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:

[email protected])

2Refinaria Henrique Lage, São José dos Campos, SP, Brazil (e-mail: [email protected] ,

[email protected] , [email protected] )

mailto:[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




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


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


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




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.


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






Transmitter13

17542.85


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




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 (%)


Avg before / after APC

APC Start-up


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)


Avg before / after APC

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

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


$-

$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




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:

[email protected])

2Refinaria Henrique Lage, São José dos Campos, SP, Brazil (e-mail: [email protected] ,

[email protected] , [email protected] )





Engineering

Optimization of H2 Production in a Hydrogen Generation Unit