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Advances in Optimization and its Applications in Process Industries. Lorenz T Biegler Department of Chemical Engineering Carnegie Mellon University Pittsburgh, PA 15213 July , 2012 . http://capd.cheme.cmu.edu. Chemical Engineering Department. Pittsburgh, PA. Carnegie Mellon Campus.

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Advances in Optimization and its Applications in Process Industries

Lorenz T Biegler

Department of Chemical Engineering

Carnegie Mellon University

Pittsburgh, PA 15213

July , 2012 

http://capd.cheme.cmu.edu


Pittsburgh pa

Chemical Engineering Department Industries

Pittsburgh, PA

Carnegie Mellon Campus


Center for Advanced Process Decision-making Industries

(CAPD) Faculty and Researchers

Jeff Siirola

Erik Ydstie

Ignacio Grossmann

Nick Sahinidis

Larry Biegler

PhD Students: 28 MS Students: 11

Post Docs: 7 Visitors: 10


Long term goal: from molecular to enterprise level Industries

CAPD Goals

  • Provide intellectual leadership on complex modeling, design and operational problems faced by process industries

  • Promote and Enhance PSE Science Base: optimization, control, computer science, systems engineering, business

Basic methodologies

Process modeling

Mathematical programming

Systems Engineering

Process control

Advanced computing

Areas of application

Process and product synthesis

Energy Systems

Supply chain optimization

Molecular Design

Systems Biology


Capd principal investigators

L. T. Biegler Industries

Nonlinear Programming and Parameter Estimation

Optimization of Differential Algebraic Systems

Nonlinear Optimization-based Control

I. E. Grossmann

Mixed Integer Nonlinear Programming for Process Synthesis

Planning and Scheduling of Batch and Continuous Processes

Design under Uncertainty

N. Sahinidis

Global Optimization Algorithms, and Software

Modeling of metabolic and signaling pathways

Design of environmentally benign chemicals

J. J. Siirola

Process Synthesis of Advanced Energy Systems

Synthesis of Nonideal Separation Systems

Product and Process Design

B. E. Ydstie

Adaptive and Robust Control Strategies

Thermodynamic Approaches to Process Control

Discrete Events and Scheduling

 Solar Cell Modeling

CAPD Principal Investigators


Optimal design of responsive process supply chains
Optimal Design of Responsive Process Supply Chains Industries

Ignacio Grossmann

Max: Net present value

Max: Responsiveness

(Expected Lead Time)

Supply chain: an integrated network of business units for the supply, production, distribution and consumption of the products.


Supply chain case study
Supply Chain Case Study Industries

  • Problem Size MINLP:

    • # of Discrete Variables: 215

    • # of Continuous Variables: 8126

    • # of Constraints: 14617

  • Solution Time:

    • Solver: GAMS/BARON

    • Direct Solution: > 2 weeks

    • Proposed Algorithm: ~ 4 hours


Decisions Industries:

Number and capacity of TLP/FPSO facilities

Installation schedule for facilities

Number of sub-sea/TLP wells to drill

Oil production profile over time

Optimal Development Planning under Uncertainty

  • Offshore oilfield having several reservoirs under uncertainty

  • Maximize the expected net present value (ENPV) of the project

Tarhan, Grossmann (2009)

facilities

TLP

FPSO

Reservoirs

wells

Uncertainty:

  • Initial productivity per well

  • Size of reservoirs

  • Water breakthrough time for reservoirs


Distribution of net present value oilfield planning
Distribution of Net Present Value IndustriesOilfield Planning

Deterministic Mean Value = $4.38 x 109

Multistage Stoch Progr = $4.92 x 109

=> 12% higher, more robust

Computation: Algorithm 1: 120 hrs; Algorithm 2: 5.2 hrs

Nonconvex MINLP: 1400 discrete vars, 970 cont vars, 8090 Constraints


Simulation based optimization
Simulation-based Optimization Industries

Nick Sahinidis

  • Goals:

    • Efficient optimization of complex chemical processes

    • Accurate solutions using function evaluations from high fidelity simulators

  • Challenges and solutions:

    • Lack of an algebraic model → Build surrogate models

    • Computationally costly simulations → Selectively choose a minimal data set

    • Often noisy function evaluations → Use regression surrogate models

    • Scarcity of fully robust simulations → Disaggregate the process

Process simulation

Optimization model

Function evaluation


Automated learning of algebraic models for optimization
Automated Learning of Algebraic Models for Optimization Industries

