastusest kulub orienteeruvalt 3-4 nädalat
  • Kogus:
  • Lisa ostukorvi
  • Tasuta tarne
  • Tellimisaeg 2-4 nädalat
  • Lisa soovinimekirja
  • Raamatukogudele
Process Modelling and Simulation in Chemical, Biochemical and Environmental EngineeringSuurem pilt
  • Formaat: Paperback / softback, 424 pages, kõrgus x laius: 234x156 mm, kaal: 453 g, 21 Tables, black and white; 85 Illustrations, black and white
  • Ilmumisaeg: 30-Mar-2017
  • Kirjastus: CRC Press
  • ISBN-10: 1138075086
  • ISBN-13: 9781138075085
Teised raamatud teemal:
Teised raamatud sellest sooduspakkumisest: Kevadine sooduspakkumine - kuni 31. maini üle miljoni raamatu kuni 25% soodsamalt
Püsilink: https://www.kriso.ee/db/9781138075085.html

The use of simulation plays a vital part in developing an integrated approach to process design. By helping save time and money before the actual trial of a concept, this practice can assist with troubleshooting, design, control, revamping, and more. Process Modelling and Simulation in Chemical, Biochemical and Environmental Engineering explores effective modeling and simulation approaches for solving equations. Using a systematic treatment of model development and simulation studies for chemical, biochemical, and environmental processes, this book explains the simplification of a complicated process at various levels with the help of a "model sketch."

It introduces several types of models, examines how they are developed, and provides examples from a wide range of applications. This includes the simple models based on simple laws such as Fick’s law, models that consist of generalized equations such as equations of motion, discrete-event models and stochastic models (which consider at least one variable as a discrete variable), and models based on population balance.

Divided into 11 chapters, this book:

  • Presents a systematic approach of model development in view of the simulation need
  • Includes modeling techniques to model hydrodynamics, mass and heat transfer, and reactors for single as well as multi-phase systems
  • Provides stochastic and population balance models
  • Covers the application and development of artificial neural network models and hybrid ANN models
  • Highlights gradients based techniques as well as statistical techniques for model validation and sensitivity analysis
  • Contains examples on development of analytical, stochastic, numerical, and ANN-based models and simulation studies using them
  • Illustrates modeling concepts with a wide spectrum of classical as well as recent research papers

Process Modelling and Simulation in Chemical, Biochemical and Environmental Engineering includes recent trends in modeling and simulation, e.g. artificial neural network (ANN)-based models, and hybrid models. It contains a chapter on flowsheeting and batch processes using commercial/open source software for simulation.

Arvustused

"Overall, the material covered is good. The need for mathematical modeling and simulation, the basic steps involved in the development of mathematical modeling and simulation, and validation of the models are highlighted a systematic way. For large number of situations, the associated mathematical model equations with the relevant boundary conditions/initial conditions are given. The analytical solutions, wherever possible, are given. For situations requiring numerical solutions, MATLAB® programs are given. There is a good presentation of the subject materials. The book can be recommended for two courses: one at the undergraduate level (chapters 1 to 4 and 9), and one at the post-graduate level (chapters 5 to 11)." Dr. M. Chidambaram, Indian Institute of Technology Madras

"A thorough book on modeling and simulation for different engineering fields that is augmented by case studies from a wide range of applications." Jadran Vrabec, Mechanical Engineering, University of Paderborn, Germany

"The strength of the book is the diversity of topics covered starting with models based on simple laws and conservation laws illustrated for multiphase systems without and with reaction. The whole picture of process modeling and simulation ends with the last chapter on simulation of large plants." Alirio E. Rodrigues, The Faculdade de Engenharia da Universidade do Porto, Portugal

Preface xvii
Acknowledgements xxi
Author xxiii
Nomenclature xxv
1 Introduction to Modelling and Simulation 1(16)
1.1 Chemical Processes
1(5)
1.1.1 Unit Process: Fixed Bed
2(2)
1.1.2 Sulphuric Acid Plant
4(1)
1.1.3 Complex Nature of Chemical Processes
5(1)
