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 Factors Length-scale Energy (algae, energy-based metabolic engineering & optimisation) Transport (Molecular Design of Nanoparticles) Product (quality, formulation, quantity) Control (model-based Information pathways) Time-scale 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 Core Multi-scale Understanding & Modelling Chemical Engineering – a model Design/ Products & Processes Core Multi-scale UnderstandingMeasurements/ Experiments/ & Modelling Visualization/ Validation Analytics Properties/ Transport/ Reaction/ Separation Simulation/ Optimization Chemical Engineering – a model Molecular & Materials/Product Chemical Engineering Bio & Medical driven Chemical Design/ Simulation/ Engineering Products & Optimization Processes Core Multi-scale UnderstandingMeasurements/ Experiments/ & Modelling Visualization/ Validation Analytics Properties/ Transport/ Energy/ Nano-Chemical Reaction/ Sustainability Engineering Separation Chemical Engineering Chemical Engineering – a model Molecular/Materials Chemical Engineering Materials Systems Bio & Med driven Chemical Engineering Core Multi-scale Reaction Understanding Analytical & Modelling & Sciences Catalysis Nano- & Multi-scale Chemical Engineering Transport & Separation Energy/ Sustainability Chemical Engineering 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 industryacross 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 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) 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 Product Value Chain Recognition of length and time scales (Marquardt; Grossmann et al) PSE evolution ... Multi-scale Modelling PSE evolution ... simulation optimization Multiscale Modelling synthesis control Product/process design PSE evolution 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 Multi-scale Process Systems Engineering Molecular Systems Engineering simulation Biological & Biomedical Systems Engineering optimization Multi-scale Modelling synthesis control Product/process design Supply Chain Energy/Sustainability Systems Systems Engineering Engineering 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 Molecular Systems Engineering simulation Biological & Biomedical Systems Engineering optimization Multi-scale Modelling synthesis control design Supply Chain Systems Engineering Energy/Sustainability Systems Engineering Multi-scale Process Systems Engineering leads to .. Molecular Systems Engineering simulation Biological & Biomedical Systems Engineering optimization Multi-scale Modelling synthesis control design Supply Chain Systems Engineering Energy/Sustainability Systems Engineering Model Based Innovation across the Process ModelLifecycle Operational based optimization Process flowsheeting automation Process developmen t CONCEPT Detailed design of complex equipment DESIGN OPERATION A Optimization of plant and operating procedures Plant TC Troubleshooting/ Safety Process Systems Engineering .. provides the ‘scientific glue’ within chemical engineering (Perkins, 2008) Molecular Driven Chemical Engineering Bio-driven Chemical Engineering Materials Properties Process Systems Transport Engineering Analytics/ Phenomena Multi-scale Chemical Engineering Experimental Reaction engineering Energy -driven Chemical Engineering Process Systems Engineering ‘systems thinking & practice’ – essential to address societal grand challenges Nano - materials simulation Health optimization Systems Engineering synthesis Sustainable design Manufacturing control Energy 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. WittmannHohlbein, 