Computation of mathematical models for complex industrial processes /:
Designed for undergraduate and postgraduate students, academic researchers and industrial practitioners, this book provides comprehensive case studies on numerical computing of industrial processes and step-by-step procedures for conducting industrial computing. It assumes minimal knowledge in numer...
Gespeichert in:
1. Verfasser: | |
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Weitere Verfasser: | , , |
Format: | Elektronisch E-Book |
Sprache: | English |
Veröffentlicht: |
Hackensack, NJ :
World Scientific Publishing Co. Inc.,
2014.
|
Schriftenreihe: | Advances in process systems engineering ;
v. 4. |
Schlagworte: | |
Online-Zugang: | Volltext |
Zusammenfassung: | Designed for undergraduate and postgraduate students, academic researchers and industrial practitioners, this book provides comprehensive case studies on numerical computing of industrial processes and step-by-step procedures for conducting industrial computing. It assumes minimal knowledge in numerical computing and computer programming, making it easy to read, understand and follow. Topics discussed include fundamentals of industrial computing, finite difference methods, the Wavelet-Collocation Method, the Wavelet-Galerkin Method, High Resolution Methods, and comparative studies of various methods. These are discussed using examples of carefully selected models from real processes of industrial significance. The step-by-step procedures in all these case studies can be easily applied to other industrial processes without a need for major changes. Thus, they provide readers with useful frameworks for the applications of engineering computing in fundamental research problems and practical development scenarios. |
Beschreibung: | 1 online resource (xiv, 148 pages) |
Bibliographie: | Includes bibliographical references. |
ISBN: | 9789814360944 9814360945 1306941970 9781306941976 |
Internformat
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245 | 1 | 0 | |a Computation of mathematical models for complex industrial processes / |c Yu-Chu Tian, Tonghua Zhang, Hongmei Yao, Moses O Tade. |
260 | |a Hackensack, NJ : |b World Scientific Publishing Co. Inc., |c 2014. | ||
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490 | 1 | |a Advances in process systems engineering ; |v vol. 4 | |
504 | |a Includes bibliographical references. | ||
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505 | 0 | |a 1. Introduction. 1.1. Background. 1.2. Motivation. 1.3. Process modelling. 1.4. Model approximation. 1.5. Algorithm design and setup. 1.6. Interpretation of verification of computing results. 1.7. Book outline -- 2. Fundamentals of process modelling and model computation. 2.1. Building mathematical models. 2.2. General ODE and PDE models for industrial processes. 2.3. Examples of ODE and PDE process models. 2.4. Solutions of process models. 2.5. The Runge-Kutta methods. 2.6. Finite difference methods. 2.7. Wavelets-based methods. 2.8. High resolution methods -- 3. Finite difference methods for ordinary differential equation models. 3.1. Fermentation processes. 3.2. Biology of lysine synthesis. 3.3. Model construction. 3.4. Numerical approximations of fermentation models. 3.5. Simulation for Batch fermentation. 3.6. Simulation for Fed-Batch fermentation -- 4. Finite difference methods for partial differential equation models. 4.1. Continuous galvanizing processes. 4.2. Development of a PDE model. 4.3. Discrete state space model. 4.4. Stability analysis and parameter settings of the model. 4.5. Identification of model parameters. 4.6. Least-square algorithms for system identification. 4.7. Simplification of system identification algorithms. 4.8. Simulations and industrial applications -- 5. Wavelets-based methods. 5.1. Process modelling for chemical reactions. 5.2. Three versions of wavelet collocation methods. 5.3. Wavelet collocation method for reaction processes. 5.4. Model development for crystallization processes. 5.5. Wavelet Galerkin method for PDEs. 5.6. Solution based on wavelet Galerkin method -- 6. High resolution methods. 6.1. Column chromatographic separation processes. 6.2. Model development for column chromatography. 6.3. Analytical solution for linear equilibrium case. 6.4. Model discretization using high resolution methods. 6.5. The Alexander method for time integration. 6.6. Solutions to the chromatographic process model. 6.7. Crystallization and population balance equations. 6.8. Crystallization with pure size-independent growth. 6.9. Process with size-independent growth and nucleation -- 7. Comparative studies of numerical methods for SMB chromatographic processes. 7.1. Chromatographic separation processes. 7.2. Dynamic modelling of SMBC processes. 7.3. Numerical computation. 7.4. Case study I: Fructose-glucose separation. 7.5. Case study II: Bi-naphthol enantiomers separation. 7.6. Concluding remarks -- 8. Conclusion. | |
