دانلود کتاب Modelling of Chemical Process Systems

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Cover
Half Title
Modelling of Chemical Process Systems
Copyright
Content
Contributors
Preface
Part I: Theory and Background
1. An introduction to modeling of chemical process systems
1. What is a model and modeling?
2. Historical perspective of simulation, systems engineering, and process systems modeling
2.1 Theoretical development
2.2 Computer software
2.3 Human-machine interface
3. Classification of models
3.1 Use of mechanism
3.2 Scales of models
4. Multiscale modeling
5. Modeling applications in processes
5.1 Research and development of new product
5.2 Design and commissioning of process systems
5.3 Operator training
5.4 Operations and debottlenecking
5.5 Abnormal situation management
6. Scope of the book
References
2. Model equations and modeling methodology
1. Process model and model equations
2. Model equations
2.1 Conservation equations
2.2 Constitutive equations
3. Systematic method for building process models
3.1 Systems thinking
3.2 Steps for mechanistic model building
3.3 Case study: PTA reactor model
4. Summary
References
Part II: Micro Scale Modelling
3. Density functional theory (DFT) models for extraction of sulfur compounds from fuel oils by using ionic liquids
1. Introduction
1.1 Global hardness and global softness
1.2 Electronegativity
1.3 Chemical potential
1.4 Electrophilicity index
1.5 Dipole moment
2. Results and discussion
2.1 Effect of the alkyl group chain lengths of the N-alkylpyridium and N-carboxyalkylpyridinium cations on the quantum chemical properties
2.2 Effect of alkyl group chain lengths on the quantum chemical properties of the N-alkylpyridium ILs
2.3 Effect of the five anions on the quantum chemical properties of
2.4 Effect of the alkyl group chain lengths on the quantum chemical properties of the N-carboxyalkylpyridinium-based ILs
2.5 Molecular electrostatic potential results
2.6 Interaction energies and thermodynamic calculations for the interactions with DBT and the ILs
2.7 N-Alkylpyridinium ILs with HSO4- anions
2.8 N-Alkylpyridinium ILs with H2PO4- and Ac anions
2.9 N-Alkylpyridinium ILs with TFA and BF4 anions
2.10 N-Carboxyalkylpyridinium ILs with HSO4- anions
2.11 N-Carboxyalkylpyridinium ILs with H2PO4- and Ac- anions
2.12 N-Carboxyalkylpyridinium ILs with TFA- and BF4- anions
3. Conclusion and perspective for future developments
4. Summary
Acknowledgments
References
4. Molecular dynamics simulation in energy and chemical systems
1. Introduction
2. Fundamentals of MD technique
3. Emerge of MD technique
4. Architecture of MD technique
5. Theoretical frameworks of MD technique
5.1 Atomic interactions and forces
5.2 Periodic boundary conditions
5.3 Numerical integration algorithms
5.4 Statistical ensembles
5.5 Property calculation
6. Algorithms and simulation packages for MD technique
7. Advantages and disadvantages of the MD technique
8. Applications/case studies of MD
9. Theoretical and practical challenges in MD implementation
10. Current status and future prospects of MD technique
References
5. Single-event kinetic modeling of catalytic dewaxing on commercial Pt/ZSM-5
1. Introduction
2. Reactor modeling
2.1 Shape-selectivity effects
2.2 Reaction mechanism
2.3 Reaction network generation
2.4 Single-event kinetic modeling
2.5 Posteriori lumping
2.6 Net rate of formation
2.7 Reactor model
2.8 Physical properties estimation
3. Results and discussion
3.1 Parameter estimation
3.2 Effect of temperature
3.3 Effect of pressure
3.4 Effect of H2/HC ratio
3.5 Effect of the liquid hourly space velocity (LHSV)
3.6 Effects of feed carbon number
3.7 Effect of shape selectivity
4. Conclusions
References
6. Modeling and simulation of batch and continuous crystallization processes
1. Introduction to solution crystallization
2. Supersaturation and metastable limit
2.1 Solubility
2.2 Supersaturation
2.3 Metastable zone and metastable limit
3. Kinetics of crystallization in supersaturation
3.1 Nucleation kinetics
3.2 Kinetics of crystal growth
4. Crystal size distribution and population balance equations
4.1 Crystal size distribution
4.2 The population balance equation
5. Modeling of batch and continuous crystallization processes
5.1 Batch crystallization
5.1.1 Modeling of a batch crystallization process
5.1.2 Case study I: Seeded batch crystallization of (R)-mandelic acid in the presence opposite enantiomer
5.2 Continuous crystallization
5.2.1 Modeling of an MSMPR crystallizer
5.2.2 Case study II: The MSMPR crystallization of ciprofloxacin from crude APIs
Summary
References
Part III: Macro Scale Modelling of Process Systems
7. Fuel processing systems
1. The need for fuel processing units
2. Fundamentals of fuel processing
2.1 Steam reforming
2.2 Dry reforming
2.3 Partial oxidation
2.4 Water gas shift reaction
2.5 Autothermal reforming
3 Recent developments in the reforming of common fuels
3.1 Reforming of hydrocarbons
3.2 Alcohol reforming
4. Electrochemical H2 production
4.1 PEM electrolyzer
4.2 Solid oxide electrolyzer
4.3 Overpotential loses
5. Kinetic models for reforming
5.1 Methane reforming
5.2 Methanol reforming
5.3 Ethanol reforming
6. Reactor choice
6.1 Reactor designs based on packed bed
6.2 Reactor design based on monoliths
6.3 Membrane reactors
7. Reactor modeling
7.1 Zero-dimensional stirred tank model
7.2 One-dimensional plug flow model
7.3 One-dimensional packed-bed model
8. Sizing of reactor for applications in fuel cells
9. Summary
References
8. Crude to chemicals: Conventional FCC unit still relevant
1. History of direct crude processing
2. Update on crude to chemical processing
3. FCC unit: Conventional FCC units with high severity to maximize propylene and ethylene
4. Riser and regenerator mathematical model
5. FCC catalysts and role in crude to chemical technology
6. The future of crude to chemicals
References
Part IV: Machine Learning Techniques for Modelling Process Systems
9. Hybrid model for a diesel cloud point soft-sensor
1. Introduction
1.1 Terminology related to hybrid model
2. Case study: A hybrid model for diesel cloud point prediction
2.1 Cloud point soft-sensor—Mechanistic model
2.1.1 TBP module
2.1.2 Hydro-dewaxing (HDW) reactor model
2.1.3 Hydrodesulfurization reactor model
2.1.4 Solid-liquid equilibrium thermodynamic model
2.2 Surrogate model
2.2.1 Design of computer experiments
2.2.2 Nonlinearity tracking and exploration capability
3. Results
3.1 Industrial case study
3.2 On-line cloud point soft-sensor application
4. Summary
Appendix
References
10. Large-scale process models using deep learning
1. Large-scale system modeling challenges
2. Motivation for deep learning algorithms
3. Deep learning methods
3.1 Supervised deep learning
3.2 Unsupervised deep learning
4. Exploring key deep learning methods in large-scale process modeling
4.1 Recurrent neural network
4.2 Long short-term memory network
4.3 Autoencoder
4.4 Variational autoencoder
5. Application to modeling chemical and biological systems
6. Summary
References
Index