Process Simulation

Disaggregation and modeling

Optimization

Block 1

Model 1

Block 2

Model 2

Surrogate model generation using ALAMO

Algebraic optimization

Block 3

Model 3

True vs. Empirical

Error

Ideal Model

New

Model

Build simple and accurate models with a functional form tailored for an optimization framework

Locate

model

error

Data points

Complexity

New point

Error maximization

Rebuild model

Combine surrogate models along with design specs, heat/mass balances, logical constraints, etc. to formulate an algebraic optimization model

Iterative design of experiments

Model functional form


Co 2 capture case study
CO Industries2 Capture Case Study

Outlet gas

Solid feed

Minimize the increased cost of electricity

Maximize %CO2 removal ( )

Cooling

water

CO2 rich gas

CO2 rich solid outlet

Surrogate model

Simulation

Tradeoff:

Cost

vs. Environmental impact

Generate a low-complexity surrogate model of %CO2 removal as a function of reactor bed depth and cooling water flow from Aspen Custom Model runs

2. Surrogate model generation

3. Results: Pareto Analysis

1. Optimize a CO2 fluidized bed reactor


Process control research at cmu erik ydstie
Process Control Research at CMU IndustriesErik Ydstie

Research topics:

Solar Energy (Production processes and DSSC)

Dynamic modeling and Control of Supply Chains

Modeling and Control of Particulate Systems

Adaptive Control and Adaptive Optimization

Plant wide Simulation and Control

Process Automation and Safety

Fundamental Control Theory


Feed Forward Adaptive Control Industries

Applied to Propane Cracker - DOW Chemicals

  • Control Objectives:

  • Stabilize pressure (CV) in response to frequent disturbances

  • Optimally choose cheapest fuel

CV: Pressure

MV: Low-pressure Flow

DV1: Off-gas

DV2: Residue

DV3: Fuel flow to Propane cracker

  • PI Control (green)

  • PI with adaptive optimization (blue)

B Erik Ydstie, CMU


Plantwide Control Systems? IndustriesFrom Sand to Windshields(with Dr Yu Jiao PPG Inc, Glass Technology Research Center)

Silicate Sand

Soda-ash

Iron Oxide

++

8 flat glass plants

10 windshield lines

Accuracy of shape, color, distortion (optical properties) depend on mix, melting conditions in furnace and operation of the tin bath.


Results from Trial at Wichita Falls TX Industries

Conditioner temperature (KPI)

  • Yield improved by 3-5%

  • Excellent operator acceptance

  • Maintainable and expandable

  • Implemented on all PPG plants

  • $30-40M per year saving

Defects Measured


Process optimization l t biegler

Large Scale Nonlinear Programming Algorithms: Industriesprocess optimization for design, control and operations

Evolution of NLP Solvers:

Process OptimizationL. T. Biegler

SQP

rSQP

IPOPT

rSQP++

IPOPT 3.x

’80s: Flowsheet optimization

over 100 variables and constraints

’90s: Static Real-time optimization (RTO)

over 100 000 variables and constraints

’00s: Simultaneous dynamic optimization

over 1 000 000 variables and constraints

Object Oriented Codes to tailor structure, architecture to problems


Large scale optimization l t biegler

Grade Transitions - Polymer Processes Industries

Large-Scale Optimization (L. T. Biegler)

  • Periodic Adsorption Process Optimization

Simulated Moving Bed - Optimal Operation

CPU Time for optimization: 9.03 min

34098 variables, 34013 equations

Real-time Dynamic Optimization


Dynamic Optimization IndustriesProblem

s.t.

t, time

z, differential variables

y, algebraic variables

tf, final time

u, control variables

p, time independent parameters


Collocation on Industries

finite Elements

Nonlinear Programming

Problem (NLP)

Discretized variables

NonlinearProgrammingFormulation

Nonlinear Dynamic

Optimization Problem

(Piecewise)

Continuous profiles


Nonlinear Programming Problem Industries

s.t.

Finite elements, hi, can also be variable to determine break points for u(t).

