Dokumentvorlage für wissenschaftliche Arbeiten

180 Pages·2015·1.98 MB·English

by  David Schmidtke

Fakultät für Chemie der Technischen Universität München
Lehrstuhl für Technische Chemie I
Value Stream Mapping and Discrete Event Simulation for Optimization of Chemical
Production Processes
David Christian Schmidtke
Vollständiger Abdruck der von der Fakultät für Chemie der Technischen Universität
München zur Erlangung des akademischen Grades eines
Doktor-Ingenieurs (Dr.-Ing.)
genehmigten Dissertation.
Vorsitzende(r): Univ.-Prof. Dr. rer. nat. Thomas Brück
Prüfer der Dissertation:
1. Univ.-Prof. Dr.-Ing. Kai-Olaf Hinrichsen
2. Univ.-Prof. Dr.-Ing. Dirk Weuster-Botz

Die Dissertation wurde am 28.04.2015 bei der Technischen Universität München
eingereicht und durch die Fakultät für Chemie am 28.09.2015 angenommen.

Acknowledgements

Acknowledgements
Over the course of compiling my dissertation, I was able to profit from the support of
numerous colleagues and friends. This support involved professional as well as personal
guidance, which was crucial for my work within the last years. In particular, I would
like to express my gratitude to the following group of people:
First of all, I would like to thank Prof. Kai-Olaf Hinrichsen for giving me the
opportunity to channel my research into a dissertation. I would like to thank him for his
enduring interest in my field of study and his great guidance throughout my dissertation
project. I am also thankful to his entire research group, the Chair of Technical
Chemistry I at the Technical University of Munich. In particular, I would like to thank
Prof. Hinrichsen’s assistant, Heidi Holweck, who greatly supported me despite my
status as an external PhD student.
Furthermore, I am grateful to my colleagues at Clariant Produkte Deutschland
GmbH, who supported me throughout my dissertation project. Especially, I want to
thank Dr. Uwe Heiser, the initiator of my dissertation, who closely guided me during
my work and shared numerous ideas, comments and suggestions with me, which helped
shaping my thesis. I would further like to thank Maximilian Weiss, whom I could
support with his Master thesis, as well as Dr. Andrea De Toni, Dr. Andreas Bentele,
Andreas Rauch, Enrico Zein, PD Dr. Frank Klose, Dr. Gustav Schrenk, Markus Kerzel
and Sascha Podehl for their countless fruitful discussions.
I am greatly thankful to the Imagine That Inc. team, who provided me with a
research grant for their Discrete Event Simulation package ExtendSim® AT. What’s
more, they offered me invaluable support for the simulation approaches of my thesis. In
particular, I would like to thank Bob Diamond, Cecile Damiron, Dave Krahl and Kathi
Hansen.
Last but definitely not least, I would like thank my parents Gabi and Joachim
Schmidtke, as well as my sister Marie Schmidtke for all their help, advice and support,
not only during my studies. Especially, I would like to thank my beloved Kathi. Without
you, this work would not have been possible!
Thanks for all of that, David

I
Abstract

Abstract
Over the past 15 years, Value Stream Mapping (VSM) has emerged as the predominant
Lean implementation method within the manufacturing industry. The secret of its
success is rooted in the holistic approach and, at the same, the straightforwardness
which allows for engagement of all stakeholders along the value stream. These
attributes of the “paper and pencil” method, however, result in limitations when
applying VSM in complex production environments, as particularly found in the
chemical industry. Upon closer inspection, VSM lacks the ability to adequately capture
complex process routing and to account for process variability. In those environments, it
further fails to prove feasibility and to reliably quantify the actual benefit of proposed
future states prior to implementation of the improvement measures. In this thesis, these
shortcomings are tackled through enhancement of the method with the aid of Discrete
Event Simulation (DES). The outcome is a VSM approach, which is capable of
predicting feasibility and benefit of proposed future states in complex chemical
production environments. In addition, the applicability of DES for modelling chemical
production processes is explored beyond its deployment in VSM projects. Integration of
DES with Discrete Rate Simulation and continuous simulation is elaborated, in order to
account for peculiarities of chemical production. The benefit of the simulation
technique, which has only been applied scarcely in the chemical industry to date, is
demonstrated for holistic operating cost optimization as well as debottlenecking of
transient mixed batch-continuous chemical production processes.

