university politehnica of bucharest - doctor honoris causa
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University Politehnica of Bucharest - Doctor Honoris Causa. Professor Stratos Pistikopoulos FREng. Outline. A brief introduction Chemical Engineering Process Systems Engineering On-going research areas & projects Multi-parametric programming & control. Stratos Pistikopoulos. - PowerPoint PPT PresentationTRANSCRIPT
University Politehnica of Bucharest -
Doctor Honoris Causa
Professor Stratos Pistikopoulos FREng
Outline
A brief introduction
Chemical Engineering
Process Systems Engineering
On-going research areas & projects
Multi-parametric programming & control
Stratos Pistikopoulos
Diploma (Chem Eng) AUTh, 1984 PhD (Chem Eng) CMU, 1988 1991 – Imperial College London; since 1999 Professor of
Chemical Engineering 2002 - 2009 Director, Centre for Process Systems
Engineering (CPSE), Imperial 2009 - 2013 Director of Research, Chem Eng, Imperial 2009 - 2013 Member, Faculty of Engineering Research
Committee, Imperial
Stratos Pistikopoulos
Process systems engineering Modelling, optimization & control Process networks, energy & sustainable systems,
bioprocesses, biomedical systems 250+ major journal publications, 8 books, 2 patents h-index 40; ~5000 citations
Stratos Pistikopoulos FREng, FIChemE (Co-) Editor, Comp & Chem Eng Co-Editor, Book Series (Elsevier & Wiley) Editorial Boards – I&ECR, JOGO, CMS Founder/Co-founder & Director – PSE Ltd, ParOS 2007 – co-recipient Mac Robert Award, RAEng 2008 – Advanced Investigator Award, ERC 2009 – Bayer Lecture, CMU 2012 – Computing in Chemical Engineering Award, CAST,
AIChE 2014 – 21st Professor Roger Sargent Lecture, Imperial
Chemical Engineering
Emerging Chemical Engineering
Relatively young[er] profession (societies founded in early part of 19th century, Manchester, UCL, Imperial - 1880s; MIT 1888)
(Most likely the) most versatile engineering profession (strong societies & academic programmes, highly-paid in manufacturing, business, banking, consulting)
Central discipline towards addressing societal grand challenges (energy & the environment/sustainability, health & the bio-(mics) ‘revolution’, Nano-engineering, Info-’revolution’, central to almost all Top 10 emerging technologies for 2012 World Economic Forum!)
Multi-scale & multi-discipline chemical engineering
Evolution of Chemical Engineering
Recognition of length and time scales
Evolution of Chemical Engineering
Length-scale
Time-scale
Factors Energy (algae, energy-based metabolic engineering & optimisation)
Product(quality, formulation, quantity)
Control(model-basedInformation pathways)
Transport(MolecularDesign of Nanoparticles)
Only Chemical Engineering integrates TIME, LENGTH, FACTORS (input/output)
Chemical Engineering - research
Research .. – strong core chemical engineering, new opportunities in nano-driven chemical engineering, biochemical and biomedical-driven chemical engineering, energy/sustainability-driven chemical engineering, info-driven chemical engineering
Interactions/interfaces with chemistry, materials, medicine, biology, computing/applied math & beyond – molecular level, nano-materials, nano/micro-reaction, ‘micro-human’, carbon dioxide conversion, bio-energy, resource efficiency & novel manufacturing, from ‘mind to factory’, systems of systems, ...