Add hu ≥hi≥ 0, S hi=tf

Can add constraints g(h, z, u) ≤ e for approximation error


Process optimization with dynamic reactor models
Process Optimization with Dynamic Reactor Models Industries

  • Optimal Catalyst Distribution in Graded Fixed Bed Reactors (Y. Nie, Dr. Paul Witt, Dr. AnshulAgarwal)

  • Dynamic Modeling and Recipe Optimization of Polyether Polyol Processes (Y. Nie, Dr. Carlos Villa)

  • Combined Recipe Optimization and Product Scheduling (YisuNie, Dr. John Wassick)

  • Characteristics:

  • Large-scale, nonlinear, (often) exothermic reactive systems

  • Modeled with simultaneous collocation methods

  • Need to capture nonlinear, (often) unstable modes and runaways, enable highly efficient and safe operation

  • Fast solution of optimization problems




Optimal catalyst distribution multizone optimization problem
Optimal Catalyst Distribution IndustriesMultizone Optimization Problem





Recipe Optimization Industries

Semi-Batch Polyether Polyol Process (Yisu Nie)






Special Industries industrial interest group:

Enterprise-wide Optimization for Process Industries

Multidisciplinary team:

Chemical engineers, Operations Research, Industrial Engineering

Researchers:

Carnegie Mellon: Ignacio Grossmann (ChE)

Larry Biegler (ChE)

John Hooker (OR)

Nicola Secomandi (OR)

Lehigh University: Katya Scheinberg (Ind. Eng)

Univ. Pittsburgh: Andrew Schaeffer (Ind. Eng.)

Overall Goal:

  • Novel planning and scheduling models, including consideration of uncertainty

  • Effective integration of Production Planning, Scheduling and Real–time Optimization

  • Optimization of Entire Supply Chains


Hierarchy of Enterprise Wide Optimization Industries

  • Supply Chain, Planning and Scheduling

    • Large LP and MILP models

    • Many Discrete Decisions

    • Few Nonlinearities

    • Essential link needed to process models

    • Decisions need to be feasible at lower levels


Process Operations Applications Industries

  • Real-time Optimization and Control

    • Large, Complex Process Models

    • Few Discrete Decisions

    • Nonlinearities and Dynamics

    • Essential to Link with Logistics and Planning

    • “Time-limited” on-line optimization

    • Optimal performance needs to be passed to higher levels


Multiscale temporal and Industries

spatial integration

Multi-site Production Planning

Polymer plants (25 grades)

Objective: Production Planning and Distribution

Model for Batch Polymerization Reactors


Production planning and scheduling

Production Site: Industries

Raw material availability and Raw material costs

Storage tanks with associated capacity

Transportation costs to each customer

Reactors:

Materials it can produce

batch sizes (lbs) for each material it can produce

operating costs ($/hr) for each material

Sequence dependent clean out times (hrs per transition for each material pair)

Time the reactor is available during a given month (hrs)

STORAGE

Reaction 1

A

F1

INTERMEDIATE

STORAGE

F2

STORAGE

Reaction 2

B

F3

STORAGE

Reaction 3

C

F4

Production Planning and Scheduling

  • Customers:

  • Monthly forecasted demands for desired products

  • Price paid for each product

  • Materials:

  • Raw materials, Intermediates, Finished products

  • Unit ratios (lbs of needed material per lb of material produced)





SEN/DAE Case Study Industries



Real time optimization for asus
Real-time Optimization for ASUs Industries

  • Air Separation Unit, key unit in IGCC-based Power Plants

  • Need for high purity O2

  • Respond quickly to changes in process demand

  • Large, highly nonlinear dynamic separation (MESH) models

  • Related work:

  • Methanol distillation (Diehl, Bock et al., 2005)

    • 40 trays, 210 DAEs, 19746 discretized equations

  • Argon Recovery Column

    • 50 trays, 260 DAEs, 21306 discretized equations

  • Double Column ASU Case Study

    • 80 trays, 1520 DAEs, 116,900 discretized equations


  • Real time optimization components

    w Industries

    Real-time Optimization: Components

    Plant

    APC

    y

    u

    RTO

    c(x, u, p) = 0

    DR-PE

    c(x, u, p) = 0

    p

    • Data reconciliation – identify gross errors and inconsistency in data

    • Periodic update of process model identification

    • Usually requires APC loops (MPC, DMC, etc.)

    • RTO/APC interactions: Assume decomposition of time scales

      • APC to handle disturbances and fast dynamics

      • RTO to handle static operations

    • Typical cycle: 1-2 hours, closed loop

    • What if steady state and dynamic models are inconsistent?


