Optimization of H2 Production in a
Hydrogen Generation Unit
Márcio R. S. Garcia1,
Renato N. Pitta2,
Gilvan A. G. Fischer2,
André S. R. Kuramoto2
1Radix Engenharia e Desenvolvimento de Software Ltda, Rio de Janeiro, RJ, Brazil (e-mail:
2Refinaria Henrique Lage, São José dos Campos, SP, Brazil (e-mail: [email protected] ,
Summary
1. Process description
2. Advanced Process Control
3. Modelling and Identification
4. Results
5. Conclusion
Process description – Hydrogen Generation Unit
Hydrogen Generation Units (HGU) are designed to supply the H2
necessary for the hydrotreating process;
Hydrotreating Units (HDT) use H2 for Sulfur, Nitrogen, Oxygen and
other contaminants removal from the Diesel / Naphtha streams and
also for aromatics / olefins conversion;
REVAP (Henrique Lage Refinery), located in the state of São Paulo,
Brazil is one of the largest refineries in the country and contemplates
6 HDTs and 2 HGUs and 1 CCR (Continuous Catalytic Reforming
Unit)
Process description - H2 Header configuration
H2 HEADER
Process description - H2 consumption profile
0.40%
4.60%
7.00%
16.00%
19.00%
53.00%
Tail Gas
Naphtha / Kerosine HDT
Coker Naphtha HDT
Cracked Naphtha HDS
Diesel HDT
Gasoil HDT
Process description - H2 production profile
61%
21%
18%
HGU-II
CCR
HGU-I
Process description - H2 Generation Process
n CO + n H2O n CO2 + n H2
CnHm + n H2O n CO + (n + m/2) H2
Process description - Steam / Carbon ratio
control loop
Process description - Air / Fuel Gas ratio
control loop
Process description - H2 Venting (Before APC)
0
100
200
300
400
500
600
700
800
900
0
20
40
60
80
100
120
14
0
160
180
Daily Avg Flow rate (kg/h)
Average
Process description - Evolution of the LNG
Cost (USD/ton)
-
200.00
400.00
600.00
800.00
1,000.00
1,200.00
Cost
ofL
NG
($
/to
n)
LNG Cost($/ton)
Summary
1. Process description
2. Advanced Process Control
3. Modelling and Identification
4. Results
5. Conclusion
Advanced Process Control – Problem Statement
Advanced Model Predictive-based control strategies (APC) are more suitable asa solution than DCS (Digital Control System) leadlag control, since it isintrinsically multivariable and also due to the high number of disturbancevariables;
DCS Leadlag control is more likely to introduce plant variability or even lead theplant to unstable conditions due to plant-model mismatch. APC is more robust tomodel errors. Robustness in leadlag controllers are usually associated to a highlylimitation of its control signal;
APC present discrete and constrained control actions, resulting in a smootheroperation of the unit;
APC is easier to tune when compared to common leadlag controllers;
APC optimizes plant operation. DCS Leadlag control only rejects disturbances.
Advanced Process Control - Configuration
Unit Disturbance Variables Manipulated Variables
HGU-II HGU Natural Gas feed
Gasoil HDT- LCO (Light Cycle Oil)
- Coker Gasoil / Heavy Naphtha
Coker Naphtha HDT - Coker Light Naphtha
Cracked Naphtha HDS - FCC’s Light Cracked Naphtha
Diesel HDT - FCC’s Heavy Cracked Naphtha
HGU-I - H2 production to header
CCR - H2 production to header
- Manipulated variables have their setpoints or control signals defined by the advanced controller
in order to keep the process controlled variables (constraints) within their limits;
- Disturbance variables are used for feedforward control, anticipating the H2 header’s pressure
drop. The disturbance is caused by the variation on the magnitude of these variables.
Advanced Process Control - Configuration
HGU Section Equipments Controlled Variables
Feed -- Steam flow setpoint;
- Steam flow control signal.
Feed Purification- Dessulphurization Reactor
- Hydrodessulphurization Reactor-
Reformer
- Forced Air Draft Fan
- Furnace
- Induced Draft Fan
- Air flow setpoint / control signal;
- Fuel gas setpoint / control signal;
- Chamber pressure control signal.
Steam Generation- Dessuperheater
- Heat Boiler- Export Steam Temperature control signal.
Shift - Shift Reactor -
H2 Purification - PSA
- PSA’s Inlet Temperature;
- Header pressure;
- Spillback control signal
- Controlled variables represent the process constraints and must remain within their
safe operational limits;
Advanced Process Control – Control Strategy
Linear Optimizer
- Economic Function;
- Linear / Quadratic programing;
- Steady state targets.
Controller
- ARX models;
- Model Predictive Control.