Chemical Engineering – a model
CoreMulti-scale
Understanding& Modelling
Chemical Engineering – a model
CoreMulti-scale
Understanding& Modelling
Simulation/Optimization
Measurements/Visualization/
Analytics
Design/Products &Processes
Properties/Transport/Reaction/
Separation
Experiments/Validation
Chemical Engineering – a model
Bio & Medical driven
Chemical Engineering
Energy/Sustainability
ChemicalEngineering
Nano-ChemicalEngineering
Molecular & Materials/Product
Chemical Engineering
CoreMulti-scale
Understanding& Modelling
Simulation/Optimization
Measurements/Visualization/
Analytics
Design/Products &Processes
Properties/Transport/Reaction/
Separation
Experiments/Validation
Chemical Engineering – a model
Bio & Meddriven
Chemical Engineering
Energy/Sustainability
ChemicalEngineering
Nano- &Multi-scale Chemical
Engineering
Molecular/MaterialsChemical Engineering
CoreMulti-scale
Understanding& Modelling
Materials
AnalyticalSciences
Systems
Transport&
Separation
Reaction&
Catalysis
Outline
A brief introduction
Chemical Engineering
Process Systems Engineering
On-going research areas & projects
Multi-parametric programming & control
Process Systems Engineering
Process Systems Engineering
Scientific discipline which focuses on the ‘study & development of theoretical approaches, computational techniques and computer-aided tools for modelling, analysis, design, optimization and control of complex engineering & natural systems – with the aim to systematically generate and develop products and processes across a wide range of systems involving chemical and physical change; from molecular and genetic information and phenomena, to manufacturing processes, to energy systems and their enterprise-wide supply chain networks’
PSE – brief historical overview
Relatively ‘new’ area in chemical engineering – started in the sixties/early seventies [Roger Sargent, Dale Rudd, Richard Hughes, and others & their academic trees]
Chemical Engineering – around 1890+ [MIT, UCL, Imperial]
AIChE - 1908; IChemE - 1922
PSE – brief historical overview
Relatively ‘new’ area in chemical engineering – started in the sixties/early seventies [Roger Sargent, Dale Rudd, Richard Hughes, and others & their academic trees]
Key historical dates – 1961 the term introduced [special volume of AIChE Symposium Series]; 1964 first paper on SPEEDUP [simulation programme for the economic evaluation and design of unsteady-state processes]; 1968 first textbook ‘Strategy of Process Engineering’ by Rudd & Watson (Wiley); 1970 CACHE Corporation; 1977 CAST division of AIChE; 1977 Computers & Chemical Engineering Journal
PSE – brief historical overview
1980s – FOCAPD 1980; PSE 1982; CPC, FOCAPO
Early 90s – ESCAPE series
Significant growth
Centres of excellence & critical mass – CMU, Purdue, UMIST, Imperial, DTU, MIT, others around the world (US, Europe, Asia – Japan, Singapore, Korea,
China, Malaysia)
PSE – Current Status
Well recognized field within chemical engineering
PSE academics in many [most?] chemical engineering departments
Undergraduate level – standard courses [& textbooks] on process analysis, process design, process control, optimization, etc
Research level – major activity & strong research programmes [US & Canada, Europe, Asia, Latin America, Australia]
PSE – Current Status
Well established global international events & conferences
Highly respected journals, books & publications
Strong relevance to & acceptance by industry- across wide range of sectors [from oil & gas to chemicals, fine chemicals & consumer goods, ..]
PSE software tools – essential in industry & beyond [simulation, MPC, optimization, heat integration, etc – PSE linked companies]
PSE – impact Training & education Significant research advances
process design process control process operations numerical methods & optimization [software & other] tools
Beyond chemical engineering .. [?]
‘Traditional’ PSE
PSE Core Mathematical Modelling Process Synthesis Product & Process Design Process Operations Process Control Numerical Methods & Optimization
PSE Core Recognition of length and time scales
From nano-scale (molecular)
to micro-scale (particles, crystals)
to meso-scale (materials, equipment, products)
to mega-scale (supply chain networks, environment)
PSE evolution ..
PSE Core Recognition of length and time scales
From nano-scale (molecular)
to micro-scale (particles, crystals)
to meso-scale (materials, equipment, products)
to mega-scale (supply chain networks, environment)
Multi-scale Modelling
PSE evolution ..
Product Value Chain (Marquardt; Grossmann et al)
Recognition of length and time scales
PSE evolution ...
Multi-scaleModelling
PSE evolution ...
