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58th Annual ISA Power Industry Division Symposium7-11 June 2015, Kansas City, Missouri
Kansas City Marriott Hotel
Dynamic Modeling and Control of an Integrated Solid Sorbent Based CO2 Capture Process Benjamin Omell, Debangsu Bhattacharyya*Department of Chemical EngineeringWest Virginia University, Morgantown, WV
David C. Miller, National Energy Technology LaboratoryUS DOE, Pittsburgh, PA
Jinliang Ma, Priyadarshi Mahapatra, Stephen E. ZitneyNational Energy Technology LaboratoryUS DOE, Morgantown, WV
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58th Annual ISA POWID Symposium, 7-11 June 2015, Kansas City, Missouri
Short Biography of Benjamin Omell
• Post-Doctoral Fellow in the Department of Chemical Engineering at West Virginia University
• PhD from Illinois Institute of Technology, Chicago• Research interest is in the area of steady-state and
dynamic modeling and advanced process control of energy-generating and associated processes
• Member of AICHE• Hobbies- Biking, hiking
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58th Annual ISA POWID Symposium, 7-11 June 2015, Kansas City, Missouri
CO2 Capture & Compression SystemsCoupled with the Steam Cycle
• Solid-sorbent systems are energetically superior to typical MEA-based solvent systems
• CCS system requires power and steam
• Dynamics are important during load-following
Bubbling Fluidized
Bed (BFB) Adsorber
CO2 Compression System
Moving Bed (MB)
Regenerator
58th Annual ISA POWID Symposium, 7-11 June 2015, Kansas City, Missouri
Goals and Objectives
• Develop accurate and flexible steady-state and dynamic solid sorbent models for CO2 capture– BFB and MB adsorber/regenerator– Solids heat exchangers– CO2 compression – Balance of the plant
• Create toolset to simplify implementation of advance control strategies that can perform efficiently in the presence of unmodeleddisturbances, noisy measurements, and unmeasured variables
• Use these models and tools for optimization, transient studies, and control system design as part of DOE’s Carbon Capture Simulation Initiative (CCSI)
4
58th Annual ISA POWID Symposium, 7-11 June 2015, Kansas City, Missouri
CCSI Toolset Framework
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58th Annual ISA POWID Symposium, 7-11 June 2015, Kansas City, Missouri
Challenge: Limitations in Previous Bubbling Fluidized Bed Models
• Typical simplifications to facilitate analytic solutions– Isothermal– Simplistic reaction kinetics and
transport– Steady-state– Embedded heater/cooler neglected
• Limited support in commercial tools
• Minimal application to CO2 capture in literature
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58th Annual ISA POWID Symposium, 7-11 June 2015, Kansas City, Missouri
A Flexible BFB Model
• Flexible configurations– Dynamic or steady-state– Adsorber or regenerator– Under/overflow– Integrated heat exchanger for
heating or cooling
• Supports complex reaction kinetics
• Compatible with CCSI uncertainty quantification (UQ) tools
1-D, two-phase, pressure-driven and non-isothermal models developed in both Aspen Custom Modeler (ACM) and gPROMS
7
58th Annual ISA POWID Symposium, 7-11 June 2015, Kansas City, Missouri
Model Development
• Gaseous species : CO2, N2, H2O• Solid phase components: bicarbonate, carbamate, and
physisorbed water.• Transient species conservation and energy balance
equations for both gas and solid phases in all three regions.
