A generalized framework for multi-scale simulation of complex crystallization processes [Elektronische Ressource] / Viacheslav Kulikov

Informations

• Publié par: rheinisch-westfalischen_technischen_hochschule_-rwth-_aachen
• Publié le: 01 janvier 2011
• Nombre de lectures: 14
• Langue: English
• Poids de l'ouvrage: 3 Mo

Extrait

”A Generalized Framework for Multi Scale Simulation of
Complex Crystallization Processes”
Von der Fakultat¨ fur¨ Maschinenwesen der Rheinisch Westfalischen¨
Technischen Hochschule Aachen zur Erlangung des akademischen Grades
eines Doktors der Ingenieurwissenschaften genehmigte Dissertation
vorgelegt von
Viacheslav Kulikov
Berichter: Universitatsprofessor¨ Dr. Ing. Wolfgang Marquardt
¨Universit Dr. Ing. Heiko Briesen
Tag der mundlichen¨ Prufung:¨ 20. September 2010
Diese Dissertation ist auf den Internetseiten der Hochschulbibliothek online verfugbar¨ .Contents
Preface VIII
Summary IX
Kurzfassung XI
1 Introduction 1
2 Fundamentals of crystallization and fluid dynamics modeling 4
2.1 Crystallization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
2.1.1 Characterization of particles . . . . . . . . . . . . . . . . . . . . . . . 4
2.1.2 Population balance . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
2.1.3 Crystallization kinetics . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2.1.4 Solution of population balance models . . . . . . . . . . . . . . . . . 11
2.2 Fluid dynamics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
2.2.1 Modeling of fluid dynamics problems . . . . . . . . . . . . . . . . . . 14
2.2.2 Methods of computational fluid dynamics . . . . . . . . . . . . . . . . 16
2.3 Multi physics and multi scale problems . . . . . . . . . . . . . . . . . . . . . 17
2.3.1 Classification of complexities . . . . . . . . . . . . . . . . . . . . . . 17
2.3.2 Interplay between CFD and crystallization . . . . . . . . . . . . . . . 18
2.3.3 Models with structural complexity: CFD and process simulation . . . . 19
2.3.4 Multi scale modeling . . . . . . . . . . . . . . . . . . . . . . . . . . 20
2.3.5 Simulation approaches for fluid dynamics – population balance prob
lems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
3 Simulation of process flowsheets 24
3.1 Flowsheet representation and simulation approaches . . . . . . . . . . . . . . 24
3.1.1 Flowsheet representation . . . . . . . . . . . . . . . . . . . . . . . . 25
3.1.2 Overview of the simulation approaches . . . . . . . . . . . . . . . . . 27
IICONTENTS III
3.2 Modular dynamic simulation . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
3.2.1 Iteration in function space . . . . . . . . . . . . . . . . . . . . . . . . 29
3.2.2 in modular flowsheet simulation . . . . . . . . . . . . . . . . 30
3.2.3 Time discretization and integration intervals . . . . . . . . . . . . . . 34
3.2.4 Modular dynamic simulation algorithm . . . . . . . . . . . . . . . . . 37
3.3 Software implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
3.3.1 Requirements for software tool integration . . . . . . . . . . . . . . . 39
3.3.2 Simulation software tools . . . . . . . . . . . . . . . . . . . . . . . . 39
3.3.3 Tool integration solutions . . . . . . . . . . . . . . . . . . . . . . . . . 41
3.4 Case study: pentaerythritol crystallization . . . . . . . . . . . . . . . . . . . . 43
3.4.1 Problem description . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
3.4.2 Choice of software tools . . . . . . . . . . . . . . . . . . . . . . . . . 47
3.4.3 Simulation results . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
3.5 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
4 Coupled simulation of fluid dynamics and crystallization 53
4.1 Example problem: tubular crystallizer . . . . . . . . . . . . . . . . . . . . . . 53
4.2 Generalized process flowsheet modeling and simulation . . . . . . . . . . . . 56
4.2.1 Problem decomposition . . . . . . . . . . . . . . . . . . . . . . . . . 56
4.2.2 Multi scale problem discretization: fine grid and compartments . . . . 58
4.2.3 Scale integration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
4.2.3.1 Operations of scale integration . . . . . . . . . . . . . . . . 61
4.2.3.2 Spatial aggregation . . . . . . . . . . . . . . . . . . . . . . 61
4.2.3.3 Spatial disaggregation in the cells . . . . . . . . . . . . . . 62
4.2.3.4 Spatialgation on the surfaces . . . . . . . . . . . . 66
4.2.3.5 Scale integration in the generalized flowsheet . . . . . . . . 67
4.2.4 Auxiliary modeling and disaggregation of source terms . . . . . . . . 67
4.2.5 Choice and treatment of coupling variables . . . . . . . . . . . . . . . 69
4.2.6 Multi scale problem decomposition . . . . . . . . . . . . . . . . . . . 72