MultiscaleModelling
simulation
control
optimization
Product/processdesign
synthesis
Recognition of length and time scales From nano-scale (molecular)
to micro-scale (particles, crystals)
to meso-scale (materials, equipment, products)
to mega-scale (supply chain networks, environment) Core, generic enabling technology provider to other domains
molecular genomic biological materials energy automation plants oilfields global supply chains
Multi-scale process systems engineering
PSE evolution
Multi-scale Process Systems Engineering
Biological& Biomedical
Systems Engineering
Energy/SustainabilitySystems
Engineering
Supply ChainSystems
Engineering
Multi-scale Modelling
MolecularSystems
Engineering
simulation
control
optimization
Product/processdesign
synthesis
Multi-scale PSE
PSE Core Domain-driven PSE Problem-centric PSE
PSE Core
Multi-scale Modelling Multi-scale Optimization Product & Process Design Process Operations Control & Automation
Domain-driven PSE
Molecular Systems Engineering Materials Systems Engineering Biological Systems Engineering Energy Systems Engineering
Problem-centric PSE
Environmental systems engineering Safety systems engineering Manufacturing supply chains
Multi-scale Process Systems Engineering
Biological& Biomedical
Systems Engineering
Energy/SustainabilitySystems
Engineering
Supply ChainSystems
Engineering
Multi-scale Modelling
MolecularSystems
Engineering
simulation
control
optimization
design
synthesis
Multi-scale Process Systems Engineering leads to ..
Biological& Biomedical
Systems Engineering
Energy/SustainabilitySystems
Engineering
Supply ChainSystems
Engineering
Multi-scale Modelling
MolecularSystems
Engineering
simulation
control
optimization
design
synthesis
CONCEPT OPERATIONDESIGNDetailed design of complex
equipment
Process flowsheeting
Optimization of plant and
operating procedures
Process developmen
t
Operationaloptimization
TC
A
PlantTroubleshooting/
Safety
Model-based
automation
Model Based Innovation across the Process Lifecycle
Process Systems Engineering.. provides the ‘scientific glue’ within
chemical engineering (Perkins, 2008)
Bio-drivenChemical
Engineering
Energy -drivenChemical
Engineering
Multi-scaleChemical
Engineering
ProcessSystems
Engineering
MolecularDriven
ChemicalEngineering
Materials
Analytics/Experimental
Properties
Reactionengineering
TransportPhenomena
Process Systems Engineering‘systems thinking & practice’ – essential to address societal grand challenges
Health
EnergySustainable
Manufacturing
Systems Engineering
Nano - materials
simulation
control
optimization
design
synthesis
Outline
A brief introduction
Chemical Engineering
Process Systems Engineering
On-going research areas & projects
Multi-parametric programming & control
Research Group - research areas & current projects
Acknowledgements Funding
EPSRC - GR/T02560/01, EP/E047017, EP/E054285/1 EU - MOBILE, OPTICO, PRISM, PROMATCH, DIAMANTE, HY2SEPS, IRSES
CPSE Industrial Consortium, KAUST Air Products
People J. Acevedo, V. Dua, V. Sakizlis, P. Dua, N. Bozinis, P. Liu, N. Faisca, K.
Kouramas, C. Panos, L. Dominguez, A. Voelker, H. Khajuria, M. Wittmann-Hohlbein, H. Chang
P. Rivotti, A. Krieger, R. Lambert, E. Pefani, M. Zavitsanou, E. Velliou, G. Kopanos, A. Manthanwar, I. Nascu, M. Papathanasiou, N. Diangelakis, M. Sun, R. Oberdieck
John Perkins, Manfred Morari, Frank Doyle, Berc Rustem, Michael Georgiadis
Imperial & ParOS R&D Teams, Tsinghua BP Energy Centre
Current Research Focus Overview
Multi-parametric programming & Model Predictive Control [MPC]
Energy & Sustainability (driven) Systems Engineering
Biomedical Systems Engineering
Energy and Sustainability (driven) Systems
Synthesis and Design Design of micro-CHP systems for residential applications Design of poly-generation systems Long-term design and planning of general energy systems under
uncertainty
Operations and control Scheduling under uncertainty of micro-CHP systems for residential
applications Supply chain optimization of energy systems Integration of design and control for energy systems – fuel cells,
CHPs Integration of scheduling and control of energy systems under
uncertainty
Biomedical Systems Engineering
Leukaemia – Development of optimal protocols for chemotherapy drug delivery for:
Acute Myeloid Leukaemia (AML) Chronic Lymphocytic Leukaemia (CLL)
Experimental, modelling and optimization activity Anaesthesia & Diabetes
Emphasis on modelling and control in volatile anaesthesia the artificial pancreas
Collaboration with Prof. Mantalaris and Dr. Panoskaltsis Collaboration with Prof Frank Doyle, UC Santa-Barbara
Multi-Parametric Programming & Explicit MPC
a progress report
Professor Stratos Pistikopoulos FREng
Outline
Key concepts & historical overview Recent developments in multi-parametric
programming and mp-MPC
MPC-on-a-chip applications
What is On-line Optimization?