1.2 What Is Simulation?
6(6)
1.2.1 Types of Simulation
7(1)
1.2.1.1 Steady-State Simulation
7(1)
1.2.1.2 Dynamic Simulation
7(1)
1.2.1.3 Stochastic Simulation (Monte Carlo Simulation)
7(1)
1.2.1.4 Discrete-Event Simulation
8(1)
1.2.1.5 Molecular Simulation
8(1)
1.2.2 Applications of Simulation in Chemical Engineering
8(4)
1.2.2.1 Process Synthesis
9(1)
1.2.2.2 Equipment Design
9(1)
1.2.2.3 Retrofitting
9(1)
1.2.2.4 Process Design
10(1)
1.2.2.5 Process Operation
10(1)
1.2.2.6 Process Control
11(1)
1.2.2.7 Process Safety
11(1)
1.2.2.8 Personnel Training
11(1)
1.3 Modelling
12(3)
1.3.1 What Is a Model?
12(2)
1.3.2 Role of Modelling in Simulation
14(1)
1.3.3 Limitations of Models
14(1)
1.4 Summary
15(1)
References
15(2)
2 An Overview of Modelling and Simulation 17(36)
2.1 Strategy for Simulation
18(7)
2.1.1 Problem Definition
18(1)
2.1.2 Understanding the Process
18(1)
2.1.3 Process Modelling
19(1)
2.1.4 Software Selection: Factors Affecting the Selection
20(4)
2.1.4.1 Availability
20(1)
2.1.4.2 Cost
20(1)
2.1.4.3 Trained Personnel
20(1)
2.1.4.4 Suitability
20(4)
2.1.5 Solution of Model Equations
24(1)
2.1.6 Model Validation
24(1)
2.1.7 Simulation Study
24(1)
2.2 Approaches for Model Development
25(2)
2.3 Types of Models
27(8)
2.3.1 Deterministic Models
28(1)
2.3.2 Lumped Parameter Models
28(1)
2.3.3 Distributed Parameter Models
29(2)
2.3.4 Steady-State Models
31(1)
2.3.5 Dynamic Models
31(1)
2.3.6 Stochastic Models
31(1)
2.3.7 Population Balance Models
32(1)
2.3.8 Agent-Based Models
32(1)
2.3.9 Discrete-Event Models
32(1)
2.3.10 Artificial Neural Network-Based Models
33(1)
2.3.11 Fuzzy Models
34(1)
2.4 Types of Equations in a Model and Solution Strategy
35(4)
2.4.1 Algebraic Equations
36(1)
2.4.2 Differential Equations
37(2)
2.4.3 Differential-Algebraic Equations
39(1)
2.5 Sources of Equations
39(3)
2.5.1 Empirical Equations
40(1)
2.5.2 Equations Based on Theoretical Concepts
40(1)
2.5.3 Consistency Equations
41(1)
2.5.4 Differential Equations Using Laws of Conservation
41(1)
2.5.5 Integration over Area, Volume and Time
42(1)
2.5.6 Population Balance
42(1)
2.6 Simplifying Concepts
42(8)
2.6.1 Continuum
43(1)
2.6.2 Combination of Simple and Rigorous Models
44(1)
2.6.3 Uniform Probability Distribution
45(1)
2.6.4 Parallel Mechanisms
45(1)
2.6.5 Analogy to Electrical Circuits
46(1)
2.6.6 Film Model
47(1)
2.6.7 Boundary Layer Approximation
48(1)
2.6.8 Order of Magnitude Approximation
49(1)
2.6.9 Quasi-Steady State
49(1)
2.6.10 Finite and Infinite Dimensions
50(1)
2.7 Summary
50(1)
References
51(2)
3 Models Based on Simple Laws 53(26)
3.1 Equation of State
53(3)
3.1.1 Ideal Gas Law
53(1)
3.1.2 Cubic Equations of State
54(2)
3.2 Henry's Law
56(1)
3.3 Newton's Law of Viscosity
57(2)
3.4 Fourier's Law of Heat Conduction
59(1)
3.5 Fick's First Law
60(1)
3.6 Fick's Second Law
61(1)
3.7 Film Model
62(3)
3.8 Two-Film Theory
65(3)
3.9 Arrhenius' Law
68(1)
3.10 Adsorption Isotherms
68(1)
3.11 Examples
69(7)
