Priyadarshi Mahapatra, Benjamin OmellNational Energy Technology Laboratory, Pittsburgh, PA

2018 Capture Technology Project Review MeetingPittsburgh, PA

August 13, 2018

Development and Application of Advanced Process Control for UKy CO2 Capture Pilot-Plant

Overview

2

• Background– Motivation | Objectives– CCSI’s APC Framework Toolset– UKy-CAER CO2 Capture Pilot-Plant Facility

• Project Plan / Status• Past Accomplishments

– Identify “most-influential” I/O variables– Develop dynamic reduced models– Offline “simulation-based” control studies

• Current Activity / Accomplishments– Integration with pilot-plant DCS– Implement real-time APC

• Results• Summary

Motivation / Contribution to CCSI2

3

• Industrial APC Ain’t Easy– Computational cost– Need for accurate and fast real-time

prediction models– APC / NMPC module costs - $$$– Non-generic, embedded within DCS

• New Contribution– NMPC-based industrial control

• Optimal dynamic operation– Exploit more-efficient third-party

solvers (MATLAB – sparse matrix calculations, IPOPT, etc.)

BackgroundCCSI’s Advanced Process Control Framework

4

D-RM

uk

APC

d2

y1

Process

u1

dk-1 yk

rk

uk-1

d1

d3

SetpointsMeasured Process Variables(inputs, states, outputs) Controller Outputs(manipulated inputs)

r1

Why Advanced Process Control (APC) Framework ?• Integrated framework for optimal control of CO2 capture processes• Efficient dynamic transition to desired set-point and mitigation of process

uncertainties• Enables to protection of intellectual data by serving as a “black-box” surrogate

dynamic-model• Leverage “fast” D-RMs from CCSI’s D-RM Builder as predictive models to

optimize control-moves towards cost-effective transient response in face ofprocess constraints

APC Framework Features• Constrained Nonlinear Model Predictive Control (NMPC) using DAB-Net D-RM

model• Constrained Multiple-Model Predictive Control (MMPC) based on multiple linear

state-space “model-bank”• Unscented Kalman Filter (UKF)-based state-estimation

Background

5

• University of Kentucky’s CCS Project– Center for Applied Energy Research (CAER)

• Other Participants: LG&E/KU, Hitachi, EPRI, etc.– 2 MWth (0.7 MWe) slip stream test facility– At E. W. Brown Generating Station

• Louisville Gas & Electric (LG&E) and Kentucky Utilities (KU)• In Harrodsburg, KY, 30 miles from UKy-CAER

– Sponsors• DOE/NETL ($14.55 Million)• Kentucky Department of Energy Development and Independence• Carbon Management Research Group (Consortium)

– Catch and release program• Opportunity: improve control responses time | residence time in

solvent/desiccant loops

CAER’s CO2 Capture Test Facility

6

LG&E/KU Brown Station CO2 Capture Facility

Existing Control System• Emerson’s DeltaV system• All standard PID Controllers

(w/ 2-3 cascade loops)• Currently uses 170 process

variables– Maximum 250 variables from

the license• Over 20 manipulated input

variables• Solvent residence time: ~30

min through the loop; scope for improvement

Project Status/Plan

7

• Assess control requirements• Operability and controllability analysis

•Identify relevant I/O process variables•Design step-change sequence•Run step-tests

− Keep low-level PID controllers unchanged• Build D-RM for the system

•Validate approach on secondary-stripping column sub-section•Develop D-RM for entire plant

− Testing data | Validation data• Evaluate APC methodology for online real-time control

•Validate APC approach using offline “plant” based on D-RM – demonstrated benefits•Integrate CCSI’s APC Framework w/ pilot-plant’s DCS•Closed-loop identification based on historical data

• Implement real-time nonlinear MPC•Controller tuning and validation (preliminary)•Demonstrate operational improvement over existing methods

CAER’s CO2 Capture Process

8

• Three loops– Flue gas pretreatment loop– Amine solvent loop– Liquid desiccant loop

• Solvent loop design– Single absorber with intercooler– 2 strippers

• Primary stripper• Secondary air stripper

• Cooling tower/liquid desiccant loop design– Removing moisture in humid air by liquid desiccant