    Dynamic real time optimization rto

    d Industries

    Plant

    Dynamic Real-time Optimization (RTO)

    m

    PC

    u

    y

    Real-time Optimization

    Dynamic Models

    State Estimation

    Model Updates

    p

    • Goal: Integrate On-line Optimization with Advanced Process Control

    • Requires time-critical calculations

    • Current optimization makes this available in practice

    • Links to Decision-making at other scales/levels

    • Several applications in Chemical Industry

    • Essential for:

      • Inherently Dynamic Energy Systems

      • Handling Uncertainties in prices, supplies and demands

      • Optimal disturbance rejection


    On line optimization nonlinear model predictive control nmpc

    NMPC Estimation and Control Industries

    On-line Optimization: Nonlinear Model Predictive Control (NMPC)

    • Why NMPC?

    • Track a profile – evolve from linear dynamic models (MPC)

    • Severe nonlinear dynamics (e.g, sign changes in gains)

    • Operate process over wide range (e.g., startup and shutdown)

    z : differential states

    y : algebraic states

    Process

    d : disturbances

    u : manipulated

    variables

    NMPC Controller

    ysp : set points

    Model Updater

    NMPC Subproblem


    What about fast nmpc
    What about Fast NMPC? Industries

    • Fast NMPC is not just NMPC with a fast solver (Engell, 2007)

    • Computational delay – between receipt of process measurement and injection of control, determined by cost of dynamic optimization

    • Leads to loss of performance and stability(see Findeisen and Allgöwer, 2004; Santos et al., 2001)

    As larger NLPs are considered for NMPC, can computational delay be overcome?


    Can we avoid on line optimization
    Can we avoid on-line optimization? Industries

    • Divide Dynamic Optimization Problem:

      • preparation, feedback response and transition stages

      • solve complete NLP in background (‘between’ sampling times)

        as part of preparation and transition stages

      • solve perturbed problem on-line based on NLP sensitivity

      • > two orders of magnitude reduction in on-line computation

    • Based on NLP sensitivity of z0 for dynamic systems

      • Extended to Collocation approach – Zavala et al. (2008, 2009)

      • Similar approach for Moving Horizon Estimation – Zavala et al. (2008)

    • Stability Properties (Zavala et al., 2009)

      • Nominal stability – no disturbances nor model mismatch

        • Lyapunov-based analysis for NMPC

      • Robust stability – some degree of mismatch

        • Input to State Stability (ISS) from Magni et al. (2005)

      • Extension to economic objective functions


    Advanced Step Nonlinear MPC Industries(Zavala, B., 2008)

    Solve NLP in background (between steps, not on-line)

    Update using sensitivity on-line

    x(k)

    xk+1|k

    u(k)

    tk tk+1 tk+2

    tk+N

    Solve NLP(k) in background (between tk and tk+1)


    Advanced Step Nonlinear MPC Industries(Zavala, B., 2008)

    Solve NLP in background (between steps, not on-line)

    Update using sensitivity on-line

    xk+1|k

    x(k)

    x(k+1)

    u(k+1)

    u(k)

    tk tk+1 tk+2

    tk+N

    Solve NLP(k) in background (between tk and tk+1)

    Sensitivity to update problem on-line to get (u(k+1))


    Advanced Step Nonlinear MPC Industries(Zavala, B., 2008)

    Solve NLP in background (between steps, not on-line)

    Update using sensitivity on-line

    xk+2|k+1

    x(k)

    x(k+1)

    u(k+1)

    u(k)

    tk tk+1 tk+2

    tk+N

    Solve NLP(k) in background (between tk and tk+1)

    Sensitivity to update problem on-line to get (u(k+1))

    Solve NLP(k+1) in background (between tk+1 and tk+2)


    Nonlinear Model Predictive Control Industries

    Air Separation Unit (Huang, B., 2011)

    Objective: minimize operating cost subject to demand specifications

    4manipulated variables.

    4 output variables.

    Horizon: 100minutes in 20

    finite elements.

    Sampling time: 5 minutes.

    First Principle Index 1 Model:

    1520 DAEs

    OCFE Discretization:

    Variables: 117,140

    Constraints: 116,900


    NMPC Ramping for Air Separation Unit Industries

    (Huang, Zavala, B., 2009)

    At t = 30-60 min, product rates are ramped down by 40%.

    At t =1000-1030 min, they are ramped back. 5% disturbance is added to Mi.

    N = 20, K = 3

    320 ODEs, 1200 AEs.

    Variables: 117,140

    Constraints: 116,900

    400 NLPs solved

    Background: 200 CPUs, 6 iters.

    Online: 1 CPUs

    Computational Feedback Delay Reduced from 200 1 CPUs

    Blue dashed lines are ideal NMPC profile

    Red lines are AS-NMPC profile.

