Integrarea si Intensificarea Proceselor Chimice

1

Integrarea si Intensificarea Proceselor Chimice

Michael Baldea The University of Texas at Austin

Facultatea de Chimie si Inginerie

Chimica

Universitatatea “Babes-Bolyai”

Cluj, 18 martie, 2015

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Process and Energy Systems Engineering

Context

Process Integration (IP):

A holistic approach to process design, based on maximizing

recovery and utilization of energy and materials from within the

process, reducing the use of utilities and minimizing environmental

impact (cf. Hallale, 2001, Cussler and Moggridge, 2001, El-Halwagi,

2006)

2

Process intensification (PI):

“Any chemical engineering development that leads to substantially

smaller, cleaner, safer and more energy efficient technology”

(Reay et al., 2013) or “that combine[s] multiple operations into

fewer devices.” (Tsouris and Porcelli, 2003)

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Context



smaller, cleaner, safer and more energy efficient technology” (Reay

et al., 2013) or “that combine[s] multiple operations into fewer

devices.” (Tsouris and Porcelli, 2003)

Multum in parvo (Lat.) : much in little

3

Stankiewicz and Moulijn, 2003 Reay et al., 2013


Context



smaller, cleaner, safer and more energy efficient technology” (Reay

et al., 2013) or “that combine[s] multiple operations into fewer

devices.” (Tsouris and Porcelli, 2003)

Multum in parvo (Lat.) : much in little

Paradigm

• Process should be governed by intrinsic rates

• Identify limiting factor(s) in a process (transport, transfer)

• Address them via changes in system operation (batch

continuous), device geometry, external energy fields

• Scale-up by “numbering-up”

4

3


PI: Multiple Phenomena, Scale-Independent

5

www.velocys.com

Courtesy of Bailee Roach

A,B,C

A

B

c

A,B,C

A

c

B

Reaction

mm channels

<1m length

cm diameter

Several meters tall

Separation

Integrated Intensified

ΔH<0

ΔH>0


PI: Multiple Phenomena, Scale-Independent

6

A,B,C

A

B

c

A,B,C

A

c

B

Reaction

mm channels

<1m length

cm diameter

Several meters tall

Separation

Integrated Intensified

ΔH<0

ΔH>0

30% capital savings,

use up to 40% less

energy

10x smaller size

Schultz et al., CEP, 2002, Kiss and

Bildea, CEPPI, 2011

Zanfir and Gavriilidis, CES, 2003

PI practice ahead of theory

4


0 2 4 6 8 100

2

4

6

V, m

30 2 4 6 8 10

a.u

. CAPEX OPEX

R/Fo

I II III

Integration vs. Intensification

7

RI

Fo,CAout=0

I

RII

Fo,CAo

II

Fo,CAout=0

Fo,CAo

III

Fo,CAout=0

Baldea, FOCAPD, 2014


Integration vs. Intensification

• Fundamental changes in design, operation 8

RI

Fo,CAout=0

I

RII

Fo,CAo

II

Fo,CAout=0

Fo,CAo

III

Fo,CAout=0

• “The front-runner of

industrial process

intensification” (Harmsen, 2007)

5


Integration vs. Intensification: Empirical

• Reduced number of units

• Reduced unit size and

holdup

• Reduced OPEX (no

recycling)

BUT

• Reduced number of degrees

of freedom for design and

control

9

RI

Fo,CAout=0

I

RII

Fo,CAo

II

Fo,CAout=0

Fo,CAo

III

Fo,CAout=0

Schembecker and Tlatlik, 2003; Nikacevic et al., 2012


Heat Integration vs. Intensification

• Often safety-critical

• Ample instrumentation and

actuation

• Multiple design DOF, several

possible control configurations

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• Microchannel reactors

• Exothermic and endothermic

reactions occur in parallel

channels

ΔH<0

ΔH>0

ΔH>0

ΔH<0

ΔH>0

ΔH<0

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Prototype System: Methane Steam Reformer

Assumptions

• Gas phase: 2D convection-diffusion-reacting flow (laminar)

• Wall: 2D heat conduction

• Catalyst layers: 1D reaction-diffusion

• Boundary conditions: no flux (outlet), equal flux (fluid-solid interface),

symmetry (channel center)

Implementation: gPROMS

• Discretization: axial: finite differences, radial: OCFEM

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Zanfir and Gavriilidis, 2003, Zanfir et al., 2011, Pattison and Baldea, 2013


0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1800

900

1000

1100

1200

1300

1400

1500

Dimensionless z

Tem

pera

ture

(K

)

Pre-disturbance

Post-disturbance

Temperature Control Problem

12

∆H>0

∆H<0

Reactor subject to operational disturbances:

Flow rate

Pressure

Temperature

Composition

May result in formation of “hotspots” that damage the reactor or catalysts

Goal: develop distributed control concepts that prevent the formation of hotspots

Pattison, R.C. and Baldea, M., AIChE J., 2013

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Concept 1: Segmented Catalyst

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Alternating catalytically active and inactive segments

Emulates a distributed feed system : increase number of design degrees of freedom

Modulates the rate of heat generation axially

Formulate optimization to select:

the optimal parametric temperature trajectory and

the optimal catalyst segmentation to track the trajectory

Pattison, R.C. and Estep, F.E. and Baldea, M., IECR., 2013

Tem

pera

ture

z

Base profile

Desired profile

Eigenberger et al., CES, 2000


Catalyst Segmentation Results

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Base Case

Segmented Catalyst

200oC reduction in maximum temperature

Higher average temperature

Increase methane conversion in both sets of channels

Pattison, R.C. and Estep, F.E. and Baldea, M., IECR., 2013

8


Concept 2: Thermal Fuse

15

T

z

∆H>0

∆H<0

Tmelt

z

Phase change material (PCM) layer absorbs heat at constant temperature when melting, prevents formation of hotspots

Pattison, R.C. and Baldea, M., AIChE J., 2013; Patent WO2014042800 A1


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PCM Controller Tuning

Single degree of freedom: PCM layer thickness

• Geometry is fixed and must be determined at design stage

• Online tuning is not possible

Model-based controller design:

• Shape dynamic behavior (melting/solidification time) via

stochastic optimization of PCM size

• Identification-based optimization: represent disturbances as

multi-level random signals, impose on system model during

dynamic optimization iterations

ΔH<0

ΔH>0

ref ref v =v (v,σ)

Wang and Baldea, Comput. Chem. Eng., 2014

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Optimization Under Uncertainty

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• Problem θ: Uncertain variables

d: design variables

u: controls

Pistikopoulos, 1995

Infinite-dimensional problem due to 𝜽, 𝒖

- Discretization to convert to finite-dimensional (MI)NLP

- Problem solution:

Multi stage (design vs. resource variables), generalized Benders

decomposition

- Chance constrained formulation, parametric programming

Acevedo and Pistikopoulos, 1997, Clay and Grossmann, 1997, Dua et al., 2002,

Benerjee and Ierapetritou, 2002, Li et al., 2008, Faisca et al., 2008

min 𝒖,𝒅

𝐸(𝐽 𝒙 , 𝒙, 𝒖, 𝜽 𝑡 , 𝑡 )

s.t. 𝒉 𝒙 , 𝒙, 𝒖, 𝜽 𝑡 , 𝑡 = 0

𝒈 𝒙 , 𝒙, 𝒖, 𝜽 𝑡 , 𝑡 ≤ 0


Discretization

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-4 -3 -2 -1 0 1 2 3 4-4 -3 -2 -1 0 1 2 3 4

• Sample Average Approximation

(SAA)

Ierapetritou et al., 1996, Acevedo and Pistikopoulos, 1998 Linderoth et al.2006, Constantinescu et al., 2008

• Infinite-dimensional problem converted to (MI)NLP

• Computationally expensive when number of scenarios increases

• Similar to a system identification experiment

min𝒖,𝒅

𝑤𝑝

𝑃

𝑝=1

𝐽 𝒙 , 𝒙, 𝒖, 𝒅, 𝜽𝑝, 𝑡

s.t. ∀𝑝 𝒉 𝒙 , 𝒙, 𝒖, 𝜽𝑝 , 𝑡 = 0

𝒈 𝒙 , 𝒙, 𝒖, 𝜽𝑝 , 𝑡 ≤ 0

min𝒖,𝒅

1

𝑁 𝐽 𝒙 , 𝒙, 𝒖, 𝒅, 𝜽𝑘 , 𝑡

𝑁

𝑘=1

s.t. ∀𝑘 𝒉 𝒙 , 𝒙, 𝒖, 𝜽𝑘 , 𝑡 = 0

𝒈 𝒙 , 𝒙, 𝒖, 𝜽𝑘 , 𝑡 ≤ 0

• Multiperiod

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Identification-Based Optimization

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• Nonlinear systems: pseudo-random multi-level signal

(PRMS)

Step testing: generate scenarios

to quantify input/output

relationship

Train of signal changes (e.g.:

PRBS): increase efficiency

Godfrey, 1993, Barker., 2004

• IBO concept:

- represent uncertain variables as pseudo-random

multi-level signals (PRMS)

- apply PRMS to efficiently evaluate the response of

the system to fluctuations in uncertain variables.