3.11.1 Overall Heat Transfer Coefficient in a Composite Cylindrical Wall
69(1)
3.11.2 Cooling of a Small Sphere in a Stagnant Fluid
70(1)
3.11.3 Diffusion in a Stagnant Gas Film
71(2)
3.11.4 Diffusion-Reaction Systems
73(2)
3.11.5 Gas-Liquid Solid-Catalysed Reactions
75(1)
3.12 Summary
76(1)
References
77(2)
4 Models Based on Laws of Conservation 79(50)
4.1 Laws of Conservation of Momentum, Mass and Energy
79(10)
4.1.1 Equation of Continuity
80(4)
4.1.2 Laws of Conservation of Momentum, Mass and Energy
84(2)
4.1.3 Boundary Conditions
86(3)
4.1.3.1 Boundary Conditions of the First Kind
86(2)
4.1.3.2 Boundary Conditions of the Second Kind
88(1)
4.1.3.3 Boundary Conditions of the Third Kind
88(1)
4.2 Laminar Flow
89(6)
4.2.1 Velocity Field in Laminar Flow
89(1)
4.2.2 Velocity Profile in Simple Geometries
89(3)
4.2.3 Convective Heat and Mass Transfer in Simple Geometries
92(3)
4.3 Boundary Layers: Momentum, Thermal and Diffusional
95(3)
4.4 Turbulence Models
98(6)
4.4.1 What Is Turbulence?
98(4)
4.4.2 Eddy Viscosity, Eddy Diffusivity and Eddy Thermal Conductivity
102(1)
4.4.3 Prandtl Mixing Length
103(1)
4.4.4 Turbulence Kinetic Energy and Length and Time Scale
104(1)
4.5 Surface Renewal Models at High Flux of Momentum, Mass or Heat
104(6)
4.5.1 Penetration Model (Higbie's Surface Renewal)
104(2)
4.5.2 Surface Renewal Model for Other Residence Time Distributions
106(1)
4.5.2.1 Danckwerts' Surface Renewal Model
106(1)
4.5.3 Surface Renewal Model with a Packet of Finite Length
106(3)
4.5.4 Coexistence of Surface Renewal and Film
109(1)
4.5.5 Surface Renewal with Laminar Flow in Eddies
109(1)
4.6 Analogy between Momentum, Mass and Heat Transfer
110(8)
4.6.1 Universal Velocity Profile
111(1)
4.6.2 The Reynolds Analogy
112(1)
4.6.3 Prandtl's Analogy
113(1)
4.6.4 Karman's Analogy
114(1)
4.6.5 Lin-Moultan-Putnam's Analogy Based on an Eddying Sub-Layer
115(1)
4.6.6 Chilton-Colburn Analogy: j Factors
116(1)
4.6.7 Heat and Mass Transfer Analogy in Bubble Columns
116(2)
4.6.8 Limitations of Analogies
118(1)
4.7 Simple Models for Reactors and Bioreactors
118(8)
4.7.1 Stirred-Tank Reactors
118(2)
4.7.2 Batch and Semi-Batch Reactors
120(1)
4.7.3 Axial Dispersion Model
121(2)
4.7.4 Tank in Series Model
123(1)
4.7.5 Modelling of Residence Time Distribution
124(2)
4.8 Summary
126(1)
References
127(2)
5 Multiphase Systems without Reaction 129(50)
5.1 Consideration of a Continuous-Phase Axial Solid Profile in a Slurry Bubble Column
130(3)
5.2 Single Interface: The Wetted Wall Column
133(4)
5.3 Stationary Dispersed-Phase Systems (Gas-Solid Systems)
137(6)
5.3.1 Heat Transfer in Packed Beds
137(4)
5.3.2 Mass Transfer in Packed Beds
141(2)
5.4 Moving Dispersed Systems (Gas-Solid Systems): Wall-to-Bed Heat Transfer in a Fluidised Bed
143(10)
5.4.1 Models Based on Film Theory
147(1)
5.4.2 Models Based on the Surface Renewal Concept
148(5)
5.5 Moving Dispersed Systems (Gas-Liquid Systems): Transfer Processes in Bubble Columns
153(5)