DIGITAL CONTROL SYSTEM (DCS)
- Process variables;
- Human-machine Interface.
Targets
U*, Yl*
MV’s Setpoints,
Control Actions
MV’s, DV’s
and CV’s
Advanced Process Control – Control Strategy
The APC uses a two-layer control strategy:
1. Linear Optimizer
DU = Control action increment; - SCV = Slack Control Variable;
- W1 = economic coefficient; - uat = previous control action;
- W2 = supression factor; - Uinf, Usup = MV limits;
- W3 = slack variables weights; - Yinf, Ysup = CV limits;
𝐽 = minΔ𝑈,𝑆𝐶𝑉
−𝑊1Δ𝑈 + 𝑊2ΔU 22 + 𝑊3𝑆𝐶𝑉 2
2
s.t.
Δ𝑈 = 𝑈𝑆 − 𝑢𝑎𝑡
𝑈𝑆𝑖𝑛𝑓≤ 𝑈𝑆 ≤ 𝑈𝑆
𝑠𝑢𝑝
𝑌𝑆𝑖𝑛𝑓≤ 𝑌𝑆 + 𝑆𝐶𝑉 ≤ 𝑌𝑆
𝑠𝑢𝑝
Advanced Process Control – Control Strategy
The Controller is a DMC algorithm with Quadratic programming:
2. Controller
- nr = Prediction horizon; - nl = Control horizon;
- W4 = CV weight; - uinf , usup = Control signal limits;
- W5 = supression factor; - Y*, u* = Targets from the linear optimizer;
- W6 = MV weights; - Yp = prediction for the controlled variables
𝐽 = minΔ𝑈𝑖,𝑖=1,…,𝑛𝑙
𝑗=1
𝑛𝑟
𝑊4 𝑌𝑝 − 𝑌𝑙∗2
2+
𝑖=1
𝑛𝑙
𝑊5ΔU𝑖 22 +
𝑖=1
𝑛𝑙
𝑊6 𝑢𝑖−1 −
𝑘=1
𝑖
Δ𝑈𝑘 − 𝑢∗
2
2
s.t.
−Δ𝑈𝑚𝑎𝑥 ≤ Δ𝑈 ≤ Δ𝑈𝑚𝑎𝑥
𝑢𝑖𝑛𝑓≤ 𝑢𝑖−1 −
𝑖=1
𝑗
Δ𝑈𝑖 ≤ 𝑢𝑠𝑢𝑝
Summary
1. Process description
2. Advanced Process Control
3. Modelling and Identification
4. Results
5. Conclusion
Modelling and Identification – H2 header
dynamic simulation
Identification tests were performed in the real plant and generated thestep-response based ARX models;
Some disturbance variables identification tests could not be performedon site, due to reliability issues;
A dynamic simulator project was built by the time of the headerintegration and used for modelling and identification of thesedisturbance variables;
The software used for simulation is the RSI’s Indiss® suite. Consumersand producers were modelled as infinite mass generators, with the H2
consumption / production profile adjusted to match real operationvalues.
Modelling and Identification – H2 header
dynamic simulationSheet
Compressor
U262
1.18e+007 Pa308 K0.83 kg/s
-0.11 kg/sValve14
Transmitter18
612.20
Transmitter17
1264.24
0.35 kg/sPC222235
Transmitter14
3.02
0.06 kg/sValve16
0.22 kg/sValve12
0.11 kg/s20994 Pa
PipeSegment5
0.17 kg/sValve11
0.83 kg/sValve1
PIDController
4
Transmitter10
0.00
0.06 kg/sValve9
0.22 kg/sValve8
0.18 kg/sValve7
0.00 kg/sValve6
Q
K
Transmitter15
20.00
Transmitter16
19.96
0.22 kg/s11362 Pa
PipeSegment12
PIDController
17
Transmitter28
0.80
U272D
1.40e+006 Pa303 K0.22 kg/s
0.22 kg/sValve34
0.06 kg/s8157 Pa
PipeSegment13
PIDController
18
Transmitter29
0.23
U272NQ
1.40e+006 Pa303 K0.06 kg/s
0.06 kg/sValve35
0.17 kg/s2993 Pa
PipeSegment11
0.11 kg/s1164 Pa
PipeSegment10
0.11 kg/sValve5
Transmitter12
20.32
Transmitter11
20.24
Transmitter9
20.10
Transmitter7
19.29
Transmitter6
19.53
Transmitter5
20.07
Transmitter4
19.44
0.06 kg/s265 Pa
PipeSegment9
OL
BCF
0.00 kg/s8882 Pa
PipeSegment8
PIDController
15
Transmitter27
0.02
U238
1.40e+006 Pa303 K0.00 kg/s
0.00 kg/sValve33
PIDController
3
0.00 kg/sValve3
0.35 kg/s3773 Pa
PipeSegment3
Transmitter2
19.08
PIDController
2
0.00 kg/sValve2
-0.11 kg/s-24392 Pa
PipeSegment2
Transmitter1
19.44
PIDController