520 | |a Designed for undergraduate and postgraduate students, academic researchers and industrial practitioners, this book provides comprehensive case studies on numerical computing of industrial processes and step-by-step procedures for conducting industrial computing. It assumes minimal knowledge in numerical computing and computer programming, making it easy to read, understand and follow. Topics discussed include fundamentals of industrial computing, finite difference methods, the Wavelet-Collocation Method, the Wavelet-Galerkin Method, High Resolution Methods, and comparative studies of various methods. These are discussed using examples of carefully selected models from real processes of industrial significance. The step-by-step procedures in all these case studies can be easily applied to other industrial processes without a need for major changes. Thus, they provide readers with useful frameworks for the applications of engineering computing in fundamental research problems and practical development scenarios. | ||
650 | 0 | |a Mathematical models |x Industrial applications. | |
650 | 6 | |a Modèles mathématiques |x Applications industrielles. | |
650 | 7 | |a TECHNOLOGY & ENGINEERING |x Industrial Engineering. |2 bisacsh | |
650 | 7 | |a TECHNOLOGY & ENGINEERING |x Industrial Technology. |2 bisacsh | |
650 | 7 | |a TECHNOLOGY & ENGINEERING |x Manufacturing. |2 bisacsh | |
650 | 7 | |a TECHNOLOGY & ENGINEERING |x Technical & Manufacturing Industries & Trades. |2 bisacsh | |
700 | 1 | |a Zhang, Tonghua. | |
700 | 1 | |a Yao, Hongmei. | |
700 | 1 | |a Tade, Moses O. | |
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776 | 0 | 8 | |i Print version: |t Computation of Mathematical Models for Complex Insutrial Processes. |d Hackensack, NJ : World Scientific Publishing Co. Inc., 2014 |z 9789814360937 |
830 | 0 | |a Advances in process systems engineering ; |v v. 4. |0 http://id.loc.gov/authorities/names/no2009111210 | |
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DE-BY-FWS_katkey | ZDB-4-EBA-ocn883374314 |
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adam_text | |
any_adam_object | |
author | Tian, Yu-Chu |
author2 | Zhang, Tonghua Yao, Hongmei Tade, Moses O. |
author2_role | |
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author_facet | Tian, Yu-Chu Zhang, Tonghua Yao, Hongmei Tade, Moses O. |
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contents | 1. Introduction. 1.1. Background. 1.2. Motivation. 1.3. Process modelling. 1.4. Model approximation. 1.5. Algorithm design and setup. 1.6. Interpretation of verification of computing results. 1.7. Book outline -- 2. Fundamentals of process modelling and model computation. 2.1. Building mathematical models. 2.2. General ODE and PDE models for industrial processes. 2.3. Examples of ODE and PDE process models. 2.4. Solutions of process models. 2.5. The Runge-Kutta methods. 2.6. Finite difference methods. 2.7. Wavelets-based methods. 2.8. High resolution methods -- 3. Finite difference methods for ordinary differential equation models. 3.1. Fermentation processes. 3.2. Biology of lysine synthesis. 3.3. Model construction. 3.4. Numerical approximations of fermentation models. 3.5. Simulation for Batch fermentation. 3.6. Simulation for Fed-Batch fermentation -- 4. Finite difference methods for partial differential equation models. 4.1. Continuous galvanizing processes. 4.2. Development of a PDE model. 4.3. Discrete state space model. 4.4. Stability analysis and parameter settings of the model. 4.5. Identification of model parameters. 4.6. Least-square algorithms for system identification. 4.7. Simplification of system identification algorithms. 4.8. Simulations and industrial applications -- 5. Wavelets-based methods. 5.1. Process modelling for chemical reactions. 5.2. Three versions of wavelet collocation methods. 5.3. Wavelet collocation method for reaction processes. 5.4. Model development for crystallization processes. 5.5. Wavelet Galerkin method for PDEs. 5.6. Solution based on wavelet Galerkin method -- 6. High resolution methods. 6.1. Column chromatographic separation processes. 6.2. Model development for column chromatography. 6.3. Analytical solution for linear equilibrium case. 6.4. Model discretization using high resolution methods. 6.5. The Alexander method for time integration. 6.6. Solutions to the chromatographic process model. 6.7. Crystallization and population balance equations. 6.8. Crystallization with pure size-independent growth. 6.9. Process with size-independent growth and nucleation -- 7. Comparative studies of numerical methods for SMB chromatographic processes. 7.1. Chromatographic separation processes. 7.2. Dynamic modelling of SMBC processes. 7.3. Numerical computation. 7.4. Case study I: Fructose-glucose separation. 7.5. Case study II: Bi-naphthol enantiomers separation. 7.6. Concluding remarks -- 8. Conclusion. |