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: Experimental, modelling and optimization activity Anaesthesia & Diabetes Acute Myeloid Leukaemia (AML) Chronic Lymphocytic Leukaemia (CLL) 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 Control Actions Data Measurements SYSTEM What is Multi-parametric Programming? Given: a performance criterion to minimize/maximize a vector of constraints a vector of parameters z ( x) min f (u , x) u s.t. g (u , x) 0 x Rn u Rs What is Multi-parametric Programming? Given: u a performance criterion to minimize/maximize s.t. g (u, x) 0 a vector of constraints a vector of parameters x Rn Obtain: z ( x) min f (u, x) u R s 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 Multi-parametric programming (1) Optimal look-up function z ( x) min f (u , x) u s.t. g (u , x) 0 x Rn u R (2) Critical Regions s u (x) Obtain optimal solution u(x) as a function of the parameters x Multi-parametric programming Problem Formulation min 3 u1 8 u2 u1 ,u2 st. 1 5 8 4 1 1 4 u1 0 22 u 2 0 1 0 0 13 0 0 x1 20 0 1 x 2 121 0 0 8 0 10 x1 10 100 x 2 100 Multi-parametric programming Critical Regions 4 Feasible Region Fragments 100 CR001 CR002 CR003 CR004 80 60 40 x2x2 20 0 -20 -40 -60 -80 -100 -10 -8 -6 -4 -2 0 x1 x1 2 4 6 8 10 Multi-parametric programming U Multi-parametric Solution 1 0.0 3 1 6.7 1 1 5 0 x1 0 1 0 1 0.3 3 0 x 1 1.6 7 x 2 1.3 3 0 x 1 4.6 7 i f 0 1 2 100 1 0 100 1 0.1 1 5 8.6 5 1 0.0 3 1 6.7 1 1 0.0 4 5 x 1 7.5 0.7 3 0.0 3 x 1 5.5 10 i f x 1 0 0.2 6 0.0 3 x 7.5 2 2 0 100 1 1 0 100 0 0 x 1 0 1 0 x 1 3 2 0 0.0 5 x 1 1 1.8 0 0.0 6 x 9.8 2 1 0.0 4 5 7.5 x 1 1 0 5 if x 2 0 1 1 0 0 1 0.1 1 8.6 5 x if 1 0 1 10 x 2 0 1 1 0 0 Multi-parametric programming min 3u1 8u2 u st. 1 13 0 1 0 1 20 0 0 0 x 5 4 u 1 1 8 22 u2 0 1 x2 121 0 8 0 0 0 4 1 10 x1 10, 100 x2 100 4 Feasible Region Fragments 100 CR001 CR002 CR003 CR004 80 60 40 x2 20 0 -20 -40 -60 -80 -100 -10 -8 -6 -4 -2 0 x1 2 4 6 8 10 U 0.3 3 3 1.3 3 3 0.7 3 3 3 0.2 6 6 6 7 0 x1 1.6 6 6 7 1 4.6 6 6 7 0 x2 0.0 3 3 3 x1 5.5 7.5 0.0 3 3 3 3 x2 if if 0 1 0 x1 0 1 3 0 x2 if 0 0 0.0 5 1 2 8 x1 1 1.8 4 6 2 9.8 0 7 6 9 0.0 6 4 1 x2 if 1 1 1 0 0 0.0 3 1 2 5 6.7 1 8 7 5 0 5 x1 0 1 0 x2 1 1 0 0 1 100 1 1 1 1 0 0 0.1 1 5 3 8 5 8.6 5 3 8 5 6.7 1 8 7 5 0.0 3 1 2 5 0.0 4 5 4 5 4 5 7.5 x1 x 0 1 0 2 1 100 1 100 1 1 0 0.0 4 5 4 5 4 5 7.5 x1 5 0 x 1 2 100 1 1 0 0.1 1 5 3 8 5 8.6 5 3 8 5 x1 0 10 x 1 100 2 Only 4 optimization problems solved! On-line Optimization via off-line Optimization POP PARAMETRIC PROFILE OPTIMIZER Control Actions System State SYSTEM Control Actions System State SYSTEM Function Evaluation! Multi-parametric/Explicit Model Predictive Control Compute the optimal sequence of manipulated inputs which minimizes tracking error = output – reference subject to constraints on inputs and outputs On-line re-planning: Receding Horizon Control Multi-parametric/Explicit Model Predictive Control Compute the optimal sequence of manipulated inputs which minimizes Solve a QP at each time interval On-line re-planning: Receding Horizon Control Multi-parametric Programming Approach State variables Parameters Control variables Optimization variables MPC Multi-Parametric Programming problem Control variables F(State variables) Multi-parametric Quadratic Program Explicit Control Law 2 CR0 CR1 CR2 1.5 J ( x(t )) min xTt j |t x t j |t 0.01 ut2 j |t xTt 2 |t P x t 2 |t 1 ut |t , ut 1|t 1 j 0 0.7326 0.0861 0.0609 s.t x t j 1|t x t j |t 0.0064 ut j |t 0 . 1722 0 . 9909 2 ut j |t 2 j 1,2 x t |t x(t ) 0.5 x2 0 -0.5 -1 -1.5 -2 -2 -1.5 -1 -0.5 0 x1 0.7059 0.7083 0.2065 6 . 