8
58th Annual ISA POWID Symposium, 7-11 June 2015, Kansas City, Missouri
Single-Stage BFB Model: Steady-State Results
0 0.2 0.4 0.6 0.8 10.05
0.06
0.07
0.08
0.09
0.1
0.11
0.12
0.13
Z/L
Mole
Frac
tion
H2O
CO2
Flue GasInlet
Flue GasOutlet
0 0.2 0.4 0.6 0.8 10
0.2
0.4
0.6
0.8
1
1.2
1.4
Z/L
Mol
ar L
oadi
ng (m
ol/k
g so
lids)
Solids overflow exit type configuration
BicCarH2O
SolidsInlet
0 0.2 0.4 0.6 0.8 10
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Z/L
Mole
Frac
tion
H2O
CO2
Gas Inlet Gas Outlet 0 0.2 0.4 0.6 0.8 10.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
Z/L
Mol
ar L
oadi
ng (m
ol/k
g so
lids)
BicCarH2O
Solids Outlet Solids Inlet
Adsorber
Regenerator
9
58th Annual ISA POWID Symposium, 7-11 June 2015, Kansas City, Missouri
Dynamic Results – Increase Inlet GasFlow by 20.6%
Gas CO2 Concentration Gas H2O Concentration
58th Annual ISA POWID Symposium, 7-11 June 2015, Kansas City, Missouri
Challenge: Limitations of ExistingMoving Bed Models
Very few references in literature, little application to CO2 capture
Previous applications in literature include MB furnaces for iron pellet reduction Dryers Non-catalytic gas-solid reactions
Lack of mathematical model with large amount of heat transfer for solid sorbent regeneration
Embedded heater/cooler not modeled Mainly steady-state model Hardly any model available in the
commercial software
Solid In
Solid Out
Gas In
Gas Out
Utility In
Utility Out
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58th Annual ISA POWID Symposium, 7-11 June 2015, Kansas City, Missouri
CCSI’s Moving Bed Models
Flexible dynamic and steady-state models Integrated heat exchanger that can be
used interchangeably for heating or cooling
Flexible enough for adsorber application
A1-D, two-phase, pressure-driven and non-isothermal regenerator model developed in both ACM and gPROMS
Solid In
Solid Out
Gas In
Gas Out
Utility In
Utility Out
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58th Annual ISA POWID Symposium, 7-11 June 2015, Kansas City, Missouri
Step Test: Sorbent Temperature
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58th Annual ISA POWID Symposium, 7-11 June 2015, Kansas City, Missouri
Multi-Stage Moving Bed Design to Overcome Fluidization Concerns
ConcernTwenty-seven 9 m diameter MB regenerators in parallel required to maintain flow in moving bed regime*
Solution: Multi-Stage Moving Bed Regenerator• Some CO2 (gas) removed between
stages• Reduced gas flowrate at the top of the
MB• Requires an advanced control strategy
Solid in
Stage 1
Stage 2
HX steam
downcomer
CO2 draw-off
Steam Regenerated solids
CO2 to compressor
*Assumptions for preliminary analysis: 12% CO2 flue gas @ 2000 mol/s with 90% capture rate
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58th Annual ISA POWID Symposium, 7-11 June 2015, Kansas City, Missouri
Challenge: Limitations of Existing Compression Systems Models
• Has been mostly developed for non-CO2 systems• For CO2 systems, developed mainly for sCO2 cycles
– Pressure ratio of 1 to 2.6 (about 150 for CCS)– Fixed inventory (variable for CCS)– Composition change, especially water content, is not a
major concern for sCO2 cycles• Typically steady-state
– Dynamics are essential for load-following in power systems
• Lack of performance curves for CO2 systems• Surge detection and control algorithms hardly studied for
these systems
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58th Annual ISA POWID Symposium, 7-11 June 2015, Kansas City, Missouri
CCSI CO2 Compression System ModelDynamic model of multi-stage integral-gear compression system
Surge Control Valves
TEG Regenerator & AbsorberFlash Vessels
Surge detection and control algorithm also developed
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58th Annual ISA POWID Symposium, 7-11 June 2015, Kansas City, Missouri
Integrated Model Enables Investigationof Entire Process Dynamics
Integrated Model in gPROMS
(also ACM version)
58th Annual ISA POWID Symposium, 7-11 June 2015, Kansas City, Missouri