ctrlnum | (OCoLC)883374314 |
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dewey-sort | 3670.15118 |
dewey-tens | 670 - Manufacturing |
discipline | Werkstoffwissenschaften / Fertigungstechnik |
format | Electronic eBook |
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series | Advances in process systems engineering ; |
series2 | Advances in process systems engineering ; |
spelling | Tian, Yu-Chu. Computation of mathematical models for complex industrial processes / Yu-Chu Tian, Tonghua Zhang, Hongmei Yao, Moses O Tade. Hackensack, NJ : World Scientific Publishing Co. Inc., 2014. 1 online resource (xiv, 148 pages) text txt rdacontent computer c rdamedia online resource cr rdacarrier Advances in process systems engineering ; vol. 4 Includes bibliographical references. Print version record. 1. Introduction. 1.1. Background. 1.2. Motivation. 1.3. Process modelling. 1.4. Model approximation. 1.5. Algorithm design and setup. 1.6. Interpretation of verification of computing results. 1.7. Book outline -- 2. Fundamentals of process modelling and model computation. 2.1. Building mathematical models. 2.2. General ODE and PDE models for industrial processes. 2.3. Examples of ODE and PDE process models. 2.4. Solutions of process models. 2.5. The Runge-Kutta methods. 2.6. Finite difference methods. 2.7. Wavelets-based methods. 2.8. High resolution methods -- 3. Finite difference methods for ordinary differential equation models. 3.1. Fermentation processes. 3.2. Biology of lysine synthesis. 3.3. Model construction. 3.4. Numerical approximations of fermentation models. 3.5. Simulation for Batch fermentation. 3.6. Simulation for Fed-Batch fermentation -- 4. Finite difference methods for partial differential equation models. 4.1. Continuous galvanizing processes. 4.2. Development of a PDE model. 4.3. Discrete state space model. 4.4. Stability analysis and parameter settings of the model. 4.5. Identification of model parameters. 4.6. Least-square algorithms for system identification. 4.7. Simplification of system identification algorithms. 4.8. Simulations and industrial applications -- 5. Wavelets-based methods. 5.1. Process modelling for chemical reactions. 5.2. Three versions of wavelet collocation methods. 5.3. Wavelet collocation method for reaction processes. 5.4. Model development for crystallization processes. 5.5. Wavelet Galerkin method for PDEs. 5.6. Solution based on wavelet Galerkin method -- 6. High resolution methods. 6.1. Column chromatographic separation processes. 6.2. Model development for column chromatography. 6.3. Analytical solution for linear equilibrium case. 6.4. Model discretization using high resolution methods. 6.5. The Alexander method for time integration. 6.6. Solutions to the chromatographic process model. 6.7. Crystallization and population balance equations. 6.8. Crystallization with pure size-independent growth. 6.9. Process with size-independent growth and nucleation -- 7. Comparative studies of numerical methods for SMB chromatographic processes. 7.1. Chromatographic separation processes. 7.2. Dynamic modelling of SMBC processes. 7.3. Numerical computation. 7.4. Case study I: Fructose-glucose separation. 7.5. Case study II: Bi-naphthol enantiomers separation. 7.6. Concluding remarks -- 8. Conclusion. Designed for undergraduate and postgraduate students, academic researchers and industrial practitioners, this book provides comprehensive case studies on numerical computing of industrial processes and step-by-step procedures for conducting industrial computing. It assumes minimal knowledge in numerical computing and computer programming, making it easy to read, understand and follow. Topics discussed include fundamentals of industrial computing, finite difference methods, the Wavelet-Collocation Method, the Wavelet-Galerkin Method, High Resolution Methods, and comparative studies of various methods. These are discussed using examples of carefully selected models from real processes of industrial significance. The step-by-step procedures in all these case studies can be easily applied to other industrial processes without a need for major changes. Thus, they provide readers with useful frameworks for the applications of engineering computing in fundamental research problems and practical development scenarios. Mathematical models Industrial applications. Modèles mathématiques Applications industrielles. TECHNOLOGY & ENGINEERING Industrial Engineering. bisacsh TECHNOLOGY & ENGINEERING Industrial Technology. bisacsh TECHNOLOGY & ENGINEERING Manufacturing. bisacsh TECHNOLOGY & ENGINEERING Technical & Manufacturing Industries & Trades. bisacsh Zhang, Tonghua. Yao, Hongmei. Tade, Moses O. has work: Computation of mathematical models for complex industrial processes (Text) https://id.oclc.org/worldcat/entity/E39PCFxQXY9DjCPqVFgXD7wFjy https://id.oclc.org/worldcat/ontology/hasWork Print version: Computation of Mathematical Models for Complex Insutrial Processes. Hackensack, NJ : World Scientific Publishing Co. Inc., 2014 9789814360937 Advances in process systems engineering ; v. 4. http://id.loc.gov/authorities/names/no2009111210 FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=810361 Volltext |
