Optimization of H2 Production in a Hydrogen Generation Unit

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:
marcio.garcia@radixeng.com.br)
2Refinaria Henrique Lage, São José dos Campos, SP, Brazil (e-mail: renato.pitta@petrobras.com.br ,
gilvan@petrobras.com.br , kuramoto@petrobras.com.br )

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
4.60%
0.40%
7.00%
19.00%
16.00%
53.00%
Gasoil HDT
Diesel HDT
Cracked Naphtha HDS
Coker Naphtha HDT
Naphtha / Kerosine HDT
Tail Gas

Process description - H2 production profile
61%
18%
21%
HGU-I
CCR
HGU-II

Process description - H2 Generation Process
CnHm + n H2O n CO + (n + m/2) H2
n CO + n H2O n CO2 + n H2

Process description - Steam / Carbon ratio
control loop

Process description - Air / Fuel Gas ratio
control loop

Process description - H2 Venting (Before APC)
900
800
700
600
500
400
300
200
100
0
0
20
40
60
80
100
Daily Avg Flow rate (kg/h)
120
140
160
180
Average

Process description - Evolution of the LNG
Cost (USD/ton)
1,200.00
1,000.00
800.00
600.00
400.00
200.00
-
Cost of LNG ($/ton)
LNG Cost
($/ton)

Advanced Process Control – Problem Statement
 Advanced Model Predictive-based control strategies (APC) are more suitable as
a solution than DCS (Digital Control System) leadlag control, since it is
intrinsically multivariable and also due to the high number of disturbance
variables;
 DCS Leadlag control is more likely to introduce plant variability or even lead the
plant to unstable conditions due to plant-model mismatch. APC is more robust to
model errors. Robustness in leadlag controllers are usually associated to a highly
limitation of its control signal;
 APC present discrete and constrained control actions, resulting in a smoother
operation 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
- 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.
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

Advanced Process Control - Configuration
- Controlled variables represent the process constraints and must remain within their
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
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

The APC uses a two-layer control strategy:
1. Linear Optimizer
퐽 = min
Δ푈,푆퐶푉
−푊1Δ푈 + 푊2ΔU 2 2
+ 푊3푆퐶푉 2 2
s.t.
Δ푈 = 푈푆 − 푢푎푡
푈푖푛푓 ≤ 푈≤ 푈푆
푆 푆
푠푢푝
푖푛푓 ≤ 푌푆 + 푆퐶푉 ≤ 푌푆
푌푆
푠푢푝
 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;

The Controller is a DMC algorithm with Quadratic programming:
2. Controller
퐽 = min
Δ푈푖,푖=1,…,푛푙
푛푟
푗=1
∗
푊4 푌푝 − 푌푙
2
+
2
푛푙
푖=1
푊5ΔU푖 2 2
+
푛푙
푖=1
푊6 푢푖−1 −
푖
푘=1
Δ푈푘 − 푢∗
푗
- 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
2
2
s.t.
−Δ푈푚푎푥 ≤ Δ푈 ≤ Δ푈푚푎푥
푢푖푛푓 ≤ 푢푖−1 −
푖=1
Δ푈푖 ≤ 푢푠푢푝

Modelling and Identification – H2 header
dynamic simulation
 Identification tests were performed in the real plant and generated the
step-response based ARX models;
 Some disturbance variables identification tests could not be performed
on site, due to reliability issues;
 A dynamic simulator project was built by the time of the header
integration and used for modelling and identification of these
disturbance variables;
 The software used for simulation is the RSI’s Indiss® suite. Consumers
and producers were modelled as infinite mass generators, with the H2
consumption / production profile adjusted to match real operation
values.