5.5.1 Models Based on the Boundary Layer Concept
153(1)
5.5.2 Models Based on the Surface Renewal Concept
154(2)
5.5.3 A Unified Approach Based on Liquid Circulation Velocity
156(1)
5.5.4 Modified Boundary-Layer Model
157(1)
5.5.5 Surface Renewal with an Adjacent Liquid Layer
157(1)
5.6 Regions of Interest Adjacent to the Interface
158(11)
5.6.1 Terminal Velocity of Bubbles
159(1)
5.6.1.1 Terminal Velocity in a Viscosity-Dominated Regime
159(1)
5.6.2 Gas-Liquid Mass Transfer Coefficient
160(2)
5.6.3 Mass Transfer over a Flat Plate: Boundary Layer
162(2)
5.6.4 Simultaneous Heat and Mass Transfer: Drying of Solids
164(2)
5.6.5 Membrane Processes: Model for Pervaporation
166(3)
5.7 More Than One Mechanism of Heat Transfer: Flat-Plate Solar Collector
169(4)
5.8 Introducing Other Effects in Laws of Conservation
173(2)
5.8.1 Reactions
173(1)
5.8.2 Electrokinetic Phenomena: Flow in Microchannels
174(1)
5.9 Summary
175(1)
References
176(3)
6 Multiphase Systems with Reaction 179(42)
6.1 Development of a Model for Multiphase Reactors: Common Assumptions and Methodology
180(5)
6.1.1 One-Dimensional and Two-Dimensional Models
180(1)
6.1.2 Homogeneous and Heterogeneous Models
181(1)
6.1.3 Two- and Three-Fluid Models
181(2)
6.1.4 Thermodynamics, Kinetics, Hydrodynamics and Reactor Model
183(1)
6.1.5 Methodology for Model Development for Multiphase Systems
184(1)
6.2 Packed Bed Reactors
185(11)
6.2.1 Isothermal Bioreactor
185(2)
6.2.2 The Solids as Reactant-Kinetic Models
187(3)
6.2.3 Catalytic Packed Bed Reactors: Reactor Models
190(9)
6.2.3.1 1D Pseudo-Homogeneous Model
191(1)
6.2.3.2 1D Heterogeneous Model
192(1)
6.2.3.3 2D Pseudo-Homogeneous Model
193(1)
6.2.3.4 2D Heterogeneous Model
194(1)
6.2.3.5 Unsteady-State or Dynamic Models
195(1)
6.3 Trickle Bed Reactors
196(3)
6.4 Slurry Reactors
199(6)
6.4.1 Mechanically Agitated Slurry Reactors
200(1)
6.4.2 Slurry Bubble Columns
201(4)
6.5 Fluidised Bed Reactors
205(14)
6.5.1 Minimum Fluidisation Velocity
205(1)
6.5.2 Bed Expansion
206(1)
6.5.3 Flow Regimes
207(1)
6.5.4 Two-Phase Fluidised Bed Reactors
208(2)
6.5.5 Fluidised Bed Bioreactors
210(1)
6.5.6 Two-Fluid Models
211(2)
6.5.7 Three-Phase Fluidised Bed Bioreactors
213(2)
6.5.8 Dynamic Model for Three-Phase Fluidised Bed Bioreactors
215(4)
6.6 Summary
219(1)
References
220(1)
7 Population Balance Models and Discrete-Event Models 221(26)
7.1 Stochastic Models
222(1)
7.2 The Complex Nature of the Dispersed Phase
223(3)
7.2.1 Size Variation of the Dispersed Phase
224(1)
7.2.2 Movement of the Dispersed Phase
225(1)
7.2.3 Discrete Nature of Time
225(1)
7.3 Population Balance Equation
226(2)
7.4 Probability Distribution Functions
228(3)
7.4.1 Normal or Gaussian Distribution
229(1)
7.4.2 Logarithmic Normal Distribution
229(1)
7.4.3 Poisson Distribution
229(1)
7.4.4 Gamma Distribution
230(1)
7.4.5 Beta Distribution
230(1)
7.4.6 Exponential Distribution
231(1)
7.5 Population Balance Models: Simulation Methodology