    In contrast, linearized controller is unstable



    D-RTO to Minimize Electricity Cost Industries

    Min SiPriceix (MA+EA)i + Regi

    S.t. ASU model

    time horizon 2hrs

    sampling time 6 min

    D-RTO with day ahead costs


    Dynamic Optimization for Day Ahead Strategy Industries

    Air Separation Unit

    ASU Compressor feeds follow trend of electricity price. Output profiles satisfied for demand specifications.

    Optimal D-RTO: $12,511

    Optimal set-point tracking: $13,042

    4.25% ($100k/yr) decrease over

    Optimal setpoint tracking.


    ARIMA Model to Real Time Price Industries

    ARIMA (2,1,1)


    Online ARIMA model for Price Estimation Industries

    Energy Price

    Update ARIMA price model with moving horizon estimation.

    Predicted price for a 24 hour period


    Dynamic Real-time Optimization for Real Time Pricing Industries

    Same formulation as in day ahead strategy but using predicted price.

    Cost of the proposed method: $5,939

    Cost of set-point tracking: $6,307

    6.19% ($135k/yr) savings over optimal setpoint tracking


    Projects with EWO partners Industries

    ABB: Optimal Design of Supply Chain for Electric Motors

    Contact: Iiro Harjunkoski Ignacio Grossmann, Analia Rodriguez

    Air Liquide: Optimal Coordination of Production and Distribution of Industrial Gases

    Contact: Jean Andre, Jeffrey Arbogast Ignacio Grossmann, Vijay Gupta, Pablo Marchetti

    Air Products: Design of Resilient Supply Chain Networks for Chemicals and Gases

    Contact: James Hutton Larry Snyder, Katya Scheinberg

    Braskem: Optimal production and scheduling of polymer production

    Contact: Rita Majewski, Wiley Bucey Ignacio Grossmann, Pablo Marchetti

    Cognizant: Optimization of gas pipelines

    Contact: Phani Sistu Larry Biegler, Ajit Gopalakrishnan

    Dow: Multisite Planning and Scheduling Multiproduct Batch Processes

    Contact: John Wassick Ignacio Grossmann, Bruno Calfa

    Dow: Batch Scheduling and Dynamic Optimization

    Contact: John Wassick Larry Biegler, Yisu Nie

    Ecopetrol: Nonlinear programming for refinery optimization

    Contact: Sandra Milena Montagut Larry Biegler, Yi-dong Lang

    ExxonMobil: Global optimization of multiperiod blending networks

    Contact: Shiva Kameswaran, Kevin Furman Ignacio Grossmann, Scott Kolodziej

    ExxonMobil: Design and planning of oil and gasfields with fiscal constraints

    Contact: Bora Tarhan Ignacio Grossmann, Vijay Gupta

    Praxair:Capacity Planning ofPower Intensive Networks with Changing Electricity Prices

    Contact: Jose Pinto Ignacio Grossmann, Sumit Mitra

    UNILEVER: Scheduling of ice cream production

    Contact: Peter Bongers Ignacio Grossmann, Martijn van Elzakker

    BP*:Refinery Planning with Process Models

    Contact: Ignasi Palou-Rivera Ignacio Grossmann, Abdul Alattas

    PPG*: Planning and Scheduling for Glass Production

    Contact: Jiao Yu Ignacio Grossmann, Ricardo Lima

    TOTAL*:Scheduling of crude oil operations

    Contact: Pierre Pestiaux Ignacio Grossmann, Sylvain Mouret

    Projects and Seminars: http://egon.cheme.cmu.edu/ewocp


    Collaborative IndustriesCyberinfrastructure for (MINLP) minlp.org

    CMU:Ignacio Grossmann, PietroBelotti, Lorenz Biegler, Pedro Castro, Francois Margot,

    Juan Ruiz, NikolaosSahinidis

    • Objectives http://www.minlp.org:

    • Create a library of optimization problems that can be

    • generally formulated as MINLP models.

    • Provide high level descriptions of the problems with one or several model

    • formulations with corresponding input files for one or several instances.


    http:// Industrieswww.minlp.org

    • MILP, NLP and MINLP models in diverse areas: engineering, physics, biology, finance

    - Formulation of models is emphasized

    which allows comparison and

    evaluation of numerical performance

    of different codes

    - Supports discussion through forum

    • Future:

    • Guidelines for modeling

    • Contribute open problems


    ABB Industries

    Air Liquide

    Air Products

    Braskem

    Cognizant

    Dow Chemical

    Eastman Chemical

    Ecopetrol

    ExxonMobil

    FICO

    GS Eng. & Constr.