Ramp In Flue Gas
Flow into compressor train
Time Hours
Inle
t flo
w k
mol
/hr
0.0 20.0 40.0
5600
.058
00.0 CO2 % Removed
Time Hours
%
0.0 20.0 40.0-0.2
-0.1
0.0
0.1
0.2
0.3
0.4Ramp in flue gas
Time=0 : 15.8% increaseTime=20: 28.8% decrease
Total Power
Time Hours
kW
0.0 5.0 10.0 15.0 20.0 25.0 30.0 35.0 40.0
2300
0.0
2400
0.0
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58th Annual ISA POWID Symposium, 7-11 June 2015, Kansas City, Missouri
Dynamics of Integrated Process
• Transient effect of the capture and compression process on the power plant and vice versa
• Can result in temporal variation in CO2 capture target-different time constants depending on the type of the bed
• CCSI tools DRM-builder and APC toolset can improve system dynamics
58th Annual ISA POWID Symposium, 7-11 June 2015, Kansas City, Missouri
CCSI Toolset Framework
20
58th Annual ISA POWID Symposium, 7-11 June 2015, Kansas City, Missouri
Motivation
• High-fidelity dynamic models (e.g. ACM dynamic models) are computationally expensive– Need to solve many Differential Algebraic Equations
(DAEs)– May require small time steps due to stiffness of DAEs– Not fast enough to catch up with real time
• Dynamic reduced models (D-RMs)– Speed up the dynamic simulations– Capture dynamic systems with reasonable accuracy– Can be used for Advanced Process Control (APC) and
Real Time Optimization (RTO)
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58th Annual ISA POWID Symposium, 7-11 June 2015, Kansas City, Missouri
D-RM Builder Workflow
Generate D-RM Based on
Simulation Results
1u
2u1
2
3
4
I/O Variable Selection
GUI for Configuring Inputs/Outputs GUI for Configuring
Training SequenceRun Training
Sequence
Analyze Reduced Model using UQ Tools, Validation Sets, Plots
D-RM in Form of MATLAB Code
SinterConfigGUI
58th Annual ISA POWID Symposium, 7-11 June 2015, Kansas City, Missouri
D-RM for the BFB Adsorber
D-RM generated based on open-loop ACM model Inputs:
• Flue gas flow rate: 6,075 to 7,425 kmol/hr• Sorbent flow rate: 540,000 to 660,000 kg/hr
Output:• CO2 removal (Fraction of CO2 in flue gas removed)
DABNet* model with pole values optimized CPU time required for ACM simulations
• Approximately 50 minutes for 2500 sampling steps (Sampling time interval at 0.1 second)
*Sentoni, G.B., L.T. Biegler, J.B. Guiver and H. Zhao, “State-Space Nonlinear Process Modeling: Identification and Universality,” AIChE Journal, 44(10), 2229-2239, 1998.
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58th Annual ISA POWID Symposium, 7-11 June 2015, Kansas City, Missouri
Validation Input Data
Time (sec)
Sorb
ent F
low
(k
g/hr
)Fl
ue G
as F
low
(k
mol
/hr)
ACM
D-RM
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58th Annual ISA POWID Symposium, 7-11 June 2015, Kansas City, Missouri
Validation Output Data
Time (sec)
Rel
ativ
e Er
ror
Frac
tion
of
CO
2R
emov
ed
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58th Annual ISA POWID Symposium, 7-11 June 2015, Kansas City, Missouri
CCSI Toolset Framework
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58th Annual ISA POWID Symposium, 7-11 June 2015, Kansas City, Missouri
APC Framework’s Capabilities
Utilize proven techniques from control relevant studies in literature and combine them synergistically in an efficient and robust process control framework.
DABNet-based Nonlinear System Identification
Analytical Jacobians and Hessians
Multiple Model Predictive Control (MMPC)
Auto Covariance Estimation (ALS Technique)
IPOPT-based Optimization Technique
UKF-based State Estimation
Enhanced User-Friendly GUI
Poor control response for highly nonlinear plants using traditional PID or MPC
Nonlinear MPC becomes computationally expensive for large-scale plant
Noisy measurements along with unmodeled disturbance and unmeasured variables provides poor control response
Controller is too sensitive to user-provided tuning parameters (inexperienced operators)
Typical APC interface overwhelms the operators (setting up control-model, tuning parameters, etc.)