MODEL/OPTIMIZER
SYSTEM
Data - Measurements
Control Actions
What is Multi-parametric Programming?
Given: a performance criterion to minimize/maximize a vector of constraints a vector of parameters
s
n
u
u
x
xug
xufxz
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),(min)(
What is Multi-parametric Programming?
Given: a performance criterion to minimize/maximize a vector of constraints a vector of parameters
Obtain: the performance criterion and the optimization
variables as a function of the parameters the regions in the space of parameters where these
functions remain valid
s
n
u
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x
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xufxz
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Multi-parametric programming
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(2) Critical Regions
(1) Optimal look-up function
Obtain optimal solution u(x) as a function of the parameters xObtain optimal solution u(x) as a function of the parameters x
Multi-parametric programming
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Multi-parametric programmingMulti-parametric Solution
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Multi-parametric programming
Only 4 optimization problems solved!Only 4 optimization problems solved!
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On-line Optimization via off-line Optimization
System State
Control Actions
OPTIMIZER
SYSTEM
POP
PARAMETRIC PROFILE
SYSTEM
System State
Control Actions
Function Evaluation!Function Evaluation!
Multi-parametric/Explicit Model Predictive Control
Compute the optimal sequence of manipulated inputs which minimizes
On-line re-planning: Receding Horizon Control
tracking error = output – reference
subject to constraints on inputs and outputs
tracking error = output – reference
subject to constraints on inputs and outputs
Compute the optimal sequence of manipulated inputs which minimizes
On-line re-planning: Receding Horizon Control
Multi-parametric/Explicit Model Predictive Control
Solve a QP at each time intervalSolve a QP at each time interval
Multi-parametric Programming Approach
State variables Parameters Control variables Optimization variables
MPC Multi-Parametric Programming problem
Control variables F(State variables)
Multi-parametric Quadratic ProgramMulti-parametric Quadratic Program
Explicit Control Law
-2 -1.5 -1 -0.5 0 0.5 1 1.5 2-2
-1.5
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-0.5
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Multi-parametric Controllers
SYSTEM
Parametric Controller
Optimization Model
(2) Critical Regions
(1) Optimal look-up function
MeasurementsControl Action
Input Disturbances
System Outputs
Explicit Control Law Eliminate expensive, on-line
computations Valuable insights !
MPC-on-a-chip!
A framework for multi-parametric programming & MPC (Pistikopoulos 2008, 2009)
‘High-Fidelity’ Dynamic Model
Model Reduction Techniques
System Identification
Modelling/ Simulation
Identification/ Approximation
Model-Based Control & Validation
Closed-LoopControl System Validation
Extraction of Parametric Controllers
u = u ( x(θ) )
‘Approximate Model’
Multi-Parametric Programming (POP)
‘High-Fidelity’ Dynamic Model
Model Reduction Techniques
System Identification
Modelling/ Simulation
Identification/ Approximation
Model-Based Control & Validation
Closed-LoopControl System
Validation
Extraction of Parametric Controllersu = u ( x(θ) )
‘Approximate Model’
Multi-Parametric Programming
(POP)
REAL SYSTEM EMBEDDED CONTROLLEROn-line Embedded
Control:
Off-line Robust Explicit Control Design:
A framework for multi-parametric programming and MPC (Pistikopoulos 2010)
