Optimizing Diesel Production Using Advanced Process Control and Dynamic Simulation

Optimizing Diesel Production UsingAdvanced Process Control and Dynamic

Simulation

Márcio R. S. Garcia1,

Renato N. Pitta2,

Gilvan A. G. Fischer2,

Enéas R. N. Neto2

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 – Diesel Blending System

The Diesel blending system of REVAP (Henrique Lage Refinery), located in the state of São Paulo, is composed of three product streams: The Diesel from the Gasoil Hydrotreating Unit (HDT-GOK); the Diesel from the Diesel Hydrotreating Unit (HDT-D) and the Heavy Naphtha from the Naphtha Splitter Column;

The three streams are blended to compose the S-10 / 500 Diesel (Diesel with maximum Sulfur content of 10 / 500 ppm) product;

The Naphtha Stream has a large impact on the Diesel’s flash point (The temperature in which the hydrocarbonate vapor sparks in the presence of an ignition source).

The Diesel flash point must be constantly monitored in order to avoid the products off-specification, which represents large economic losses to the refinery due to the necessity of reprocessing

Process description – Diesel Blending System

* 7% of Diesel production, but large impact on the flash point.

*

Process description – Naphtha Splitter

Process description – Diesel Blending Profile

Diesel from HDT-GOK

Diesel from HDT-D

Heavy Naphtha

45%

48%

7%

Summary1. Process description



4. Results

5. Conclusion

Advanced Process Control – Problem Statement

The Naphtha feed was kept in Automatic mode, fixed setpoint. The temperature of the column was also kept in a fixed setpoint. There was no optimization in the split process and the splitter operated most of the time away from the limit of its capacity;

The column’s feed vessel used to flood most of the times that the operators tried to increased the processed feed;

The Naphtha flash point or T5% (The temperature directly related to the initial boiling point) are not measured. There are no analyzers in the Naphtha outlet stream and the Diesel’s flash point was always far from its minimum;

The processed feed, medium pressure (MP) steam and top reflux form a highly multivariable system. APC strategies are intrinsically multivariable and the most suitable solution for the plant optimization. Also, it can easily reject the disturbances cause by the changes in the feed composition, finding the best operation point on a real-time basis.

Advanced Process Control - Configuration

Manipulated Variables Controlled Variables

- Splitter’s Feed - Splitter’s temperature

- Medium Pressure Steam flow - Bottom level controller output signal

- Top Reflux flow- Splitter’s pressure controller output

signal

- Naphtha flash point (inferential)

- Reflux calculated ratio (inferential)

- Heavy Naphtha / T5% ratio (inferential)

- 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;

- Controlled variables represent the process constraints and must remain within their safe operational limits;

- Inferential variables are controlled variables that are not directly measured, but inferred from the operational conditions.

Advanced Process Control – Naphtha SplitterManipulated

Variables

ControlledVariables

Reflux Ratio

Naphtha’s Flash Point

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.

TargetsU*, 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;

s.t.

Advanced Process Control – Control StrategyThe 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

s.t.

Summary




4. Results

5. Conclusion

Modelling and Identification – Naphtha Splitter

Identification tests were performed in the real plant and generated the step-response based ARX models. Field tests presented poor models for the inferential variables, due to the limitation of the step tests. Also, there is no online analyzer for the Naphtha’s flash point;

The inferential models were obtained based on laboratory sampled data in different operational conditions. The Naphtha’s flash point was not measured since there are no analyzers covering its expected magnitude;

A dynamic simulator project was built in order to provide engineers with all information necessary for the inferential variables modelling and Identification. Also, the dynamic simulator was used to evaluate the APC strategy;

The software used for simulation is the RSI’s Indiss® suite. The simulation included the Splitter and the Diesel from HDT-D and from the Gasoil HDT stream, which were used to provide the Naphtha’s flash point inferential model.

