Download - University Politehnica of Bucharest - Doctor Honoris Causa

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

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


CoreMulti-scale


Simulation/Optimization

Measurements/Visualization/

Analytics

Design/Products &Processes

Properties/Transport/Reaction/

Separation

Experiments/Validation


Bio & Medical driven


Energy/Sustainability

ChemicalEngineering

Nano-ChemicalEngineering

Molecular & Materials/Product


CoreMulti-scale


Simulation/Optimization

Measurements/Visualization/

Analytics

Design/Products &Processes

Properties/Transport/Reaction/

Separation

Experiments/Validation


Bio & Meddriven


Energy/Sustainability

ChemicalEngineering

Nano- &Multi-scale Chemical

Engineering

Molecular/MaterialsChemical Engineering

CoreMulti-scale


Materials

AnalyticalSciences

Systems

Transport&

Separation

Reaction&

Catalysis

Outline







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


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


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 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 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


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


Systems Engineering


Engineering

Supply ChainSystems

Engineering


MolecularSystems

Engineering

simulation

control

optimization

design

synthesis

Multi-scale Process Systems Engineering leads to ..


Systems Engineering


Engineering

Supply ChainSystems

Engineering


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






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

http://www.brandsoftheworld.com/download/?id=32298

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


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

R

R

0),( s.t.

),(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

u

x

xug

xufxz

R

R

0),( s.t.

),(min)(

Multi-parametric programming

s

n

u

u

x

xug

xufxz

R

R

0),( s.t.

),(min)(

)(xu

(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


1001001010

0

0

0

0

8

121

20

13

00

10

00

01

14

228

45

11

.

83min21 ,

21

2

1

2

1

21uu

xx

x

x

u

u

uu

st

Problem Formulation

Multi-parametric programmingCritical Regions

-10 -8 -6 -4 -2 0 2 4 6 8 10-100

-80

-60

-40

-20

0

20

40

60

80

100

x1

x2

4 Feasible Region Fragments

CR001

CR002CR003

CR004

x 2

x1

Multi-parametric programmingMulti-parametric Solution

100

10

65.8

10

01

11.01

8.9

8.11

06.00

05.00

100

5

5.7

10

01

045.01

13

0

01

00

100

100

10

5.7

71.6

65.8

10

10

01

045.01

031.01

115.01

5.7

5.5

03.026.0

03.073.0

100

100

10

5

71.6

10

10

01

01

031.01

67.14

67.1

033.1

033.0

2

1

2

1

2

1

2

1

2

1

2

1

2

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2

1

x

xif

x

x

x

x

ifx

x

x

x

ifx

x

x

x

ifx

x

U


Only 4 optimization problems solved!Only 4 optimization problems solved!

100100,1010

0

0

0

0

8

121

20

13

00

10

00

01

14

228

45

11

.

83min

21

2

1

2

1

21

xx

x

x

u

u

st

uuu

-10 -8 -6 -4 -2 0 2 4 6 8 10-100

-80

-60

-40

-20

0

20

40

60

80

100

x1

x2

4 Feasible Region Fragments

CR001

CR002CR003

CR004

100

10

65385.8

10

01

115385.01

80769.9

8462.11

0641.00

05128.00

100

5

5.7

10

01

0454545.01

13

0

01

00

100

100

10

5.7

71875.6

65385.8

10

10

01

0454545.01

03125.01

115385.01

5.7

5.5

03333.026667.0

0333.07333.0

100

100

10

5

71875.6

10

10

01

01

03125.01

6667.14

6667.1

0333.1

0333.0

2

1

2

1

2

1

2

1

2

1

2

1

2

1

2

1

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

U

if

if

if

if

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

-1

-0.5

0

0.5

1

1.5

2

x1

x2

CR0CR1CR2

2065.07083.07059.02

2065.07083.07059.02

2065.0

2065.0

7083.07059.0

7083.07059.08585.68355.6

t

t

tt

t

xif

xif

xifx

u

)( 1,2j 22

0064.0

0609.0

9909.01722.0

0861.07326.0s.t

01.0min))((

||

|||1

|2|2

1

0

2|||

, |1|

t

PtxJ

tttjt

tjttjttjt

ttT

ttj

tjttjtT

tjtuu tttt

xxu

uxx

xxuxx

Multi-parametric Controllers

SYSTEM

Parametric Controller

Optimization Model



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]

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

http://www.brandsoftheworld.com/download/?id=32298

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)



PEM Fuel Cell System

mH2

mAir mcool

TYHydrators

Vfan

Tst HTst

PEM Fuel Cell Unit

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



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Download - University Politehnica of Bucharest - Doctor Honoris Causa

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