Key milestones-Historical Overview Number of publications
2002 Automatica paper - citations [Sep 2014]: 900+ WoS; 1200+ Scopus; 1650+ Google Scholar
Multi-parametric programming – until 1992 mostly analysis & linear models
Multi-parametric/explicit MPC – post-2002 much wider attention
Multi-Parametric Programming
Multi-Parametric MPC &
applications
Pre-1999 >100 0 Post-1999 ~70 250+
AIChE J.,Perspective (2009)
Multi-parametric Programming Theory
mp-LP Gass & Saaty [1954], Gal & Nedoma [1972], Propoi [1975], Adler and Monterio [1992], Gal [1995], Acevedo and Pistikopoulos[1997], Dua et al [2002], Pistikopoulos et al [2007]
mp-QP Townsley [1972], Propoi [1978], Best [1995], Dua et al [2002], Pistikopoulos et al [2002,2007]
mp-NLP Fiacco [1976],Kojima [1979], Bank et al [1983], Fiacco [1983], Fiacco & Kyoarisis [1986], Acevedo & Pistikopoulos [1996], Dua and Pistikopoulos [1998], Pistikopoulos et al [2007]
mp-DO Sakizlis et al.[2002], Bansal [2003], Sakizlis et al [2005], Pistikopoulos et al [2007]
mp-GO Fiacco [1990], Dua et al [1999,2004], Pistikopoulos et al [2007]
mp-MILP Marsten & Morin [1975], Geoffrion & Nauss [1977], Joseph [1995], Acevedo & Pistikopoulos [1997,1999], Dua & Pistikopoulos[ 2000]
mp-MINLP McBride & Yorkmark [1980], Chern [1991], Dua & Pistikopoulos [1999], Hene et al [2002], Dua et al [2002]
Multi-parametric/Explicit Model Predictive Control Theory
mp-MPCPistikopoulos [1997, 2000], Bemporad, Morari, Dua & Pistikopoulos [2000], Sakizlis & Pistikopoulos [ 2001], Tondel et al [2001], Pistikopoulos et al [2002], Bemporad et al [2002], Johansen and Grancharova [2003], Sakizlis et al [2003], Pistikopoulos et al [2007]
mp-Continuous MPC
Sakizlis et al [2002], Kojima & Morari[ 2004], Sakizlis et al [2005], Pistikopoulos et al [2007]
Hybrid mp-MPC Bemporad et al [2000], Sakizlis & Pistikopoulos [2001], Pistikopoulos et al [2007]
Robust mp-MPC
Kakalis & Pistikopoulos [2001], Bemporad et al [2001], Sakizlis et al [2002], Sakizlis & Pistikopoulos [2002], Sakizlis et al [2004], Olaru et al [2005], Faisca et al [2008]
mp-DP Nunoz de la Pena et al [2004],Pistikopoulos et al [2007],Faisca et al [2008]
mp-NMPC Johansen [2002], Bemporad [2003], Sakizlis et al [2007], Dobre et al [2007], Narciso & Pistikopoulos [2009]
68
Patented Technology
Improved Process Control
European Patent No EP1399784, 2004
Process Control Using Co-ordinate Space
United States Patent No US7433743, 2008
Multi-parametric programming & Model Predictive Control [MPC]
Theory of multi-parametric programming Multi-parametric mixed integer quadratic programming [mp-MIQP] Multi-parametric dynamic optimization [continuous-time, mp-DO] Multi-parametric global optimization
Theory of multi-parametric/explicit model predictive control [mp-MPC] Explicit robust MPC of hybrid systems Explicit MPC of continuous time-varying [dynamic] systems Explicit MPC of periodic systems Moving Horizon Estimation & mp-MPC
Multi-parametric programming & Model Predictive Control [MPC] – cont’d
Framework for multi-parametric programming & control Model approximation [from high fidelity models to the design of
explicit MPC controllers] Software development, prototype & demonstrations [for teaching &
research]
Application areas Fuel cell energy system – experimental/laboratory Car system control – prototypes/laboratory Energy systems [CHP and micro-CHP] Bio-processing [continuous production & control of monoclonal
antibodies] Pressure Swing Absorption [PSA] and hybrid systems Biomedical Systems
MPC-on-a-chip Applications – Recent Developments
Process Control Air Separation (Air Products)Hybrid PSA/Membrane Hydrogen Separation
(EU/HY2SEPS, KAUST)