dynamic simulation
Sheet
Compressor
1.18e+007 Pa
308 K
0.83 kg/s
U262
Valve1
PV294012
Valve14
-0.11 kg/s
Transmitter
18
612.20
Valve11
Transmitter
PipeSegment11
Transmitter
20.07
PipeSegment10
Transmitter
17
1264.24
5
PC222235
0.35 kg/s
Transmitter
14
3.02
Valve12
0.22 kg/s
Valve16
0.06 kg/s
PipeSegment4
PipeSegment5
0.11 kg/s
20994 Pa
0.17 kg/s
0.83 kg/s
PIDController
4
PipeSegment1
Transmitter
10
0.00
1.00e+004 Pa
302 K
0.00 kg/s
Transmitter
Valve6
0.00 kg/s
Valve7
0.18 kg/s
11
20.24
PipeSegment8
PipeSegment6
Transmitter
Valve8
0.22 kg/s
Valve9
0.06 kg/s
Q
K
16
19.96
PipeSegment9
PipeSegment12
Transmitter
15
20.00
0.22 kg/s
11362 Pa
Valve5
Valve26
Valve33
Valve27
PIDController
17
Transmitter
28
0.80
1.40e+006 Pa
303 K
0.11 kg/s
U266
1.40e+006 Pa
303 K
0.00 kg/s
1.40e+006 Pa
303 K
0.18 kg/s
1.40e+006 Pa
303 K
0.22 kg/s
U272D
Valve34
0.22 kg/s
PipeSegment13
0.06 kg/s
8157 Pa
PIDController
18
Transmitter
29
0.23
1.40e+006 Pa
303 K
0.06 kg/s
U272NQ
Valve35
0.06 kg/s
0.17 kg/s
2993 Pa
0.11 kg/s
1164 Pa
0.11 kg/s
Transmitter
12
20.32
Transmitter
9
20.10
Transmitter
7
19.29
Transmitter
6
19.53
4
19.44
0.062 6k5g /Psa
OL
BCF
0.00 kg/s
8882 Pa
PIDController
15
Transmitter
27
0.02
U238
0.00 kg/s
PIDController
3
Valve3
0.00 kg/s
PipeSegment2
Valve32
PipeSegment3
0.35 kg/s
3773 Pa
Transmitter
2
19.08
PIDController
2
Valve2
0.00 kg/s
-0.11 kg/s
-24392 Pa
Transmitter
1
19.44
PIDController
1
0.00 kg/s
0.18 kg/s
595 Pa
0.78 kg/s
52494 Pa
0.83 kg/s
14843 Pa
2.20e+006 Pa
303 K
0.83 kg/s
U294
0.00 kg/s
Tocha
Transmitter
26
19.29
PIDController
13
PIDController
12
Transmitter
24
0.64
Transmitter
23
0.40
U264
3.00e+006 Pa
303 K
0.17 kg/s
U292
3.00e+006 Pa
303 K
0.35 kg/s
U222
0.18 kg/s
0.11 kg/s
Consumers
Producers
H2 header
Compressor
Vent valves

Modelling and Identification – Spillback control
dynamic simulation
Transmitter
32
3.05
PV262037
1.30e+005 Pa
299 K
26.19 kg/s
PIDController
6
Transmitter
31
18.71
Valve21
0.92 kg/s
PIDController
5
PV262034
Valve20
Sheet
1.40e+006 Pa
328 K
0.00 kg/s
Compressor1
V26208
Pressure :
3.01e+006 Pa
Level :
0.00 %
Transmitter
30
29.71
Valve22
0.83 kg/s
>
ARout
Recycle Valve
2.50e+005 Pa
298 K
26.19 kg/s
ARin
0.00 kg/s
Condensado1
0.14 kg/s
P26213
0.09 kg/s
Transmitter
8
50.61
Valve19
0.78 kg/s
Valve15
0.00 kg/s
1.40e+006 Pa
302 K
0.00 kg/s
Condensado
V26207
Lev el :
0.00 %
Spillback Vessel
H2 from header