B-101

C-101

P-102

Flue Gas

E-102

T-101

To FGD

C-102

Pret

reat

men

t To

wer

Absorber

P-103

E-113

E-106

Sec. Heat Recovery

HX

Rich Heat Recover HX

C-105

Secondary Stripper

P-108

E-104

E-110

E-114Lean/Rich

HX

C-104

Primary Stripper

P-104

E-108

Pri. Heat Recovery

HX

To Stack

C-108

H2O Evaporator

P-106

E-109E-111

B-104

AirP-110

E-105

E-115

Liquid Desiccant

Cooler

Liquid Desiccant

Chiller

Air

B-103

Air

T-105

P-116

Desiccant Make-Up

Tank

T-103

P-113

Amine Make-Up

Tank

Flue Gas

Rich Amine

Lean Amine

CO2

CO2 + Air

H2O / Sat. Air

Liq. Desiccant

Air

Color Legend

P-112E-112

Process Flow Diagram

University of KentuckyCAER CO2 Capture Pilot Plant

Developed by:Priyadarshi Mahapatra, NETL

Jinliang Ma, NETL

Date: 1/19/2017

C-106

CWR

CWS

Cooling Tower

Soda Ash Make-Up

Tank

9

Proc

ess

Flow

Dia

gram

Mat

eria

l Str

eam

s +

IO V

aria

bles

Secondary Stripper

Level

Primary Heat Recovery HX Temperature

CO2Capture

Cooling Air Flowrate

Desiccant Flowrate

Rich Polishing

Steam

Secondary Stripper Air

Flowrate

Solvent Flowrate

Reboiler Steam

Evaporator Polishing

Steam

Primary Stripper Pressure

SS Overhead Temperature or CO2 Comp.

FluegasFlowrate

FluegasComposition

Ambient Humidity

Ambient Temperature

Variable Legend

DisturbanceManipulated

Control

Relevant Process Variables

10

• Manipulated Inputs (MV)– Solvent flow rate– Primary stripper pressure– Reboiler steam flowrate– Flow rate of air to secondary stripper– Cooling air flowrate– Desiccant flowrate– Rich-solvent heater steam flowrate– CO2 concentration of flue gas to absorber (disturbance)

• Output / Controlled Variables (CV)– Percentage of CO2 captured– Temperatures of product streams of individual columns – Compositions of product streams

Previous “offline” Control Studies

11

0 50 100 150 200 250Time [min]

86.5

87

87.5

88

88.5

89

89.5

90

90.5

91

91.5

CO

2 C

aptu

re [%

]Conventional PIDNMPC w/ APC Framework

CO2 concentration disturbance in inlet flue gas (14% to 16%) at t = 0

NMPC Objective function

80% reduction in settling time

Less (~5%) steam duty

( ) ( )

( )

12 2 2 2... 1

1

min CO CO CO CO

Stm Stm

M

P TSP SPp p y p p

p

MReb RHR TM M m u m

m

J=

=

= − −

+ + +

∑

∑

Δu Δuw

Δu w Δu

0 50 100 150 200 250Time [min]

17

18

19

20

21

22

23

24

Solv

ent F

low

(L/G

)

0 50 100 150 200 250Time [min]

1500

1600

1700

Reb

oile

r Ste

am [l

b/hr

]

300

400

500

RH

R S

team

[lb/

hr]

Extracost

Extra cost

Industrial Implementation: D-RM development

12

Generate D-RM Based on Plant

Results

Configure I/O Ranges & Time

Dependency

Prepare Training / Validation Input

SequenceConduct Process /

Plant Step-Tests for Training / Validation

Scenario

Configure Relevant I/O

Variables

Integrate D-RM with APC

Predict Response/Show RegressionError With Plots

UncertaintyQuantification

Analysis

1u

2u

12

34

I/O Variable Selection

Configuring Inputs/Outputs

Configuring Training Sequence

TrainingSequence

MATLAB Plot Showing Predicted Output vs.

Plant Response

Mean and CovariancesPredicted By UKF

D-RM in Form of MATLAB Code

D-RM Building Process

• Reboiler Steam Flowrate• RHR Steam Flowrate• Desiccant Flowrate• Cooling Tower Air Flowrate• L/G• CO2 Capture• Primary HX Overhead Temp

500 600 700 800 900 1000 1100 1200 1300Time [min]

8

10

12

14

16

18

20

Solv

ent L

/G

500 600 700 800 900 1000 1100 1200 1300Time [min]

42

43

44

45

46

47

48

49

50

51

Des

icca

nt F

low

rate

[gpm

]

500 600 700 800 900 1000 1100 1200 1300Time [min]

900

1000

1100

1200

1300

1400

1500

1600

1700

1800

Reb

oile

r Ste

am [l

b/hr

]

0

100

200

300

400

500

600

Ric

h-H

eat-R

ecov

ery

Stea

m [l

b/hr

]500 600 700 800 900 1000 1100 1200 1300

Time [min]

2.45

2.5

2.55

2.6

2.65

2.7

2.75

2.8

2.85

Coo

ling

Tow

er A

ir [s

cfm

]

10 4

Results – System Identification / D-RM Building

13

5 Input Variables

Results – System Identification / D-RM Building

14

500 600 700 800 900 1000 1100 1200 1300Time [min]

110

115

120

125

130

135

140

Prim

ary

Hea

t-Rec

over

y H

X Te

mp

[° F]