Modelling and Identification – Inferential variables

1. T5% Inferential model

- QTR = Top Reflux flow; - Rgas = Gas universal constant;

- QHN = Heavy Naphtha flow; - A, B, C = Inferential model constants;

- PHN = Heavy Naphtha pressure; - bias = inferential model adjust parameter;

- THN = Heavy Naphtha temperature;Parameters

A B C Rgas (cal*K-1*mol-1)

Value 9.44 0.7045

-3.7463

1.9872

Modelling and Identification – Regression data for T5% inferential model

108 110 112 114 116 118 120104.00

106.00

108.00

110.00

112.00

114.00

116.00

118.00

120.00

f(x) = 0.922168454287002 x + 8.74682443521313R² = 0.922168454287013

T5% (Inferential)

T5

% (

Lab

ora

tory

)

Modelling and Identification – Inferential variables

2. Heavy Naphtha / T5% ratio

3. Reflux Calculated Ratio

QTR = Top Reflux flow; QLN = Light Naphtha flow; QHN = Heavy Naphtha flow;

𝑅𝑄𝑇 5=𝑄𝐻𝑁

𝑇 5%

𝑅𝑅=𝑄𝑇𝑅

𝑄𝑇𝑅+𝑄𝐿𝑁

Modelling and Identification – Naphtha Splitter dynamic simulation

Transmitter1

74.43

Sheet

Sheet1

T5

111.92 +

T0

107.41

B8423.45 kg/s

FI785

29.42

FI783

28.56

PI7837.85

TI789

25.40

TI785

25.10

TI786

45.91

TI784

45.95

TI783

39.51

TI788

169.30

TI790

169.30

TI791

174.81

TI792

92.14

TI782

102.84

TI781

108.33

DS501

1.01e+005 Pa352 K29.54 kg/s

29.54 kg/sValve27

2.70 kg/sValve26

2.70 kg/sValve25

0.00 kg/sValve3

11.39 kg/sValve2

CargaT21080v2

6.00e+006 Pa366 K11.39 kg/s

OPCClient

OPCClient1

RFC786

84.29

HOSTFC

CIN786

RLC781

65.69

HOSTLC

CIN781

RTC7870.00

HOSTTC

CIN787

RFC7840.00

HOSTFC

CIN784

RLC782

67.82

HOSTLC

CIN782

RPC781B0.00

HOSTPC

CIN781B

RPC781A6.00

HOSTPC

CIN781A

RFC782

27.03

HOSTFC

CIN782

RPC782

42.41

HOSTPC

CIN782

RFC781

73.56

HOSTFC

CIN781

TB90

152.08

TB10

56.36

Flash5

-40.42

AnalyserDEE

AnalyserDEE5

Flash3

37.37

AnalyserDEE

AnalyserDEE4

Flash2

47.23

FI2620.00

FC262

FC272

FI272

100.00

0.00 kg/sValve24

23.45 kg/sValve23

DS500

1.01e+005 Pa354 K23.45 kg/s

DS50

1.01e+005 Pa345 K0.00 kg/s

BatteryLimit2

1.01e+005 Pa345 K0.00 kg/s

BatteryLimit1

1.01e+005 Pa290 K0.00 kg/s

0.00 kg/sValve22

0.00 kg/sValve21

0.00 kg/sValve206.25 kg/sValve4

AnalyserDEE

AnalyserDEE3

23.30 kg/sFC272086

0.00 kg/sFC26219

S500

Level : 100.00 %

Temperature : 354.23 K

S50

Level : 0.00 %

Temperature : 345.29 K

U272D

1.00e+006 Pa366 K23.30 kg/s

U262

1.00e+006 Pa366 K0.00 kg/s

Flash1

9.57

T10

116.42

AnalyserDEE

AnalyserDEE2

P82

Ta10

43.03

AnalyserDEE

AnalyserDEE1

Flash

-51.90

Flow Prod

29.34

FI781

60.32

15.73 kg/sValve19

AguaResf2

2.00e+005 Pa298 K15.73 kg/s

15.73 kg/sValve18

SAguaResf2

1.01e+005 Pa348 K15.73 kg/s

41.30 kg/sValve17

SAguaResf1

1.01e+005 Pa300 K41.30 kg/s

41.30 kg/sValve16

AguaResf1

2.00e+005 Pa298 K41.30 kg/s

68.46 kg/sValve15

68.46 kg/sValve14

SAguaResf

1.01e+005 Pa305 K68.46 kg/s

AguaResf

2.00e+005 Pa298 K68.46 kg/s

32.77 kg/sValve13

32.77 kg/sValve11

TI787

150.23

TC787

FI7865.62

FC786

FC781

FI782

12.01

FC782