4.2.7 Subproblem setup in software tools . . . . . . . . . . . . . . . . . . . 77
4.2.8 Overdetermined problems and built in relations . . . . . . . . . . . . 79
4.2.9 Modular dynamic simulation algorithm for generalized flowsheet . . . 79
4.3 Analysis of the generalized flowsheet . . . . . . . . . . . . . . . . . . . . . . 82
4.3.1 Characterization of errors in the simulation . . . . . . . . . . . . . . . 82
4.3.2 Model consistency . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83
4.3.2.1 Consistency of generalized flowsheet model . . . . . . . . . 83
4.3.2.2 Equivalence of crystallization subproblem equation terms . . 84IV CONTENTS
4.3.2.3 Equivalence in the fluid dynamics subproblem . . . . . . . . 88
4.3.3 Scale integration errors . . . . . . . . . . . . . . . . . . . . . . . . . 88
4.3.4 Numerical consistency error . . . . . . . . . . . . . . . . . . . . . . . 89
4.3.5 Reference error . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90
4.3.6 Sources of error in the coupled simulation . . . . . . . . . . . . . . . 90
4.3.6.1 Numerical integration error in software tools . . . . . . . . . 90
4.3.6.2 Approaches to estimation of discretization error . . . . . . . 91
4.3.6.3 to evaluation of error propagation . . . . . . . . 92
4.3.6.4 Conclusions on error estimation . . . . . . . . . . . . . . . . 93
4.3.7 Convergence of modular simulation . . . . . . . . . . . . . . . . . . . 94
4.4 Case study: Simulation of tubular crystallizer . . . . . . . . . . . . . . . . . . 94
4.4.1 Setup of simulation experiments . . . . . . . . . . . . . . . . . . . . . 95
4.4.2 Dynamic results . . . . . . . . . . . . . . . . . . . . . . . 97
4.4.3 Simulations with different coupling variables . . . . . . . . . . . . . . 104
4.4.4 Effect of the turbulent diffusion . . . . . . . . . . . . . . . . . . . . . 112
4.4.5 Simulations with different number of compartments . . . . . . . . . . 114
4.4.6 with different disaggregation methods . . . . . . . . . . . 115
4.4.7 Computational performance . . . . . . . . . . . . . . . . . . . . . . . 117
4.5 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120
5 Simulation with adaptive compartment selection 123
5.1 Compartment adaptation with heuristic criteria . . . . . . . . . . . . . . . . . 124
5.1.1 Adaptation in generalized flowsheet simulation . . . . . . . . . . . . . 124
5.1.2 Formulation of compartment adaptation problem . . . . . . . . . . . . 125
5.1.3 Adaptation criteria . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125
5.1.3.1 Adaptation criteria based on single variable . . . . . . . . . 126
5.1.3.2 Composite adaptation criteria . . . . . . . . . . . . . . . . . 128
5.1.4 Adaptation procedure . . . . . . . . . . . . . . . . . . . . . . . . . . 128
5.1.4.1 Delta algorithm for compartment selection . . . . . . . . . . 128
5.1.4.2 Split merge adaptation procedure . . . . . . . . . . . . . . . 130
5.1.5 Software technical implementation of adaptation . . . . . . . . . . . . 135
5.2 Simulation of the tubular crystallizer with compartment adaptation . . . . . . . 135
5.2.1 Adaptation with different simulation criteria . . . . . . . . . . . . . . 136
5.2.2 Evolution of topology in dynamic simulation . . . . . . . . . . . . . . 142
5.3 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 144CONTENTS V
6 Case study 146
6.1 Forced Circulation crystallizer . . . . . . . . . . . . . . . . . . . . . . . . . . 146
6.2 Modeling of the FC . . . . . . . . . . . . . . . . . . . . . . . . . 148
6.2.1 Structure and model assumptions . . . . . . . . . . . . . . . . . . . . 149
6.2.2 Model of the crystallizer vessel . . . . . . . . . . . . . . . . . . . . . 150
6.2.2.1 Fluid dynamics model . . . . . . . . . . . . . . . . . . . . . 150
6.2.2.2 Population balance model . . . . . . . . . . . . . . . . . . . 151
6.2.2.3 Particle growth model . . . . . . . . . . . . . . . . . . . . . 151
6.2.2.4 Particle settling model . . . . . . . . . . . . . . . . . . . . . 152
6.2.3 Model of the pump . . . . . . . . . . . . . . . . . . . . . . . . . . . . 154
6.2.4 Model of the heat exchanger . . . . . . . . . . . . . . . . . . . . . . . 157
6.2.5 Reference experimental data . . . . . . . . . . . . . . . . . . . . . . . 158
6.3 Coupled simulation of the FC crystallizer with a MSMPR assumption . . . . . 158
6.4 of the FC with a compartment model . . . . . 161
6.5 Coupled simulation of the FC crystallizer with adaptation . . . . 168
6.6 Evaluation of performance of the coupled simulation . . . . . . . . . . . . . . 171
6.7 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 174
7 Conclusions 176
List of variables 186
A Software technical implementation in CHEOPS 191
A.1 Communication and tool interface standards . . . . . . . . . . . . . . .