Muutke küpsiste eelistusi

Dynamic Process Modeling Volume 7 [Kõva köide]

Volume editor (Instituto de Inverstigaciones Marinas (C.S.I.C.), Chemical Engineering Laboratory, Vigo, Spain), Volume editor (Imperial College London, Department of Chemical Engineering, London, United Kingdom), Series edited by (University College London, Department of Chemi), Volume editor
  • Formaat: Hardback, 628 pages, kõrgus x laius x paksus: 244x175x38 mm, kaal: 1202 g
  • Sari: Process Systems Engineering
  • Ilmumisaeg: 27-Oct-2010
  • Kirjastus: Blackwell Verlag GmbH
  • ISBN-10: 3527316965
  • ISBN-13: 9783527316960
Teised raamatud teemal:
  • Kõva köide
  • Hind: 204,45 €*
  • * saadame teile pakkumise kasutatud raamatule, mille hind võib erineda kodulehel olevast hinnast
  • See raamat on trükist otsas, kuid me saadame teile pakkumise kasutatud raamatule.
  • Kogus:
  • Lisa ostukorvi
  • Tasuta tarne
  • Lisa soovinimekirja
  • Raamatukogudele
Dynamic Process Modeling Volume 7Suurem pilt
  • Formaat: Hardback, 628 pages, kõrgus x laius x paksus: 244x175x38 mm, kaal: 1202 g
  • Sari: Process Systems Engineering
  • Ilmumisaeg: 27-Oct-2010
  • Kirjastus: Blackwell Verlag GmbH
  • ISBN-10: 3527316965
  • ISBN-13: 9783527316960
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9783527316960.html
Märksõnad:
Inspired by the leading authority in the field, the Centre for Process Systems Engineering at Imperial College London, this book includes theoretical developments, algorithms, methodologies and tools in process systems engineering and applications from the chemical, energy, molecular, biomedical and other areas. It spans a whole range of length scales seen in manufacturing industries, from molecular and nanoscale phenomena to enterprise-wide optimization and control. As such, this will appeal to a broad readership, since the topic applies not only to all technical processes but also due to the interdisciplinary expertise required to solve the challenge.
The ultimate reference work for years to come.
Preface xv
List of Contributors
xxi
Part I Chemical and Other Processing Systems
1(318)
1 Dynamic Process Modeling: Combining Models and Experimental Data to Solve Industrial Problems
3(32)
M. Matzopoulos
1.1 Introduction
3(2)
1.1.1 Mathematical Formulation
4(1)
1.1.2 Modeling Software
5(1)
1.2 Dynamic Process Modeling - Background and Basics
5(9)
1.2.1 Predictive Process Models
6(1)
1.2.2 Dynamic Process Modeling
6(1)
1.2.3 Key Considerations for Dynamic Process Models
7(2)
1.2.4 Modeling of Operating Procedures
9(1)
1.2.5 Key Modeling Concepts
10(1)
1.2.5.1 First-Principles Modeling
10(1)
1.2.5.2 Multiscale Modeling
10(1)
1.2.5.3 Equation-Based Modeling Tools
11(1)
1.2.5.4 Distributed Systems Modeling
12(1)
1.2.5.5 Multiple Activities from the Same Model
13(1)
1.2.5.6 Simulation vs. Modeling
13(1)
1.3 A Model-Based Engineering Approach
14(9)
1.3.1 High-Fidelity Predictive Models
14(2)
1.3.2 Model-Targeted Experimentation
16(1)
1.3.3 Constructing High-Fidelity Predictive Models - A Step-by-Step Approach
16(6)
1.3.4 Incorporating Hydrodynamics Using Hybrid Modeling Techniques
22(1)
1.3.5 Applying the High-Fidelity Predictive Model
22(1)
1.4 An Example: Multitubular Reactor Design
23(8)
1.4.1 Multitubular Reactors - The Challenge