231(13)
7.5.1 Discrete Form of the Population Balance Equation
232(2)
7.5.2 Bubble Coalescence and Breakup
234(4)
7.5.2.1 Bubble Coalescence due to Turbulent Eddies
234(1)
7.5.2.2 Bubble Coalescence for Small Bubbles
235(1)
7.5.2.3 Bubble Coalescence due to the Relative Velocity of Bubbles
236(1)
7.5.2.4 Film Drainage and Bubble Coalescence
236(2)
7.5.3 Bubble Breakup
238(1)
7.5.4 Monte Carlo Simulation
239(1)
7.5.5 Stochastic Simulation
240(1)
7.5.6 Crystallisation
241(1)
7.5.7 Analytical Solution of Population Balance Models
242(1)
7.5.8 Polymerisation
243(1)
7.6 Summary
244(1)
References
244(3)
8 Artificial Neural Network-Based Models 247(30)
8.1 Artificial Neural Networks
247(4)
8.1.1 Information Processing through Neurons
248(2)
8.1.2 Radial Basis Function Networks
250(1)
8.2 Development of ANN-Based Models
251(9)
8.2.1 Architecture
251(1)
8.2.2 Identification of Inputs
252(2)
8.2.3 Choice of the Architecture
254(1)
8.2.4 Training the ANNs
255(2)
8.2.5 Performance of ANN Model
257(1)
8.2.6 Learning Methods
257(1)
8.2.7 Over-Fitting and Under-Fitting
258(2)
8.3 Applications of ANNs in Chemical Engineering
260(6)
8.3.1 ANN-Based Correlations
260(2)
8.3.2 Process Modelling and Monitoring
262(2)
8.3.3 Pattern Recognition
264(1)
8.3.4 Fault Diagnosis
264(1)
8.3.5 Process Control
265(1)
8.4 Advantages of ANN-Based Models
266(1)
8.5 Limitations of ANN-Based Models
267(1)
8.6 Hybrid Neural Networks
268(6)
8.6.1 Application of Hybrid Neural Networks
269(9)
8.6.1.1 A Hybrid ANN Model for a Bioreactor
270(1)
8.6.1.2 Other Applications
271(3)
8.7 Summary
274(1)
References
274(3)
9 Model Validation and Sensitivity Analysis 277(22)
9.1 Model Validation: Objective
278(2)
9.1.1 Model Output
278(1)
9.1.2 Assumptions
279(1)
9.1.3 Model Inputs
279(1)
9.2 Model Validation Methodology
280(5)
9.2.1 Validating Dynamic Models
283(1)
9.2.2 Statistical Analysis of the Model
284(1)
9.2.3 Analysis of Variance
285(1)
9.3 Sensitivity Analysis
285(4)
9.3.1 Direct Differential Method
285(4)
9.4 Global Sensitivity Measures
289(4)
9.4.1 Gradient-Based Global Sensitivity Measures
289(1)
9.4.2 Variance-Based Global Sensitivity Measures
289(3)
9.4.3 Determination of Variance-Based Sensitivity Indices
292(1)
9.4.3.1 Sobol's Method
292(1)
9.4.3.2 Fourier Amplitude Sensitivity Test
292(1)
9.5 Role of Sensitivity Analysis
293(4)
9.5.1 Process Design
293(1)
9.5.2 Process Operations
294(2)
9.5.3 Model Development
296(1)
9.6 Summary
297(1)
References
297(2)
10 Case Studies 299(40)
10.1 Axial Distribution of Solids in Slurry Bubble Columns: Analytical Deterministic Models
300(7)
10.1.1 Validation of the Model
304(2)
10.1.2 Simulation Studies
306(1)
10.2 Conversion for a Gas-Liquid Reaction in a Shallow Bed: A Numerical Model
307(13)
10.2.1 Size of the Bubble Formed at the Distributor Plate
311(1)
10.2.2 Hydrodynamic Model
312(2)
10.2.3 Mass Transfer and Reactor Models
314(1)
10.2.4 Model Validation
314(3)
10.2.5 Simulation Studies
317(3)