II
Kurzzusammenfassung

Kurzzusammenfassung
In den vergangenen 15 Jahren hat sich die Wertstromanalyse als bevorzugte Lean-
Implementierungsmethode der Industrie etabliert. Der Hauptgrund für die weite
Verbreitung der Methode liegt in ihrer Ganzheitlichkeit und Geradlinigkeit, die es
ermöglicht, alle in eine Prozesskette involvierten Personen in das Wertstromanalysen-
Projekt zur Lean-Implementierung einzubinden. Diese Eigenheit der Methode, für
welche keine besonderen technischen Hilfsmittel benötigt werden, führt allerdings zu
Limitierungen bei deren Anwendung auf komplexe Produktionssysteme, wie sie
beispielsweise häufig in der chemischen Industrie zu finden sind. Bei genauerer
Betrachtung stellt man fest, dass durch die Wertstromanalyse komplexe
Prozessführungen sowie Variabilität nicht abgebildet werden können. Darüber hinaus
ist es während eines Wertstromanalysen-Projekts oft nicht möglich, die technische
Umsetzbarkeit und den monetären Nutzen des auf dem Papier optimierten Prozesses vor
der Implementierung verlässlich vorherzusagen. In der vorliegenden Arbeit wird die
Methode mit Hilfe von Discrete Event Simulation (DES) so modifiziert, dass auch
komplexe Produktionssysteme analysiert und die Umsetzbarkeit sowie der Nutzen
eines Optimierungsvorschlags vorab bewertet werden können. Zusätzlich wird die
Anwendbarkeit von DES über den Einsatz in Wertstromanalysen-Projekten hinaus zur
Modellierung und Optimierung chemischer Prozesse untersucht. Hierbei wird DES mit
Discrete Rate Simulation und kontinuierlichen Simulationstechniken kombiniert, um die
Besonderheiten von chemischen Produktionsprozessen adäquat abbilden zu können. Der
Nutzen des Simulationsansatzes, welcher bisher kaum in der chemischen Industrie
Anwendung gefunden hat, wird anhand eines Projekts zur ganzheitlichen
Kostenoptimierung sowie eines Projekts zur Durchsatzoptimierung, jeweils für einen
instationären chemischen Prozesses bestehend aus verschiedenen kontinuierlichen und
chargenweise betriebenen Verfahrensschritten, gezeigt.

III
Kurzzusammenfassung

IV
Table of Contents

Table of Contents
Acknowledgements .......................................................................................................... I
Abstract ...........................................................................................................................II
Kurzzusammenfassung ................................................................................................ III
Table of Contents ............................................................................................................ V
Abbreviations ............................................................................................................. VIII
Symbols .......................................................................................................................... IX
Tables ............................................................................................................................... X
Figures .......................................................................................................................... XII
1 Introduction ............................................................................................................ 1
1.1 The evolution of production systems – from craft to Lean
production .................................................................................................. 1
1.2 Goals, principles and tools of Lean production ......................................... 3
1.3 Lean production in the chemical industry .................................................. 6
1.4 Scope and structure of this thesis ............................................................... 9
2 Optimization in the chemical industry ............................................................... 10
2.1 Economical aspects of chemical production processes ........................... 10
2.2 Objectives and strategies for optimization of chemical processes........... 15
2.3 Multi-objective optimization problems.................................................... 18
2.4 Strategies for solving optimization problems .......................................... 20
3 Value Stream Mapping ........................................................................................ 23
3.1 VSM – an integral approach to Lean implementation ............................. 23
3.2 Procedure of a VSM project .................................................................... 24
3.2.1 Selection of a product family ................................................................... 24
3.2.2 Current state map ..................................................................................... 25
V
Table of Contents