24(1)
1.4.2 The Process
25(1)
1.4.3 The Solution
25(4)
1.4.4 Detailed Design Results
29(1)
1.4.5 Discussion
30(1)
1.5 Conclusions
31(4)
2 Dynamic Multiscale Modeling - An Application to Granulation Processes
35(32)
G.D. Ingram
I.T. Cameron
2.1 Introduction
35(1)
2.2 Granulation
36(5)
2.2.1 The Operation and Its Significance
36(1)
2.2.2 Equipment, Phenomena, and Mechanisms
37(2)
2.2.3 The Need for and Challenges of Modeling Granulation
39(2)
2.3 Multiscale Modeling of Process Systems
41(4)
2.3.1 Characteristics of Multiscale Models
41(2)
2.3.2 Approaches to Multiscale Modeling
43(2)
2.4 Scales of Interest in Granulation
45(7)
2.4.1 Overview
45(2)
2.4.2 Primary Particle Scale
47(1)
2.4.3 Granule Scale
48(1)
2.4.4 Granule Bed Scale
48(1)
2.4.5 Vessel Scale
49(1)
2.4.6 Circuit Scale
50(2)
2.5 Applications of Dynamic Multiscale Modeling to Granulation
52(9)
2.5.1 Overview
52(3)
2.5.2 Fault Diagnosis for Continuous Drum Granulation
55(1)
2.5.3 Three-Dimensional Multiscale Modeling of Batch Drum Granulation
56(2)
2.5.4 DEM-PBE Modeling of Batch High-Shear Granulation
58(1)
2.5.5 DEM-PBE Modeling of Continuous Drum Granulation
59(2)
2.6 Conclusions
61(6)
3 Modeling of Polymerization Processes
67(38)
B.S. Amaro
E.N. Pistikopoulos
3.1 Introduction
67(1)
3.2 Free-Radical Homopolymerization
68(9)
3.2.1 Kinetic Modeling
68(1)
3.2.2 Diffusion-Controlled Reactions
69(2)
3.2.2.1 Fickian Description of Reactant Diffusion
71(1)
3.2.2.2 Free-Volume Theory
72(1)
3.2.2.3 Chain Length Dependent Rate Coefficients
73(2)
3.2.2.4 Combination of the Free-Volume Theory and Chain Length Dependent Rate Coefficients
75(1)
3.2.2.5 Fully Empirical Models
76(1)
3.3 Free-Radical Multicomponent Polymerization
77(3)
3.3.1 Overview
77(1)
3.3.2 Pseudo-Homopolymerization Approximation
78(2)
3.3.3 Polymer Composition
80(1)
3.4 Modeling of Polymer Molecular Properties
80(10)
3.4.1 Molecular Weight Distribution
80(10)
3.5 A Practical Approach - SAN Bulk Polymerization
90(7)
3.5.1 Model
90(1)
3.5.1.1 Kinetic Diagram
90(1)
3.5.1.2 Mass Balances
91(1)
3.5.1.3 Diffusion Limitations
92(2)
3.5.1.4 Pseudo-Homopolymerization Approximation
94(1)
3.5.2 Illustrative Results
95(2)
3.6 Conclusions
97(8)
4 Modeling and Control of Proton Exchange Membrane Fuel Cells
105(32)
C. Panos
K. Kouramas
M.C. Georgiadis
E.N. Pistikopoulos
4.1 Introduction
105(3)
4.2 Literature Review
108(1)
4.3 Motivation
109(4)
4.3.1 Reactant Flow Management
112(1)
4.3.2 Heat and Temperature Management
112(1)
4.3.3 Water Management
113(1)
4.4 PEM Fuel Cell Mathematical Model
113(15)
4.4.1 Cathode
114(3)
4.4.2 Anode
117(2)
4.4.3 Anode Recirculation
119(1)
4.4.4 Fuel Cell Outlet
120(1)
4.4.5 Membrane Hydration Model
120(2)
4.4.6 Electrochemistry
122(1)
4.4.7 Thermodynamic Balance
123(2)
4.4.8 Air Compressor and DC Motor Model
125(1)
4.4.9 DC Motor
126(1)
4.4.10 Cooling System
127(1)
4.5 Reduced Order Model
128(4)
4.6 Concluding Remarks
132(5)
5 Modeling of Pressure Swing Adsorption Processes
137(36)
E.S. Kikkinides
D. Nikolic
M.C. Georgiadis
5.1 Introduction
137(7)
5.2 Model Formulation
144(19)
5.2.1 Adsorbent Bed Models
144(1)
5.2.2 Single-Bed Adsorber
145(1)
5.2.3 Adsorption Layer Model
146(1)
5.2.3.1 General Balance Equations
146(1)
5.2.3.2 Mass Balance