Pseudo-Random Multi-Level Signal representation:

• time-unfolding using Galois Sequence support

N

PCM max,wall,j melt

j=1 0

ref,j ref

1h =argmin (T -T )ds

N

s.t. reactor model equations

v =v (v,σ)

St

Problem Reformulation

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PRMS-based formulation

PCM max,wall melt

0

ref

h =argmin (T -T )d

s.t. reactor model equations

v (t)=v

MPRSt

MPRS

Wang and Baldea, Comput. Chem. Eng., 2014

𝑁𝑡𝑠 ≤ 𝑡𝑀𝑃𝑅𝑆

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Identification-Based Optimization: Algorithm

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Make initial guess for

optimization

variables

Set initial

conditions

for model

Integrate for given time

horizon

Reset system states

to initial conditionOptimal? Display results

Calculate the gradients of

the objective function and

constraints

Integrate for given time

horizon

Compute new decision

variablesCheck optimality criteria

and constraints

NO

YESIn

itia

l ite

ratio

nIte

ratio

ns 2

...N

Generate MPRS

of all disturbances

• Assumptions

- Time horizon T is sufficiently long

- System is feedback stabilizable

- Disturbance distributions known

PRMS

min𝒅,𝒌

𝐽 𝒙 , 𝒙, 𝒖, 𝒅, 𝜽𝑃𝑅𝑀𝑆(𝑡), 𝑡

s.t. 𝒉 𝒙 , 𝒙, 𝒖, 𝒅, 𝜽𝑃𝑅𝑀𝑆(𝑡), 𝑡 = 0

u=k(x)

𝒈 𝒙 , 𝒙, 𝒖, 𝒅, 𝜽𝑃𝑅𝑀𝑆(𝑡), 𝑡 ≤ 0

0<t<T

Wang and Baldea, Comput. Chem. Eng. 2014


Thermal Fuse: Results

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Segmented Catalyst: reduced nominal temperature profile

0.8 mm Thermal Fuse: prevents the formation of hotspots

Cannot account for sustained disturbances Pattison and Baldea, AICHE J. 2013

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Application: Stranded and Associated Gas

Stranded gas: Small remote

deposits that are too expensive to

extract & transport to consumers

7000 tcf worldwide

Associated gas: found with oil,

typically flared or reinjected

4.5 trillion megajoules wasted

in 2011

Monetization: pipeline,

liquefaction, Gas-To-Liquids

(GTL):

Not viable at small scales

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image source: www.gereports.com

Microchannel reactor-based

systems can have large impact


Intensification of Systems without Recycle

• Dividing wall column (DWC): separate ternary

mixture using a single physical device


A,B,C

A

B

c

A,B,C

A

c

B

13


DWC at the University of Texas at Austin

University of Texas at Austin

Pickle Research Center

August 2014

Project Partners:

UT, Eastman, Emerson,

Packing from Sulzer, Koch-Glitsch



DWC Pilot Plant

Support El 21' 5.5"

3rd

Platform El 35' 6"

2nd

Platform El 23' 6"

Sump

Section

Stripping

Section

Lower DW

Section

Upper DW

Section

Rectifying

Section

Head

1

2

49

50

34 56

7

8

910

12 11

1416 13 15

2830 27 29

2122 23

1718

26 25

32 31

36 35 37

40 39

41 43

45

4746

48

51

24

3334

38

1920

4244

52


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PI: Future Challenges

Theory: break unit ops paradigm

Synthesis of intensified processes: Phenomena-

based Superstructure? (Ismail et al., 2001, Arizmendi-

Sánchez and Sharratt, 2008, Lutze et al., 2013)

Flowsheet “co-simulation” / optimization (Lang et al.,

2009)

Embed control capability 27

A,B,C

A

B

c

A,B,C

A

c

B ΔH<0

ΔH>0

Models readily available in

process simulator “parts bin” ?


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Conclusions

• Intensification fosters dynamic complexity

- Better economics/improved efficiency: more difficult control

- Scale independent

• Accomplishments

- “cool” applications and commercial success

• Future

- Theory: new process synthesis, simulation, optimization framework; will likely lead to new applications

- Embed control considerations at the control stage

- Applications: smarter manufacturing, interaction with power system

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• Students, in particular R.C. Pattison and S. Wang

• American Chemical Society- Petroleum Research Fund

• US Department of Energy, Advanced Manufacturing Office

• W.A. “Tex” Moncrief Grand Challenges Award

• Eastman Chemical, Emerson Process Management, ABB, Dow Chemical, Corning, Center for Operator Performance, Texas Wisconsin California Control Consortium

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Acknowledgements

Documents

Integrarea si Intensificarea Proceselor Chimice