5.61 kg/sValve9

6.25 kg/sValve8

FI784

29.42

FC784

0.00 kg/sV784

6.25 kg/sValve6

PC781A

LC782

LI782

47.90

LC781

LI781

50.05

PC781B

PI7810.99

PC782

PI7821.51

6.25 kg/sV782

NaftaPetr

1.00e+005 Pa298 K5.61 kg/s

7.84 kg/sValve12

Tocha

1.01e+005 Pa319 K0.00 kg/s

0.00 kg/sV781B

7.90 kg/sValve10

7.61 kg/sV787

0.23 kg/sV781A

7.84 kg/sValve7

V21080

P ressure : 9.53e+004 P a

Level : 50.05 %Temperature : 319.10 K

1.56 kg/sV786

1.56 kg/sValve1

C16

1.50e+006 Pa444 K1.56 kg/s

V16

1.75e+006 Pa523 K1.56 kg/s

5.61 kg/sV783

2.29 kg/sV785

B836.25 kg/s

B817.90 kg/s

P85

P81

P86

11.39 kg/sValve5

GOL1

5.00e+005 Pa365 K2.70 kg/s

11.39 kg/sV781

P65

GOL

1.09e+006 Pa453 K2.70 kg/s

CargaT21080

6.00e+006 Pa366 K0.00 kg/s

Fundo

47.90 %442 K

2.8e+005 Pa

T210802.47e+005 Pa

369.852

Naphtha Splitter

Splitter’s Feed

Blending System

HDT-D Diesel

HDT-GOK Diesel

Medium Pressure Steam Virtual Plant

Heavy Naphtha analyzer

Modelling and Identification – Naphtha’s Flash point vs T5% (dynamic simulation)

7.00

7.50

8.00

8.50

9.00

9.50

10.00

10.50

f(x) = 0.846857386941746 x − 85.0796143310715R² = 0.998597702601914

Naphtha’s T5% (oC)

Nap

hth

a’s

Fla

sh

Poin

t (o

C)

Modelling and Identification – Naphtha’s Flash Point

The bias of the T5% inferential model can be adjusted with a Hu-Burns mixing rules backcalculation method:

Flash Point:

- Pic = Flash point for the i-stream, in ºC; - FPIi = Flash Point Index, i-stream;

- QDS = Blended Diesel flow; - Qi = i-stream flow;

- FPIDS = Flash Point Index, blended Diesel stream;

- PcDS = Flash Point of the blended Diesel stream, in ºF;

𝑃𝑐𝑖=255.372∗( 𝐹𝑃 𝐼𝑖

𝑘1)𝑘2

−273.15

Parameters

k1 k2 X

Value 104 -0.038

-0.06

𝐹𝑃 𝐼𝐷𝑆=( 1𝑄𝐷𝑆

)∑𝑖=1

𝑛

𝐹𝑃 𝐼𝑖∗𝑄𝑖 𝐹𝑃 𝐼𝐷𝑆=(𝑃𝑐

𝐷𝑆+459.69)1 /𝑋∗104

(459.69 )1 /𝑋

Modelling and Identification – Real Plant vs Virtual plant model comparative

0 8 16 24 32 40 48 56 64 72 80 88 96 104 112

-10

-8

-6

-4

-2

0

2

4

6

8

10

Splitter's Feed (varia-tion)

Real Plant Step Response

Time (minutes)

5% (

)D

ToC

Modelling and Identification – Conclusion

The simulator can provide the inferential variables that can not be measured in the real plant;

The curve fitting parameter shows that the virtual plant, when compared to the real plant models, can be used for modelling and Identification of the real plant. Virtual plant-based models have shown consistent results.

Model Regression R2 Fitting parameters

Feed Steam Flow Reflux Flow

Real Virtual Real Virtual Real Virtual

Splitter’s Temperature 0.968 0.987 0.872 0.842 0.948 0.893

Level Controller Output signal 0.886 0.905 0.750 0.529 0.657 0.737

T5% 0.976 0.972 0.611 0.657 0.862 0.861

Modelling and Identification – APC model Matrix (ARX)

- First-Order Plus Dead-Time models; - Time Sample = 1 minute, Settling Time Tr = 60 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 six-month period of time 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 this condition was discarded.