147(1)
5.2.3.3 Heat Balance
147(1)
5.2.3.4 Momentum Balance
148(1)
5.2.3.5 Equation of State
148(1)
5.2.3.6 Thermophysical Properties
148(1)
5.2.3.7 Axial Dispersion
148(1)
5.2.3.8 Transport Properties
149(1)
5.2.3.9 Boundary Conditions
149(1)
5.2.4 Adsorbent Particle Model
150(1)
5.2.4.1 General Mass Balance Equations
150(1)
5.2.4.2 Local Equilibrium
151(1)
5.2.4.3 Linear Driving Force (LDF)
152(1)
5.2.4.4 Surface Diffusion
152(1)
5.2.4.5 Pore Diffusion
153(1)
5.2.4.6 Gas-Solid Phase Equilibrium Isotherms
154(3)
5.2.5 Gas Valve Model
157(1)
5.2.6 The Multibed PSA Model
158(1)
5.2.7 The State Transition Network Approach
158(4)
5.2.8 Numerical Solution
162(1)
5.3 Case-Study Applications
163(4)
5.3.1 Simulation Run I
165(1)
5.3.2 Simulation Run II
165(1)
5.3.3 Simulation Run III
166(1)
5.4 Conclusions
167(6)
6 A Framework for the Modeling of Reactive Separations
173(30)
E.Y. Kenig
6.1 Introduction
173(1)
6.2 Reactive Separations
174(2)
6.3 Classification of Modeling Methods
176(2)
6.4 Fluid-Dynamic Approach
178(5)
6.5 Hydrodynamic Analogy Approach
183(5)
6.6 Rate-Based Approach
188(5)
6.7 Parameter Estimation and Virtual Experiments
193(3)
6.8 Benefits of the Complementary Modeling
196(3)
6.9 Concluding Remarks
199(4)
7 Efficent Reduced Order Dynamic Modeling of Complex Reactive and Multiphase Separation Processes Using Orthogonal Collocation on Finite Elements
203(36)
P. Seferlis
T. Damartzis
N. Dalaouti
7.1 Introduction
203(2)
7.2 NEQ/OCFE Model Formulation
205(13)
7.2.1 Conventional and Reactive Absorption and Distillation
207(6)
7.2.2 Multiphase Reactive Distillation
213(5)
7.3 Adaptive NEQ/OCFE for Enhanced Performance
218(2)
7.4 Dynamic Simulation Results
220(14)
7.4.1 Reactive Absorption of NOx
220(1)
7.4.1.1 Process Description
220(3)
7.4.1.2 Dynamic Simulation Results
223(2)
7.4.2 Ethyl Acetate Production via Reactive Distillation
225(1)
7.4.2.1 Process Description
225(2)
7.4.2.2 Dynamic Simulation Results
227(4)
7.4.3 Butyl Acetate Production via Reactive Multiphase Distillation
231(1)
7.4.3.1 Process Description
231(1)
7.4.3.2 Dynamic Simulation Results
232(2)
7.5 Epilog
234(5)
8 Modeling of Crystallization Processes
239(48)
A. Abbas
J. Romagnoli
D. Widenski
8.1 Introduction
239(1)
8.2 Background
240(3)
8.2.1 Crystallization Methods
241(1)
8.2.1.1 Recrystallization Methods
241(1)
8.2.2 Driving Force
242(1)
8.3 Solubility Predictions
243(8)
8.3.1 Empirical Approach
243(1)
8.3.2 Correlative Thermodynamic
244(1)
8.3.3 Predictive Thermodynamic
244(1)
8.3.3.1 Jouyban-Acree Model
245(1)
8.3.3.2 MOSCED Model
245(1)
8.3.3.3 NRTL-SAC Model
246(1)
8.3.3.4 UNIFAC Model
247(1)
8.3.3.5 Solubility and Activity Coefficient Relationship
247(1)
8.3.4 Solubility Examples
247(3)
8.3.5 Solution Concentration Measurement Process Analytical Tools
250(1)
8.4 Crystallization Mechanisms
251(5)
8.4.1 Nucleation
251(1)
8.4.1.1 Modeling Nucleation
252(2)
8.4.2 Growth and Dissolution
254(1)
8.4.3 Agglomeration and Aggregation
255(1)
8.4.4 Attrition
255(1)
8.5 Population, Mass, and Energy Balances
256(8)
8.5.1 Population Balance
256(1)
8.5.2 Solution Methods
257(1)
8.5.2.1 Method of Moments
257(1)
8.5.2.2 Discretization Method
258(6)
8.5.3 Mass and Energy Balances
264(1)
8.6 Crystal Characterization
264(2)