    GAMS

    CAPD Industrial Members

    Mitsubishi Electric Res. Lab.

    NETL

    Neste Engineering Oy

    Paragon Decision

    Petrobras

    Pfizer

    Procter & Gamble

    Praxair

    Rockwell Automation

    Sasol

    Total

    Unilever


    • Refinery and batch scheduling –ABB

    • NLP optimization of gas separation plants -Air Products

    • Flowsheet optimization with SQP- Air Products, Aspen Technology

    • LSSQP, SPLIT - Aspen Technology

    • Combustion Verification – Alcoa

    • Optimal synthesis of separation of olefins -BP

    • Optimal synthesis of crystallization process –BP

    • Energy optimization in corn-based ethanol plants - Cargill

    • Optimal planning polymer plants –Dow

    • Large-scale supply chain optimization under uncertainty - Dow

    • Gasfield development model under uncertainty – ExxonMobil

    • Control and optimization of polymerization reactors - ExxonMobil

    • DICOPT, LOGMIP - GAMS

    • IPOPT Large-scale NLP software -IBM

    • Glass Furnace Control – PPG

    • Solar Grade Silicon – REC Silicon

    • Power plant control systems – Emerson Process Management


    • CAPD Academic Impact Industries

    • Largest PSE group in US

    • Graduates in academia

    • Achenie (VPI), Bañares (Oxford), Bullard (UNC), Floudas (Princeton), Govind (Cincinnati), Halemane (Karnataka), Hrymak (Western Ontario), Laird (Texas A&M), Lee (CCNY), Kawajiri (GaTech), Maravelias (Wisconsin), Oliveira (Coimbra), Pekny (Purdue), Pinto (Polytechnic), Pistikopoulos (Imperial), Rico (Celaya), Sahinidis (CMU), Swaney (Wisconsin), Turkay (Koc), Zamora (UAM), You (Northwestern)

    • 3. Textbook: Systematic methods of chemical process design

    • Biegler, Grossmann, Westerberg (Prentice-Hall)

    • 4. Adoption of CMU-research and software

    • MINLP, SQP, collocation, passivity theory, fault trees,ASCEND, BARON, SQP, IPOPT, DICOPT, LOGMIP

  • 5. Assoc. Editors Journals: AIChE J. (Grossmann), I&EC Res. (Biegler),

  • Optimization Methods and Software, Adaptive Control and Signal

  • Processing (Ydstie)


  • PSE [email protected] Industries

    • Strong Scientific Research Base

    • Numerical analysis  Simulation

    • Math. Programming  Process Optimization

    • Systems/Control Theory  Process Control

    • Computer Science  Software/Advanced Computing

    • Operations Research  Business/Operations

    • Strong Industrial Interactions

    • Enterprise Wide Optimization

    • Energy Systems

    • Process and Product Design and Development

    • Control, Dynamics and Real-time Optimization

    • Synergy of Modern Optimization Algorithms and Optimization Models

    • Design and Operation under Uncertainty

    • Planning, Scheduling and Operations

    • Optimization in Real-time

    • For more information: http//:capd.cheme.cmu.edu

    • http//:numero.cheme.cmu.edu


    Research Collaborations Industries

    Across Carnegie Mellon

    • Tepper School of Business

    • Institute for Complex Engineered Systems (ICES)

    • Mechanical Engineering

    • Electrical and Computer Engineering

    • Biomedical Engineering

    • Software Engineering Institute

    • Computer Science

    • Mathematical Sciences

    • Material Science and Engineering


    Research Collaborations Industries

    Around the World

    • Imperial College (UK)

    • ETH (Zürich)

    • RWTH-Aachen + IWR-Heidelberg + TUBerlin + MPI Magdeburg (Germany)

    • Abo Akademi + Jyvaskyla (Finland)

    • NTNU (Trondheim)

    • UPC + Alicante + Cantabria (Spain)

    • Coimbra + Porto (Portugal)

    • Maribor (Slovenia)

    • INTEC+INGAR+PLAPIQUI (Argentina)

    • Antofagasta + PUC + USACH (Chile)

    • UIA+Tec Celaya (Mexico)

    • Kyoto (Japan)

    • KAIST (Korea)

    • Tsinghua + Zhejiang+ECUST (China)

    • IIT Bombay (India)


    Process Systems Engineering Research Industries

    Process Systems Engineering is concerned with the systematic analysis and optimization of decision making processes for the discovery, design, manufacture and distribution of chemical products.


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