spellingShingle | Tian, Yu-Chu Computation of mathematical models for complex industrial processes / Advances in process systems engineering ; 1. Introduction. 1.1. Background. 1.2. Motivation. 1.3. Process modelling. 1.4. Model approximation. 1.5. Algorithm design and setup. 1.6. Interpretation of verification of computing results. 1.7. Book outline -- 2. Fundamentals of process modelling and model computation. 2.1. Building mathematical models. 2.2. General ODE and PDE models for industrial processes. 2.3. Examples of ODE and PDE process models. 2.4. Solutions of process models. 2.5. The Runge-Kutta methods. 2.6. Finite difference methods. 2.7. Wavelets-based methods. 2.8. High resolution methods -- 3. Finite difference methods for ordinary differential equation models. 3.1. Fermentation processes. 3.2. Biology of lysine synthesis. 3.3. Model construction. 3.4. Numerical approximations of fermentation models. 3.5. Simulation for Batch fermentation. 3.6. Simulation for Fed-Batch fermentation -- 4. Finite difference methods for partial differential equation models. 4.1. Continuous galvanizing processes. 4.2. Development of a PDE model. 4.3. Discrete state space model. 4.4. Stability analysis and parameter settings of the model. 4.5. Identification of model parameters. 4.6. Least-square algorithms for system identification. 4.7. Simplification of system identification algorithms. 4.8. Simulations and industrial applications -- 5. Wavelets-based methods. 5.1. Process modelling for chemical reactions. 5.2. Three versions of wavelet collocation methods. 5.3. Wavelet collocation method for reaction processes. 5.4. Model development for crystallization processes. 5.5. Wavelet Galerkin method for PDEs. 5.6. Solution based on wavelet Galerkin method -- 6. High resolution methods. 6.1. Column chromatographic separation processes. 6.2. Model development for column chromatography. 6.3. Analytical solution for linear equilibrium case. 6.4. Model discretization using high resolution methods. 6.5. The Alexander method for time integration. 6.6. Solutions to the chromatographic process model. 6.7. Crystallization and population balance equations. 6.8. Crystallization with pure size-independent growth. 6.9. Process with size-independent growth and nucleation -- 7. Comparative studies of numerical methods for SMB chromatographic processes. 7.1. Chromatographic separation processes. 7.2. Dynamic modelling of SMBC processes. 7.3. Numerical computation. 7.4. Case study I: Fructose-glucose separation. 7.5. Case study II: Bi-naphthol enantiomers separation. 7.6. Concluding remarks -- 8. Conclusion. Mathematical models Industrial applications. Modèles mathématiques Applications industrielles. TECHNOLOGY & ENGINEERING Industrial Engineering. bisacsh TECHNOLOGY & ENGINEERING Industrial Technology. bisacsh TECHNOLOGY & ENGINEERING Manufacturing. bisacsh TECHNOLOGY & ENGINEERING Technical & Manufacturing Industries & Trades. bisacsh |
title | Computation of mathematical models for complex industrial processes / |
title_auth | Computation of mathematical models for complex industrial processes / |
title_exact_search | Computation of mathematical models for complex industrial processes / |
title_full | Computation of mathematical models for complex industrial processes / Yu-Chu Tian, Tonghua Zhang, Hongmei Yao, Moses O Tade. |
title_fullStr | Computation of mathematical models for complex industrial processes / Yu-Chu Tian, Tonghua Zhang, Hongmei Yao, Moses O Tade. |
title_full_unstemmed | Computation of mathematical models for complex industrial processes / Yu-Chu Tian, Tonghua Zhang, Hongmei Yao, Moses O Tade. |
title_short | Computation of mathematical models for complex industrial processes / |
title_sort | computation of mathematical models for complex industrial processes |
topic | Mathematical models Industrial applications. Modèles mathématiques Applications industrielles. TECHNOLOGY & ENGINEERING Industrial Engineering. bisacsh TECHNOLOGY & ENGINEERING Industrial Technology. bisacsh TECHNOLOGY & ENGINEERING Manufacturing. bisacsh TECHNOLOGY & ENGINEERING Technical & Manufacturing Industries & Trades. bisacsh |
topic_facet | Mathematical models Industrial applications. Modèles mathématiques Applications industrielles. TECHNOLOGY & ENGINEERING Industrial Engineering. TECHNOLOGY & ENGINEERING Industrial Technology. TECHNOLOGY & ENGINEERING Manufacturing. TECHNOLOGY & ENGINEERING Technical & Manufacturing Industries & Trades. |
url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=810361 |
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