Typi
cal p
robl
ems
with
APC
app
licat
ions
APC
Framew
ork
27
58th Annual ISA POWID Symposium, 7-11 June 2015, Kansas City, Missouri
Time [seconds]0 500 1000 1500 2000 2500 3000
CO
2 C
aptu
re [%
]
85
86
87
88
89
90
SetpointConventional MPCDAB-Net NMPC
Time [seconds]0 500 1000 1500 2000 2500 3000
Fso
rben
t [k
g/hr
]
10 5
3
4
5
6
7
8
9
Time [seconds]0 500 1000 1500 2000 2500 3000
Fflu
egas
[km
ol/h
r]
6000
6200
6400
6600
6800
7000
Performance Comparison on 2-Stage BFB Adsorber (ACM)
Controller responses to drastic plant-load changes –Comparison with standard MPC controller
Large changes in flue-gas flowrate
(input-disturbance)
Active Constraints
Better disturbance rejection
Note: Max. Control Calculation Time << Sample Time (Ts = 20 sec), Real-Time Operation with APC
Controller Parameters
Prediction Horizon = 50 Control Horizon = 10
Wu/Wy = 0.2
Cumulative Control
Calculation Time (sec)
Max Control Calculation Time (sec)
Total Simulation Time (min)
Conventional MPC 0.019 9.54 0.33 18.39
DAB-Net NMPC 0.007 19.97 3.43 18.92
Computational CostCumulative
ResidualAlgorithm
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58th Annual ISA POWID Symposium, 7-11 June 2015, Kansas City, Missouri
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
0.18
0.2
0.01 0.1 1 10
Resi
dual
= ∑
(r-y
)TW
y(r-y
)
Wu/Wy
MATLAB's MPC Toolbox
APC Framework's DABNet-based NMPC
Performance Comparison on 2-Stage BFB Adsorber (ACM)
Benefits of APC Framework1. Low residuals – tracks
setpoint better over time
2. Low sensitivity for user-provided tuning parameters (Wu/Wy in this case)
Sensitivity of Control Performance to Tuning Parameter –Comparison with standard MPC controller
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58th Annual ISA POWID Symposium, 7-11 June 2015, Kansas City, Missouri
Developed high-fidelity steady-state and dynamic solid sorbent models for CO2 capture
Utilization of DRM-builder tool to generate reduced ordered models Use reduced order model to generate control strategy with
APC framework to handle moving boundary problem of multi-stage moving bed regenerator
APC performance is relatively insensitive to tuning parameters and results in efficient disturbance rejection and load-following characteristics
Summary
58th Annual ISA POWID Symposium, 7-11 June 2015, Kansas City, Missouri
Acknowledgement
DOE’s Carbon Capture Simulation Initiative for funding. Part of this technical effort was performed under the RES contract DE-FE0004000 through National Energy Technology Laboratory’s Regional University Alliance (NETL-RUA.
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.
Disclaimer
31
58th Annual ISA POWID Symposium, 7-11 June 2015, Kansas City, Missouri
Thank You
32
58th Annual ISA POWID Symposium, 7-11 June 2015, Kansas City, Missouri
Optimized Process Developed using CCSI Toolset
GHX-001
CPR-001
ADS-001
RGN-001
BLR-001
Flue Gas from Plant
Clean Gas to Stack
CO2 to Sequestration SHX-01
SHX-02
CPR-002
LP/IP Steam from Power Plant
CPP-002
ELE-002
ELE-001
Flue GasClean Gas
Rich Sorbent
LP SteamHX Fluid
Legend
Rich CO2 Gas
Lean Sorbent
Parallel ADS Units
Parallel RGN Units
Parallel ADS Units
GHX-002
CPP-000
Cooling Water
Injected Steam
Cooling Water
Saturated Steam Return to Power Plant
Mild SteamDirectly Injected into Reactor
Compression Train
Cond
ense
d W
ater
Cond
ense
d W
ater
∆Loading1.8 mol CO2/kg
0.66 mol H2O/kg
Solid Sorbent MEA27
(∆10°C HX)MEA27
(∆5°C HX)Q_Rxn (GJ/tonne CO2) 1.82 1.48 1.48Q_Vap (GJ/tonne CO2) 0 0.61 0.74Q_Sen (GJ/tonne CO2) 0.97 1.35 0.68
Total Q 2.79 3.44 2.90