8.6.1 Crystal Shape
264(1)
8.6.2 Crystal Size
265(1)
8.6.3 Crystal Distribution
265(1)
8.6.4 Particle Measurement Process Analytical Tools
266(1)
8.7 Solution Environment and Model Application
266(4)
8.7.1 Simulation Environment
266(1)
8.7.2 Experimental Design
267(1)
8.7.3 Parameter Estimation
268(1)
8.7.4 Validation
269(1)
8.8 Optimization
270(6)
8.8.1 Example 1: Antisolvent Feedrate Optimization
270(4)
8.8.2 Example 2: Optimal Seeding in Cooling Crystallization
274(2)
8.9 Future Outlook
276(11)
9 Modeling Multistage Flash Desalination Process - Current Status and Future Development
287(32)
I.M. Mujtaba
9.1 Introduction
287(2)
9.2 Issues in MSF Desalination Process
289(3)
9.3 State-of-the-Art in Steady-State Modeling of MSF Desalination Process
292(11)
9.3.1 Scale Formation Modeling
299(2)
9.3.1.1 Estimation of Dynamic Brine Heater Fouling Profile
301(1)
9.3.1.2 Modeling the Effect of NCGs
301(1)
9.3.1.3 Modeling of Environmental Impact
302(1)
9.4 State-of-the-Art in Dynamic Modeling of MSF Desalination Process
303(5)
9.5 Case Study
308(4)
9.5.1 Steady-State Operation
308(3)
9.5.2 Dynamic Operation
311(1)
9.6 Future Challenges
312(3)
9.6.1 Process Modeling
312(1)
9.6.2 Steady-State and Dynamic Simulation
313(1)
9.6.3 Tackling Environmental Issues
313(1)
9.6.4 Process Optimization
314(1)
9.7 Conclusions
315(4)
Part II Biological, Bio-Processing and Biomedical Systems
319(264)
10 Dynamic Models of Disease Progression: Toward a Multiscale Model of Systemic Inflammation in Humans
321(48)
J.D. Scheff
P.T. Foteinou
S.E. Calvano
S.F. Lowry
I.P. Androulakis
10.1 Introduction
321(1)
10.2 Background
322(6)
10.2.1 In-Silico Modeling of Inflammation
323(2)
10.2.2 Multiscale Models of Human Endotoxemia
325(2)
10.2.3 Data Collection
327(1)
10.3 Methods
328(12)
10.3.1 Developing a Multilevel Human Inflammation Model
328(1)
10.3.1.1 Identification of the Essential Transcriptional Responses
328(2)
10.3.1.2 Modeling Inflammation at the Cellular Level
330(5)
10.3.1.3 Modeling Inflammation at the Systemic Level
335(1)
10.3.1.4 Modeling Neuroendocrine-Immune System Interactions
336(2)
10.3.1.5 Modeling the Effect of Endotoxin Injury on Heart Rate Variability
338(2)
10.4 Results
340(20)
10.4.1 Transcriptional Analysis and Major Response Elements
340(3)
10.4.2 Elements of a Multilevel Human Inflammation Model
343(2)
10.4.3 Estimation of Relevant Model Parameters
345(2)
10.4.4 Qualitative Assessment of the Model
347(1)
10.4.4.1 Implications of Increased Insult
348(1)
10.4.4.2 Modes of Dysregulation of the Inflammatory Response
349(4)
10.4.4.3 The Emergence of Memory Effects
353(1)
10.4.4.4 Evaluation of Stress Hormone Infusion in Modulating the Inflammatory Response
354(6)
10.5 Conclusions
360(9)
11 Dynamic Modeling and Simulation for Robust Control of Distributed Processes and Bioprocesses
369(34)
A.A. Alonso
M.R. Garcia
C. Vilas
11.1 Introduction
369(3)
11.2 Model Reduction of DPS: Theoretical Background
372(5)
11.2.1 Model Reduction in the Context of the Finite Element Method
374(2)
11.2.1.1 Proper Orthogonal Decomposition
376(1)
11.2.1.2 Laplacian Spectral Decomposition
377(1)
11.3 Model Reduction in Identification of Bioprocesses
377(6)
11.3.1 Illustrative Example: Production of Gluconic Acid in a Tubular Reactor
378(1)