AutomotiveActive Valve Train Control (Lotus Engineering)
Energy SystemsHydrogen Storage (EU/DIAMANTE)Fuel Cell
MPC-on-a-chip Applications – Recent Developments
Biomedical Systems (MOBILE - ERC Advanced Grant Award)
Drug/Insulin, Anaesthesia and Chemotherapeutic Agents Delivery Systems
Imperial Racing GreenFuel cell powered Student Formula Car
Aeronautics (EPSRC)
(Multiple) Unmanned Air Vehicles – with Cranfield University
Small Air Separation Units (Air Products, Mandler et al,2006)
Enable advanced MPC for small separation units
Optimize performanceMinimize operating costsSatisfy product and equipment constraints
Parametric MPC ideally suited Supervises existing regulatory control Off-line solution with minimum on-line
load Runs on existing PLC Rapid installation compared to traditional
MPC
Advantages of Parametric MPC 5% increased throughput 5% less energy usage 90% less waste Installation on PLC in 1-day
Active Valve Train Control (Lotus Engineering, Kosmidis et al, 2006)
Active Valve Trains (AVT): Optimum combustion efficiency, Reduced
Emissions, Elimination of butterfly valve, Cylinder deactivation, Controlled auto-ignition (CAI), Quieter operation
Basic idea: Control System sends signal to valve This actuates piston attached to engine
valve Enables optimal control of valve timing
over entire engine rpm range
Challenges for the AVT control Nonlinear system dynamics: Saturation,
flow non-linearity, variation in fluid properties, non-linear opening of the orifices
Robustness to various valve lift profiles Fast dynamics and sampling times (0.1ms)
Multi-parametric Control of H2 Storage in Metal-Hydride Beds (EU-DIAMANTE, Georgiadis et al, 2008)
Tracking the optimal temperature profile Ensure economic storage – expressed by
the total required storage time Satisfy temperature and pressure
constraints
Optimal look-up table(Projected on the yt - ut plane)
1
1. 02
1. 04
1. 06
1. 08
1. 1
1. 12
0 100 200 300 400 500 600 700 800
ti me
Tf(z
=1)
Tf (z=1) wi th control l erTf (z=1) wi thout control l er
PEM Fuel Cell Unit
Collaborative work with Process Systems Design & Implementation Lab (PSDI) at CERTH - Greece
PI
PI
PI
H2O
Water
MassFlow
MassFlow
MassFlow
TE
TE
TE
PT
A
K
PDT
PTTE
TE PT
TE PT
M
TE TE
PT
VENT
VENT
Hydrator
HydratorRadiatorFilter
Electronic Load
N2
H2
Air
Unit Specifications Fuel Cell : 1.2kW Anode Flow : 5..10 lt/min Cathode Flow : 8..16 lt/min Operating Temperature : 65 – 75 °C Ambient Pressure
Control StrategyStart-up Operation Heat-up Stage : Control of coolant loopNominal Operation Control Variables :
Mass Flow Rate of Hydrogen & Air Humidity via Hydrators temperature Cooling system via pump regulation
Known Disturbance : Current
Unit Design : Centre For Research & Technology Hellas (CERTH)
(2) Critical Regions
(1) Optimal look-up function
PEM Fuel Cell System
mH2
mAir mcool
TYHydrators
Vfan
Tst HTst
PEM Fuel Cell Unit
79
80
81
82
Imperial Racing Green Car Student Formula Project
Control of Start-up/Shut-down of the FC
Traction Motion Control
Control & Acquisition System
FPGA(MPC-on-a-Chip)
Biomedical Systems (MOBILE ERC Advanced Grant)
Step 1: The sensor measures the glucose concentration from
the patient
Step 2: The sensor then inputs the data to the controller which analyses it and implements the
algorithm
Step 3: After analyzing the data the controller then signals
the pump to carry out the required action
Step 4: The Insulin Pump delivers the required dose to
the patient intravenously
Controller
Sensor
Patient
Insulin Pump
12
3 4
University Politehnica of Bucharest -
Doctor Honoris Causa
Mulțumesc!
University Politehnica of Bucharest -
Doctor Honoris Causa
Professor Stratos Pistikopoulos FREng