10.2.5.1 Effects of Column Diameters .
317(1)
10.2.5.2 Effect of Nozzle Diameter
317(1)
10.2.5.3 Effect of Viscosity on Gas Holdup
318(1)
10.2.5.4 Surface Tension on Gas Holdup
319(1)
10.3 Stochastic Model to Predict Wall-to-Bed Mass Transfer in Packed and Fluidised Beds
320(11)
10.3.1 Improvement in Mass Transfer due to Translation of Particles
321(2)
10.3.2 Model for Wall-to-Bed Mass Transfer in Packed and Fluidised Beds
323(1)
10.3.3 Relationship between Mass Transfer Coefficient and Improvement Coefficient
324(1)
10.3.4 Evaluation of IjF (Ij,b,t) and Kav
325(1)
10.3.5 Validation of the Model for Fluidised Beds
326(3)
10.3.5.1 Particle Velocities
327(1)
10.3.5.2 Distance of the Particle from the Wall
328(1)
10.3.5.3 Time Interval
328(1)
10.3.6 Algorithm for Estimation of Mass Transfer Coefficient
329(1)
10.3.7 Model Validation
329(2)
10.4 Artificial Neural Network Model: Heat Transfer in Bubble Columns
331(4)
10.4.1 Selection of Inputs
332(1)
10.4.2 Obtaining Experimental Data
332(1)
10.4.3 Architecture of ANNs
333(2)
10.5 Summary
335(1)
References
336(3)
11 Simulation of Large Plants 339(28)
11.1 Interconnecting Sub-Models
341(1)
11.2 Simulation Study
342(1)
11.3 Flowsheeting and Continuous Processes
343(14)
11.3.1 Data Input and Verification
343(5)
11.3.2 Thermodynamic Properties and Unit Operations Libraries
348(2)
11.3.3 Calculation Phase
350(4)
11.3.3.1 Sequential Approach
350(1)
11.3.3.2 Tearing of Streams
351(1)
11.3.3.3 Order of Calculation
351(2)
11.3.3.4 Simultaneous or Equation-Solving Approach
353(1)
11.3.4 Modular Approach
354(2)
11.3.4.1 Choosing Modules
354(1)
11.3.4.2 Sequential Modular Approach
355(1)
11.3.4.3 Simultaneous Modular Approach
356(1)
11.3.5 Output Phase
356(1)
11.3.6 Simulation Study Using a Flowsheeting Programme
356(1)
11.4 Short-Cut Methods and Rigorous Methods
357(1)
11.5 Dynamic Simulation
358(2)
11.5.1 Data Input and Verification
359(1)
11.5.2 Calculation Phase
359(1)
11.5.3 Data Output
360(1)
11.6 Batch Processes
360(5)
11.6.1 Simulation Methodology
360(1)
11.6.2 Scheduling of Batch Processes
361(1)
11.6.2.1 Standard Recipe Approach
361(1)
11.6.2.2 Overall Optimisation Approach
362(1)
11.6.3 Representation of Batch Processes
362(1)
11.6.3.1 State Task Network
362(1)
11.6.3.2 Resource Task Network
362(1)
11.6.4 Discrete Time Formulation
363(1)
11.6.5 Types of Constraints
364(1)
11.7 Summary
365(1)
References
365(2)
Appendix A 367(4)
Appendix B 371(6)
Index 377
Ashok Kumar Verma is a professor in the Department of Chemical Engineering and Technology at the Indian Institute of Technology (Banaras Hindu University) Varanasi. He holds a BSc from Allahabad University, a BE in chemical engineering from University of Roorkee (now Indian Institute of Technology, Roorkee), an ME in chemical engineering from the Indian Institute of Sciences, Bangalore, and a PhD in chemical engineering from the Indian Institute of Technology, Kanpur. Dr. Verma joined the Institute of Technology, Banaras Hindu University, Varanasi in 1984. Dr. Verma has authored or co-authored numerous papers in journals, and national and international proceedings.