11.3.2 Observer Validation
379(4)
11.4 Model Reduction in Control Applications
383(14)
11.4.0.1 Model Equations
384(2)
11.4.1 Robust Control of Tubular Reactors
386(3)
11.4.1.1 Controller Synthesis
389(3)
11.4.1.2 Robust Control with a Finite Number of Actuators
392(2)
11.4.2 Real-Time Optimization: Multimodel Predictive Control
394(1)
11.4.2.1 Optimization Problem
395(1)
11.4.2.2 The Online Strategy
396(1)
11.5 Conclusions
397(6)
12 Model Development and Analysis of Mammalian Cell Culture Systems
403(38)
A. Kiparissides
M. Koutinas
E.N. Pistikopoulos
A. Mantalaris
12.1 Introduction
403(3)
12.2 Review of Mathematical Models of Mammalian Cell Culture Systems
406(4)
12.3 Motivation
410(3)
12.4 Dynamic Modeling of Biological Systems - An Illustrative Example
413(22)
12.4.1 First Principles Model Derivation
415(6)
12.4.2 Model Analysis
421(11)
12.4.3 Design of Experiments and Model Validation
432(3)
12.5 Concluding Remarks
435(6)
13 Dynamic Model Building Using Optimal Identification Strategies, with Applications in Bioprocess Engineering
441(28)
E. Balsa-Canto
J.R. Banga
M.R. Garcia
13.1 Introduction
441(2)
13.2 Parameter Estimation: Problem Formulation
443(4)
13.2.1 Mathematical Model Formulation
444(1)
13.2.2 Experimental Scheme and Experimental Data
444(1)
13.2.3 Cost Function
445(1)
13.2.4 Numerical Methods: Single Shooting vs. Multiple Shooting
446(1)
13.3 Identifiability
447(2)
13.4 Optimal Experimental Design
449(1)
13.4.1 Numerical Methods: The Control Vector Parameterization Approach
450(1)
13.5 Nonlinear Programming Solvers
450(3)
13.6 Illustrative Examples
453(10)
13.6.1 Modeling of the Microbial Growth
453(4)
13.6.2 Modeling the Production of Gluconic Acid in a Fed-Batch Reactor
457(6)
13.7 Overview
463(6)
14 Multiscale Modeling of Transport Phenomena in Plant-Based Foods
469(24)
Q.T. Ho
P. Verboven
B.E. Verlinden
E. Herremans
B.M. Nicolai
14.1 Introduction
469(1)
14.2 Length Scales of Biological Materials
470(2)
14.3 Multiscale Modeling of Transport Phenomena
472(4)
14.3.1 Mass Transport Fundamentals
472(2)
14.3.2 Multiscale Transport Phenomena
474(1)
14.3.2.1 Macroscale Approach
474(1)
14.3.2.2 Microscale Approach
474(1)
14.3.2.3 Kinetic Modeling
475(1)
14.3.2.4 Multiscale Model
476(1)
14.4 Numerical Solution
476(4)
14.4.1 Geometrical Model
476(2)
14.4.2 Discretization
478(2)
14.5 Case Study: Application of Multiscale Gas Exchange in Fruit
480(5)
14.5.1 Macroscale Model
480(2)
14.5.2 Microscale Model
482(1)
14.5.3 O2 Transport Model
482(1)
14.5.4 CO2 Transport Model (Lumped CO2 Transport Model)
483(2)
14.6 Conclusions and Outlook
485(8)
15 Synthetic Biology: Dynamic Modeling and Construction of Cell Systems
493(52)
T.T. Marquez-Lago
M.A. Marchisio
15.1 Introduction
493(1)
15.2 Constructing a Model with Parts
494(24)
15.2.1 General Nomenclature
494(1)
15.2.1.1 Parts and Devices
494(2)
15.2.1.2 Common Signal Carriers
496(1)
15.2.1.3 Pools and Fluxes
497(3)
15.2.2 Part Models
500(1)
15.2.2.1 Promoters
500(4)
15.2.2.2 Ribosome-Binding Sites
504(4)
15.2.2.3 Coding Regions
508(1)
15.2.2.4 Noncoding DNA
509(2)
15.2.2.5 Small RNA
511(1)
15.2.2.6 Terminator
511(1)
15.2.3 Introducing Parts and Fluxes into Deterministic Equations
512(6)
15.3 Modeling Regimes and Simulation Techniques
518(14)