8355 6 . 8585 x t i f x t 0.7059 0.7083 0.2065 ut 2 i f 0.7059 0.7083 x t 0.2065 2 i f 0.7059 0.7083 x t 0.2065 0.5 1 1.5 2 Multi-parametric Controllers (1) Optimal look-up function Optimization Model (2) Critical Regions Parametric Controller Measurements Control Action SYSTEM System Outputs Input Disturbances Explicit Control Law MPC-on-a-chip! Eliminate expensive, on-line computations Valuable insights ! A framework for multi-parametric programming & MPC (Pistikopoulos 2008, 2009) Modelling/ Simulation Identification/ Approximation ‘High-Fidelity’ Dynamic Model System Identification Model Reduction Techniques ‘Approximate Model’ Model-Based Control & Validation Multi-Parametric Programming (POP) Extraction of Parametric Controllers u = u ( x (θ ) ) Closed-Loop Control System Validation A framework for multi-parametric programming and MPC (Pistikopoulos 2010) On-line Embedded Control: Off-line Robust Explicit Control Design: Modelling/ Simulation Identification/ Approximation EMBEDDED CONTROLLER REAL SYSTEM ‘High-Fidelity’ Dynamic Model Model Reduction Techniques System Identification ‘Approximate Model’ Model-Based Control & Validation Multi-Parametric Programming (POP) Extraction of Parametric Controllers u = u ( x(θ) ) Closed-Loop Control System Validation Key milestones-Historical Overview AIChE J.,Perspective (2009) Number of publications Multi-Parametric Programming Multi-Parametric MPC & applications Pre-1999 >100 0 Post-1999 ~70 250+ 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 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-MPC Pistikopoulos [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 mpMPC 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) Automotive Active Valve Train Control (Lotus Engineering) Energy Systems Hydrogen Fuel Cell Storage (EU/DIAMANTE) MPC-on-a-chip Applications – Recent Developments Biomedical Systems (MOBILE - ERC Advanced Grant Award) Drug/Insulin, Anaesthesia and Chemotherapeutic Agents Delivery Systems Imperial Racing Green Fuel 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 performance Minimize operating costs Satisfy 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 1.12 Tf(z=1) with controller Tf(z=1) without controller 1.1 Optimal look-up table (Projected on the yt - ut plane) Tf(z=1) 1.08 1.06 1.04 1.02 1 0 100 200 300 400 time 500 600 700 800 PEM Fuel Cell Unit PI MassFlow H2 PI N2 TE MassFlow Electronic Load PT TE VENT Hydrator PI TE PT TE PT A VENT PDT K Air MassFlow TE TE Water PT TE H2O PT TE M Hydrator Filter Radiator Collaborative work with Process Systems Design & Implementation Lab (PSDI) at CERTH - Greece PEM Fuel Cell Unit 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 PEM Fuel Cell System mH2 mAir mcool TYHydrators Vfan Control Strategy Start-up Operation Heat-up Stage : Control of coolant loop Nominal 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) Tst HTst (1) Optimal look-up function (2) Critical Regions 79 80 81 82 Imperial Racing Green Car Student Formula Project Control of Start-up/Shutdown of the FC Traction Motion Control FPGA (MPC-on-a-Chip) Control & Acquisition System 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 2 Sensor Controller 1 Patient Insulin Pump 3 Step 3: After analyzing the data the controller then signals the pump to carry out the required action 4 Step 4: The Insulin Pump delivers the required dose to the patient intravenously University Politehnica of Bucharest Doctor Honoris Causa Mulțumesc! University Politehnica of Bucharest Doctor Honoris Causa Professor Stratos Pistikopoulos FREng