3.2.3 Future state map ....................................................................................... 30
3.2.4 Work plan for achieving the future state .................................................. 33
3.3 VSM in the context of optimization......................................................... 33
3.4 VSM for chemical process optimization .................................................. 36
3.4.1 Limitations ............................................................................................... 36
3.4.2 Potential adaptions ................................................................................... 39
4 Discrete Event Simulation for decision support in complex
environments ........................................................................................................ 42
4.1 Introduction to DES ................................................................................. 42
4.2 Steps in a DES study ................................................................................ 44
4.3 Adaption of DES to chemical processes .................................................. 50
4.3.1 Review of DES application in the chemical industry .............................. 50
4.3.2 Introduction to DRS ................................................................................. 51
4.3.3 Integration of DRS with DES and continuous modelling ........................ 53
4.4 Review of the application of DES for enhancing VSM ........................... 55
4.5 Development of a DES enhanced VSM procedure for complex
production environments ......................................................................... 56
5 Case studies ........................................................................................................... 60
5.1 Case study 1: Application of VSM in a catalytic converter
production process ................................................................................... 62
5.1.1 Selection of a product family ................................................................... 63
5.1.2 Current state map ..................................................................................... 64
5.1.2.1 Process Description .................................................................................. 65
5.1.2.2 Process flow and inventory ...................................................................... 68
5.1.2.3 Analysis of the current state ..................................................................... 71
5.1.3 Future state map ....................................................................................... 74
5.1.3.1 Shop floor future state .............................................................................. 74
5.1.3.2 Supply chain future state .......................................................................... 78
5.1.4 Discussion ................................................................................................ 79
5.2 Case study 2: Application of DES enhanced VSM in a catalytic
converter production process ................................................................... 81
5.2.1 Selection of a product family ................................................................... 81
5.2.2 Current state mapping .............................................................................. 82
5.2.3 Future state mapping ................................................................................ 87
5.2.4 Feasibility and trade-off analysis ............................................................. 90
5.2.4.1 DES model translation, verification and validation ................................. 90
VI
Table of Contents

5.2.4.2 Feasibility analysis. .................................................................................. 91
5.2.4.3 Trade-off analysis. ................................................................................... 96
5.2.5 Discussion ................................................................................................ 98
5.3 Case study 3: Application of DES/DRS for cost efficient operation
of a precipitation/filtration unit .............................................................. 101
5.3.1 Process description and problem formulation ........................................ 101
5.3.2 Model conceptualization ........................................................................ 104
5.3.3 Model translation and verification ......................................................... 106
5.3.4 Model experimentation and analysis ..................................................... 109
5.3.5 Discussion .............................................................................................. 114
5.4 Case study 4: Application of DES/DRS for debottlenecking of a
complex multi-stage waste water treatment process .............................. 117
5.4.1 Process description and problem formulation ........................................ 117
5.4.2 Model conceptualization and input parameter modelling ...................... 119
5.4.3 Model translation, verification and validation ....................................... 121
5.4.4 Model experimentation and analysis ..................................................... 126
5.4.5 Discussion .............................................................................................. 131
6. Concluding summary ......................................................................................... 133
7. Abschließende Zusammenfassung .................................................................... 136
Appendix A: Supplementary data for case study 3 .................................................. 140
A.1 Process flow diagram ............................................................................. 140
A.2 Material balance ..................................................................................... 140
A.3 Process ................................................................................................... 140
A.4 Variability .............................................................................................. 142
A.5 Objective function .................................................................................. 143
Appendix B: Supplementary data for case study 4 .................................................. 144
B.1 Process flow diagram ............................................................................. 144
B.2 Process and variability ........................................................................... 145
B.3 Material balance recalculation algorithm for simulation model ............ 150
References..................................................................................................................... 153
Curriculum Vitae – David Schmidtke ....................................................................... 161
VII
Abbreviations

Abbreviations

DES Discrete Event Simulation
DOE Design Of Experiments
DRS Discrete Rate Simulation
ES Evolutionary Strategies
FIFO First In First Out
I Inventory
IID independent and identically distributed
JIT Just In Time
pc Piece
PGM Platinum Group Metals
SCM Supply Chain Management
SCR Selective Catalytic Reduction
SME Subject Matter Expert (process expert, who provides input data for a Discrete
Event Simulation model)
SMED Single-Minute Exchange of Die
TPM Total Productive Maintenance
TPS Toyota Production System
VSM Value Stream Mapping

VIII