Pilot-PlantD-RM Model

500 600 700 800 900 1000 1100 1200 1300Time [min]

0

10

20

30

40

50

60

70

80

90

100

CO

2 C

aptu

re [%

]

Pilot-PlantD-RM Model

Primary Control Variable – Minimize settling times Critical Constraint Variable – Values above 139F leads to solvent leakage from stack (closely monitored)

D-RM tracks the pilot-plant response well with slight offset for validation data

2 Output Variables

Industrial APC Implementation

15

D-RM

uk

APC

d2

y1

Process

u1

dk-1 yk

rk

uk-1

d1

d3

SetpointsMeasured Process Variables(inputs, states, outputs) Controller Outputs

r1

MATLAB

Emerson DeltaV DCS

Real-time exchange of

values, events, R/W state

Integration with pilot-plant DCS

16

• OPC (OLE for Process Control) Protocol•Identify existing Emerson Delta-V OPC server on pilot-plant DCS•Create OPC client within CCSI APC Framework•Establish connection from client to server•Identify process variables tags (r/w permissions) available on server – PLC/charm names•Create read-only PV tags and writable remote setpoint (SP) tags on client•Conduct step-tests on relevant remote SP and validate PV with DCS historian

• Develop event callbacks routines for solving real-time control optimization problem• Establish real-time communication at each sampling “clock” time

OPC Write (Sync):Bucket....Real8

OPC Read (Device):Bucket...e.Real8

V

Q

V

OPC ConfigReal-Time

0 50 100 150 200 250Time [min]

78

80

82

84

86

88

90

92

94C

O2

Cap

ture

[%]

Results – Real-time APC (preliminary study)

17

Controller satisfies upper-limit – prevents potential solvent leak

Restores 90% capture quickly using settling-time optimization

Con

trol

Var

iabl

esM

anip

ulat

ed In

puts

Study Details• 3 input – 2 output• CO2 concentration

disturbance in inlet flue gas (14% to 16%) at t = 600 min

• Control objective

• Sampling-time = 1 min• Prediction Horizon = 2 hr• Control Horizon = 10

steps

( ) ( )1

2 2 2 2... 1

1

min CO CO CO COM

P TSP SPp p y p p

p

MTm u m

m

J=

=

= − −

+

∑

∑

Δu Δuw

Δu w Δu

0 50 100 150 200 250Time [min]

1400

1450

1500

1550

1600

1650

1700

Reb

oile

r Ste

am [l

b/hr

]

350

400

450

500

RH

R S

team

[lb/

hr]

0 50 100 150 200 250Time [min]

38

40

42

44

46

48

50

52

54

56

Des

icca

nt F

low

(gpm

)

APC FrameworkConventional

0 50 100 150 200 250Time [min]

136

136.5

137

137.5

138

138.5

139

139.5

Prim

ary

Hea

t-Rec

over

y H

X Te

mp

[° F]

SummaryPerformance Improvement

UKy/CAER existing control APC FrameworkNo automated control of CO2 capture Optimal setpoint tracking of CO2 possible

using NMPCRely on overhead T high-alarm visual feeds to rectify solvent loss to stack.

Overhead T monitored and predicted via model. Take necessary steps before violating constraints

Square I/O system required for multiple single-input-single-out controllers – e.g. CO2 capture may only be paired with reboiler-steam flow

One output may optimally be controlled by two or more sensitive inputs – e.g. both reboiler and RHR steam contribute to controlling CO2capture

Fixed control parameters leading to sub-optimal performance when operating far from “tuned” regime

NMPC with Kalman Filter updates the model based on extent of plant-model mismatch

Summary

19

Demonstrated CCSI’s APC Tools applicability and benefits in CO2 capture plant

– Identified most-influential pilot plat’s PV– Developed dynamic reduced-order model (D-RM)– Demonstrated ability to interface with existing pilot-plant DCS using industry-

standard OPC– Implement real-time APC for CO2 capture SP tracking with temperature

constraintFuture Work

– Refine existing D-RM through closed-loop identification using historical data– Implement plant-wide APC with economic optimization and demonstrate

benefits over existing control methods

Acknowledgement

20

• University of Kentucky’s CAER Team– Kunlei Liu, Jonathan Pelgen, Heather Nikolic, Zhen Fan– Control Room Operators: Len, Marshall, Otto

• OPC Foundation– Provide educational material for efficient OPC implementation

• Matrikon OPC Team– Provide test-bench for OPC communication offline

• MATLAB OPC Toolbox– Provide OPC client interface for APC-DeltaV communication

Disclaimer

21

This presentation was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor any agency thereof, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof.

For more informationhttps://www.acceleratecarboncapture.org/

Priyadarshi Mahapatra, NETL, PittsburghPriyadarshi.Mahapatra@netl.doe.gov

Benjamin Omell, NETL, PittsburghBenjamin.Omell@netl.doe.gov