Results - APC in Real Plant Operation

0 15 30 45 60 75 90 105

120

135

150

165

180

195

210

225

240

255

270

285

300

315

25.00

30.00

35.00

40.00

45.00

50.00

25.00

30.00

35.00

40.00

45.00

50.00

Time (days)

Da

ily-a

ve

rag

e H

ea

vy N

ap

hth

a F

low

(m

³/h

)

Time Sample Ts = 1min; Prediction Horizon nr = 60min, Control Horizon nl = 8min:

APC Start-up


0 15 30 45 60 75 90 105

120

135

150

165

180

195

210

225

240

255

270

285

300

315

50.00

55.00

60.00

65.00

70.00

75.00

80.00

85.00

90.00

95.00

100.00

50.00

55.00

60.00

65.00

70.00

75.00

80.00

85.00

90.00

95.00

100.00

Time (days)

Da

ily-a

ve

rag

e S

pli

tte

r’s F

ee

d (

m³/

h)

APC Start-up


0 15 30 45 60 75 90 105

120

135

150

165

180

195

210

225

240

255

270

285

300

315

15.00

20.00

25.00

30.00

35.00

40.00

45.00

50.00

15.00

20.00

25.00

30.00

35.00

40.00

45.00

50.00

Time (days)

He

avy N

ap

hth

a v

s L

igh

t N

ap

hth

a

Flo

w (

m³/

h)

APC Start-up


0 15 30 45 60 75 90 105

120

135

150

165

180

195

210

225

240

255

270

285

300

315

40.00%

45.00%

50.00%

55.00%

60.00%

65.00%

70.00%

40.00%

45.00%

50.00%

55.00%

60.00%

65.00%

70.00%

Time (days)

Sp

lit

Pro

file

(%

)

APC Start-up


0 10 20 30 40 50 60 70 80 90100

110120

130140

150160

170180

190200

0

10

20

30

40

50

60

70

Flash Point Mean + std - std Spec

days

Ble

nd

ed

Die

sel Fla

sh P

oin

t (o

C)

Before APC After APC DAverage Blended Diesel Flash Point giveaway (single tank) 8,49 oC 3.82 oC - 54,8%


Avg: 46.28oCStd: 2.18oC

Avg: 52.08oCStd: 5.03oC

Specification: 41.5oC

Results - Economic Assessment

Averages

Before APC After APC D

Processed Feed (m³/h) 67.27 80.49 19,64%

Heavy Naphtha flow (m³/h) 34.65 44.79 29.27%

MP Steam / Feed ratio (ton/m³)

88.73 93.96 5.90%

Split (%) 51.33 55.61 8.33%

𝑌𝑖𝑒𝑙𝑑=(𝐺1∗ Δ𝑄𝐻𝑁+𝐺2∗ Δ𝑄𝑆𝑇𝐸𝐴𝑀)∗𝑇 𝑂𝑁G1 = Price difference between Diesel and Naphtha in $/m³G2 = MP steam cost, in $/ton;DHN = ;;TON

Summary




4. Results

5. Conclusion

Conclusion

The APC improved the operational reliability by compensanting the variations on feed quality and maintining the splitter in its optimal operation point;

The economic benefits achieved by the APC control are expressive when compared to the low cost of implementation. Based on the actual costs of Diesel and Naphtha, the economic yields of the APC implementation are calculated in over $5MM.

Dynamic simulation is a powerfull tool for modelling and identification and improved the control system reliability. This tool was fundamental to provide inferential models for the variables that would, otherwise, not be controlled.

Optimizing Diesel Production UsingAdvanced Process Control and Dynamic

Simulation

Márcio R. S. Garcia1,

Renato N. Pitta2,

Gilvan A. G. Fischer2,

Enéas R. N. Neto2

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] )





Optimizing Diesel Production Using Advanced Process Control and Dynamic Simulation

Engineering

Transcript of Optimizing Diesel Production Using Advanced Process Control and Dynamic Simulation