1
0.00 kg/sPV294012
0.18 kg/s595 Pa
PipeSegment6
0.78 kg/s52494 Pa
PipeSegment4
0.83 kg/s14843 Pa
PipeSegment1
U294
2.20e+006 Pa303 K0.83 kg/s
0.00 kg/sValve32
Tocha
1.00e+004 Pa302 K0.00 kg/s
Transmitter26
19.29
PIDController
13
PIDController
12
Transmitter24
0.64
Transmitter23
0.40
U264
1.40e+006 Pa303 K0.18 kg/s
U266
1.40e+006 Pa303 K0.11 kg/s
U222
3.00e+006 Pa303 K0.35 kg/s
U292
3.00e+006 Pa303 K0.17 kg/s
0.18 kg/sValve27
0.11 kg/sValve26
ConsumersProducers
H2 header
Compressor
Vent valves
Modelling and Identification – Spillback control
dynamic simulation
Transmitter32
3.05
Transmitter31
18.71
PIDController
6
0.92 kg/sValve21
Sheet
Compressor1
V26208
Level :0.00 %
Pressure :3.01e+006 Pa
Transmitter30
29.71
0.83 kg/sValve22
>
ARout
1.30e+005 Pa299 K26.19 kg/s
ARin
2.50e+005 Pa298 K26.19 kg/s
0.00 kg/sValve20
Condensado1
1.40e+006 Pa328 K0.00 kg/s
0.14 kg/sPV262037
P262130.09 kg/s
PV262034
PIDController
5
Transmitter8
50.61
0.78 kg/sValve19
0.00 kg/sValve15
Condensado
1.40e+006 Pa302 K0.00 kg/s
V26207
0.00 %Lev el :
Spillback Vessel
H2 from header
Recycle Valve
Modelling and Identification – Compressor
dynamic simulation
ARout4
1.30e+005 Pa300 K47.15 kg/s
ARin4
2.50e+005 Pa298 K47.15 kg/s
P26217
ARout3
1.30e+005 Pa300 K47.15 kg/s
ARin3
2.50e+005 Pa298 K47.15 kg/s
P26216
ARout2
1.30e+005 Pa300 K47.15 kg/s
ARin2
2.50e+005 Pa298 K47.15 kg/s
P26215
ARout1
1.30e+005 Pa300 K47.15 kg/s
ARin1
2.50e+005 Pa298 K47.15 kg/s
P26214
FloatBox190
FloatBox
0.92 kg/sValve17
Transmitter19
61.90
Transmitter20
37.41
Transmitter21
36519.97
Transmitter3
60.36
Transmitter22
41.47
0.92 kg/sValve10
0.44 kg/sValve4
ReciprocatingCompressor8 0.48 kg/s
ReciprocatingCompressor7 0.48 kg/s
ReciprocatingCompressor6 0.48 kg/s
ReciprocatingCompressor4 0.44 kg/s
ReciprocatingCompressor3 0.44 kg/s
ReciprocatingCompressor2 0.44 kg/s
Transmitter13
17542.85
Modelling and Identification – H2 header
integration dynamic simulation
Real Plant Virtual Plant
Modelling and Identification – H2 header with
Spillback control dynamic simulation
17
17.5
18
18.5
19
19.5
20
0
100
200
300
400
500
600
700
HG
U-I H
2 P
rod
uctio
n(k
g/h
)
Time (minutes)
H2 H
ead
er
Pre
ssu
re(k
gf/
cm
²)
HGU-I H2 production
Header Pressure with Spillback
Header Pressure without Spillback
Spillback pressure
control
Modelling and Identification – APC model
Matrix (ARX)
- First-Order Plus Dead-Time models; - Time Sample = 1 minute, Settling Time Tr = 120 minutes
Summary
1. Process description
2. Advanced Process Control
3. Modelling and Identification
4. Results
5. Conclusion
Results
The following results show the application of the APC strategy in the
real plant;
The data set is collected from the historian software for a period of
time of 150 days after the APC start-up and comissioning and
compared to the units operation before the APC project;
All sampled data (before / after APC) was treated to match regular
steady-state operational conditions only, in order to correctly
evaluate the control strategy performance. The data that did not
satisfy the analysis conditions were discarded.
