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Process Modelling and Simulation in Chemical, Biochemical and Environmental Engineering (Paperback, 3rd Edition)
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Process Modelling and Simulation in Chemical, Biochemical and Environmental Engineering (Paperback, 3rd Edition)
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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.
Table of Contents
Introduction to Modelling and Simulation
Chemical Processes
What Is Simulation?
Modelling
Summary
References
An Overview of Modelling and Simulation
Strategy for Simulation
Approaches for Model Development
Types of Models
Types of Equations in a Model and Solution Strategy
Sources of Equations
Simplifying Concepts
Summary
References
Models Based on Simple Laws
Equation of State
Henry’s Law
Newton’s Law of Viscosity
Fourier’s Law of Heat Conduction
Fick’s First Law
Fick’s Second Law
Film Model
Two-Film Theory
Arrhenius’ Law
Adsorption Isotherms
Examples
Summary
References
Models Based on Laws of Conservation
Laws of Conservation of Momentum, Mass and Energy
Laminar Flow
Boundary Layers: Momentum, Thermal and Diffusional
Turbulence Models
Surface Renewal Models at High Flux of Momentum,
Mass or Heat
Analogy between Momentum, Mass and Heat Transfer
Simple Models for Reactors and Bioreactors
Summary
References
Multiphase Systems without Reaction
Consideration of a Continuous-Phase Axial Solid Profile in a Slurry Bubble Column
Single Interface: The Wetted Wall Column
Stationary Dispersed-Phase Systems (Gas–Solid Systems)
Moving Dispersed Systems (Gas–Solid Systems):
Wall-to-bed Heat Transfer in a Fluidised Bed
Moving Dispersed Systems (Gas–Liquid Systems): Transfer Processes in Bubble Columns
Regions of Interest Adjacent to the Interface
More Than One Mechanism of Heat Transfer: Flat-Plate
Solar Collector
Introducing Other Effects in Laws of Conservation
Summary
References
Multiphase Systems with Reaction
Development of a Model for Multiphase Reactors: Common Assumptions and Methodology
Packed Bed Reactors
Trickle Bed Reactors
Slurry Reactors
Fluidised Bed Reactors
Summary
References
Population Balance Models and Discrete-Event Models
Stochastic Models
The Complex Nature of the Dispersed Phase
Population Balance Equation
Probability Distribution Functions
Population Balance Models: Simulation Methodology
Summary
References
Artificial Neural Network–Based Models
Artificial Neural Networks
Development of ANN-Based Models
Applications of ANNs in Chemical Engineering
Advantages of ANN-Based Models
Limitations of ANN-Based Models
Hybrid Neural Networks
Summary
References
Model Validation and Sensitivity Analysis
Model Validation: Objective
Model Validation Methodology
Sensitivity Analysis
Global Sensitivity Measures
Role of Sensitivity Analysis
Summary
References
Case Studies
Axial Distribution of Solids in Slurry Bubble Columns: Analytical Deterministic Models
Conversion for a Gas–Liquid Reaction in a Shallow Bed: A Numerical Model
Stochastic Model to Predict Wall-to-Bed Mass Transfer in Packed and Fluidised Beds
Artificial Neural Network Model: Heat Transfer in Bubble Columns
Summary
References
Simulation of Large Plants
Interconnecting Sub-Models
Simulation Study
Flowsheeting and Continuous Processes
Short-Cut Methods and Rigorous Methods
Dynamic Simulation
Batch Processes
Summary
References
Appendix A
Appendix B
Index
General
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