Modelling and Identification – Compressor
dynamic simulation
1.30e+005 Pa
300 K
47.15 kg/s
1.30e+005 Pa
300 K
47.15 kg/s
ARout4
2.50e+005 Pa
298 K
47.15 kg/s
2.50e+005 Pa
298 K
47.15 kg/s
ARin4
P26217
ARout3
ARin3
P26216
1.30e+005 Pa
300 K
47.15 kg/s
1.30e+005 Pa
300 K
47.15 kg/s
ARout2
2.50e+005 Pa
298 K
47.15 kg/s
2.50e+005 Pa
298 K
47.15 kg/s
ARin2
P26215
ARout1
ARin1
P26214
FloatBox
FloatBox1
90
Valve17
0.92 kg/s
Transmitter
19
61.90
Transmitter
20
37.41
Transmitter
21
36519.97
Transmitter
3
60.36
Transmitter
22
41.47
Valve10
0.92 kg/s
ReciprocatingCompressor8
0.48 kg/s
Valve4
0.44 kg/s
0.48 kg/s
0.48 kg/s
0.44 kg/s
0.44 kg/s
0.44 kg/s
Transmitter
13
17542.85

integration dynamic simulation
Real Plant Virtual Plant

Modelling and Identification – H2 header with
Spillback control dynamic simulation
20
19.5
19
18.5
18
17.5
17
700
600
500
400
300
200
100
0
HGU-I H2 Production (kg/h)
Spillback pressure
Time (minutes)
H2 Header Pressure (kgf/cm²)
control
HGU-I H2 production
Header Pressure with Spillback
Header Pressure without Spillback

Modelling and Identification – APC model
Matrix (ARX)
- First-Order Plus Dead-Time models; - Time Sample = 1 minute, Settling Time Tr = 120 minutes

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
Time Sample Ts = 1min; Prediction Horizon nr = 120min, Control Horizon nl = 8min:
87.00
86.50
86.00
85.50
85.00
84.50
84.00
83.50
83.00
82.50
82.00
85.00
80.00
75.00
70.00
65.00
60.00
55.00
50.00
CV (% of span)
HGU LNG feed (APC Manipulated
Variable)
HDT-GOK H2 consumption
Time (minutes)
MV / DV (% of span)
H2 header pressure (APC Controlled variable)
HDT-GOK LCO Feed (APC Disturbance variable)

Results – APC in Real Plant Operation
87.00
85.00
83.00
81.00
79.00
77.00
75.00
95.00
85.00
75.00
65.00
55.00
45.00
CV (% of span)
H2 header pressure (APC Controlled variable)
HDT-GOK Spillback Presure Control
Time (minutes)
MV / DV (% of span)
HGU LNG feed (APC Manipulated Variable)
HGU-I H2 production to header (APC disturbance
variable
CV control limits

Results - Economic Assessment
50
45
40
35
30
25
20
15
10
5
0
900
800
700
600
500
400
300
200
100
0
Daily Avg Venting (%)
Avg before / after APC
0
20
40
60
80
100
120
140
160
180
200
220
240
260
280
300
320
Vent Opening (%)
H2 flow to flare (kg/h)
Time (days)
APC Start-up

4
3.5
3
2.5
2
1.5
1
0.5
0
0
20
40
60
80
100
120
140
160
180
Avg before / after APC
200
220
240
260
280
300
320
Excess Natural Gas Flow (t/h)
Time (days)
APC Start-up

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
퐸푐표푛표푚푖푐 푆푎푣푖푛푔푠 = 퐶퐿 푁퐺 ∗ 1 +
푄퐹퐺
푄퐻퐺푈
∗ Δ푄푁퐺
DQLNG = Excess Natural Gas Flow variation in t/month
QFG = Natural gas to reformer nominal flow;
QHGU = HGU natural gas nominal flow;
퐶퐿 푁퐺 = 퐴푣푒푟푎푔푒 퐿푁퐺 퐶표푠푡 = 750$/푡

$1,200.00
$1,000.00
$800.00
$600.00
$400.00
$200.00
$-
Time On
Savings
nov-13 dez-13 jan-14 fev-14 mar-14
100.00%
80.00%
60.00%
40.00%
20.00%
0.00%
Savings ( x1000 )
Time On (%)
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

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

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