Results - APC in Real Plant Operation
82.00
82.50
83.00
83.50
84.00
84.50
85.00
85.50
86.00
86.50
87.00
50.00
55.00
60.00
65.00
70.00
75.00
80.00
85.00
CV
(%
of
sp
an
)
Time (minutes)
MV
/ DV
(% o
fsp
an
)
H2 header pressure (APC Controlled variable)
HDT-GOK LCO Feed (APC Disturbance variable)
Time Sample Ts = 1min; Prediction Horizon nr = 120min, Control Horizon nl = 8min:
HGU LNG feed (APC Manipulated
Variable)
HDT-GOK H2 consumption
Results – APC in Real Plant Operation
75.00
77.00
79.00
81.00
83.00
85.00
87.00
45.00
55.00
65.00
75.00
85.00
95.00
CV
(%
of
sp
an
)
Time (minutes)
MV
/ DV
(% o
fsp
an
)
HGU LNG feed (APC Manipulated Variable)
H2 header pressure (APC Controlled variable)
HGU-I H2 production to header (APC disturbance
variable
HDT-GOK Spillback Presure ControlCV control limits
Results - Economic Assessment
0
5
10
15
20
25
30
35
40
45
50
0
100
200
300
400
500
600
700
800
9000
20
40
60
80
10
0
12
0
14
0
16
0
18
0
20
0
22
0
24
0
26
0
28
0
30
0
32
0
Ven
tO
pen
ing
(%)H
2fl
ow
tofl
are
(kg
/h)
Time (days)
Daily Avg Venting (%)
Daily Avg Flow rate (kg/h)
Avg before / after APC
APC Start-up
Results - Economic Assessment
0
0.5
1
1.5
2
2.5
3
3.5
4
0
20
40
60
80
10
0
12
0
14
0
16
0
18
0
20
0
22
0
24
0
26
0
28
0
30
0
32
0
Excess
Natu
ral
Gas
Flo
w(t
/h)
Time (days)
Daily Avg Flow rate (kg/h)
Avg before / after APC
APC Start-up
Results - Economic Assessment
Averages
APC off APC On D
H2 Venting (%) 5.64 0.78 -4,86
H2 Loss to Flare (kg/h) 415.99 57.74 -358.25
Excess LNG Flow (t/h) 1.71 0.25 -1.46
Economic Loss (USD/month) 920k 130k -790k
𝐸𝑐𝑜𝑛𝑜𝑚𝑖𝑐 𝑆𝑎𝑣𝑖𝑛𝑔𝑠 = 𝐶𝐿𝑁𝐺 ∗ 1 + 𝑄𝐹𝐺𝑄𝐻𝐺𝑈
∗ ∆𝑄𝑁𝐺
DQLNG = Excess Natural Gas Flow variation in t/monthQFG = Natural gas to reformer nominal flow;QHGU = HGU natural gas nominal flow; 𝐶𝐿𝑁𝐺 = 𝐴𝑣𝑒𝑟𝑎𝑔𝑒 𝐿𝑁𝐺 𝐶𝑜𝑠𝑡 = 750$/𝑡
Results - Economic Assessment
$-
$200.00
$400.00
$600.00
$800.00
$1,000.00
$1,200.00
nov-13 dez-13 jan-14 fev-14 mar-14
0.00%
20.00%
40.00%
60.00%
80.00%
100.00%
Savin
gs
( x1000 )
Tim
e O
n(%
)
nov-13 dez-13 jan-14 fev-14 mar-14
Time On 55.07% 53.40% 88.01% 59.81% 90.76%
Savings $385,51 $373,81 $616,08 $424,80 $635,31
Time On
Savings
Summary
1. Process description
2. Advanced Process Control
3. Modelling and Identification
4. Results
5. Conclusion
Conclusion
The APC improved the operational reliability by anticipating the
hydrogen consumption variation of the hydrotreating units;
The APC have shown to be a more suitable solution than regulatory-
based leadlag control due to the high number of disturbance
variables;
The economic befenits achieved by the APC control are expressive
when compared to the low cost of implementation;
Dynamic simulation is a powerfull tool for modelling and identification
and improved the control system reliability.
Conclusion – Additional optimization variables
Hydrogen production optimization is not limited to vent minimization.
Other optimization variables include:
O2 excess control (increase the reformer’s thermal efficiency);
Steam / Carbon ratio (minimize steam consumption);
Reformer’s outlet temperature control (Catalyst savings);
Shift reactor inlet temperature (maximize H2 recovery in the PSA
system);
PSA’s operational factor (optimize header CO / CO2 content)
Optimization of H2 Production in a
Hydrogen Generation Unit
Márcio R. S. Garcia1,
Renato N. Pitta2,
Gilvan A. G. Fischer2,
André S. R. Kuramoto2
1Radix Engenharia e Desenvolvimento de Software Ltda, Rio de Janeiro, RJ, Brazil (e-mail:
2Refinaria Henrique Lage, São José dos Campos, SP, Brazil (e-mail: [email protected] ,