15.3.1 Deterministic or Stochastic Modeling?
519(1)
15.3.1.1 Deterministic Regime
519(1)
15.3.1.2 Stochastic Regime
520(2)
15.3.2 Stochastic Simulation Algorithms
522(1)
15.3.2.1 Exact Algorithms
522(5)
15.3.2.2 Coarse-Grained Methods
527(5)
15.4 Application
532(9)
15.4.1 The Repressilator
533(8)
15.5 Conclusions
541(4)
16 Identification of Physiological Models of Type 1 Diabetes Mellitus by Model-Based Design of Experiments
545(38)
F. Galvanin
M. Barolo
S. Macchietto
F. Bezzo
16.1 Introduction
546(2)
16.1.1 Glucose Concentration Control Issues
547(1)
16.2 Introducing Physiological Models
548(1)
16.3 Identifying a Physiological Model: The Need for Experiment Design
548(2)
16.4 Standard Clinical Tests
550(1)
16.5 A Compartmental Model of Glucose Homeostasis
551(1)
16.6 Model Identifiability Issues
552(4)
16.6.1 A Discussion on the Identifiability of the Hovorka Model
554(2)
16.7 Design of Experiments Under Constraints for Physiological Models
556(4)
16.7.1 Design Procedure
558(2)
16.8 Design of Experimental Protocols
560(3)
16.8.1 Modified OGTT (mOGTT)
561(1)
16.8.1.1 Effect of the Number of Samples
562(1)
16.9 Dealing with Uncertainty
563(9)
16.9.1 Online Model-Based Redesign of Experiments
565(1)
16.9.2 Model-Based Design of Experiment with Backoff (MBDoE-B)
566(1)
16.9.2.1 Backoff Application
567(2)
16.9.3 Effect of a Structural Difference Between a Model and a Subject
569(3)
16.10 Conclusions
572(11)
Index 583
Efstratios N. Pistikopoulos is a Professor of Chemical Engineering at Imperial College London and Director of its Centre for Process Systems Engineering (PSE). He graduated in Chemical Engineering from Aristotle University of Thessaloniki, Greece and was awarded a PhD from Carnegie Mellon University, USA. He has authored/ co-authored over 200 publications, holds editorial positions on several editorial boards and has been involved in over 50 major research projects and contracts. Prof. Pistikopoulos is co-founder and Director of two successful spin-off companies stemming from his research at Imperial, Process Systems Enterprise (PSE) Limited and Parametric Optimization Solutions (PAROS) Limited and consults widely to numerous process industry companies.

Michael C. Georgiadis is Associate Professor in the Department of Engineering Informatics and Telecommunications at University of Western Macedonia, Greece and honorary research fellow in the Centre for Process Systems Engineering at Imperial College London. He was manager of academic business development for Process Systems Enterprise Ltd. He obtained his Chemical Engineering Diploma from Aristotle University of Thessaloniki, Greece and an MSc and PhD From Imperial College London. Dr. Georgiadis has authored over 55 papers and two books. He has a long experience in the management and participation of more than 20 collaborative research contracts and projects and consults to Process Systems Enterprise Ltd and Parametric Optimization Solutions Ltd.

Vivek Dua is a Lecturer in the Department of Chemical Engineering at University College London. He holds a degree in Chemical Engineering from Panjab University, Chandigarh, India and MTech in chemical engineering from the Indian Institute of Technology, Kanpur. He joined Kinetics Technology India Ltd. as a Process Engineer before moving to Imperial College London, where he obtained his PhD in Chemical Engineering. He was an Assistant Professor in the Department of Chemical Engineering at Indian Institute of Technology, Delhi before joining University College London. He is a co-founder of Parametric Optimization Solutions (PAROS) Ltd.

Process Systems Enterprise (PSE), provider of the gPROMS advanced process simulation and modelling environment, is the 2007 winner of the Royal Academy of Engineering's MacRobert Award. The award, the UK's most prestigious for engineering, recognises the successful development of innovative ideas. The PSE team was presented with the MacRobert gold medal by HRH Prince Philip.