Europe User Conference: ADNOC optimization of hydrocracking process as a function of operating conditions

ABU DHABI NATIONAL OIL COMPANYABU DHABI NATIONAL OIL COMPANY
OPTIMIZATION OF HYDROCRACKING PROCESS
AS A FUNCTION OF OPERATING CONDITIONS:
APPLICATION OF RESPONSE SURFACE METHODOLOGY
MR. RIZWAN AHMED & DR. HAITEM HASSAN-BECK
ADNOC REFINING RESEARCH CENTRE DIVISION

OUTLINE
• Introduction
• Hydrocracking Process Description
• Data Required
• Model Development and output
• Utilization of Model
• PIMS
• Response Surface Methodology
• Summary
OPTIMIZATIONOFHCKPORCESSUSINGRSM

INTRODUCTION TO
HYDROCRACKING
Hydrocracking is a flexible catalytic conversion process in presence of
hydrogen that upgrades heavy feed stocks by:
 Cracking large molecules to small ones to achieve the desired
boiling range products.
 Removing impurities like Metals, S, N etc.
 Saturation of olefins
Hydrocracking feeds can range from heavy vacuum gas oils and
Coker gas oils to atmospheric gas oils.

HYDROCRACKER IN REFINERY
CDU
OVHD Gases and Naphtha
Kerosene
Diesel
Atm Residue
VDU
LVGO
HVGO
Vacuum Residue
HCR
Fract.
Kerosene
Diesel
Products Unconverted Oil
H2
Crude Oil

HYDROCRACKER MODE OF OPERATION
Atm.
Residue
HVGO UCO
VDU HCK BO
UNIT
Base Oil
R E C Y C L E M O D E
O N C E T H R O U G H M O D E
VDU HCK
HVGO
UCO
Products
Atm. Residue

TYPICAL HYDROCRACKER UNIT
R1 R2
Feed
Heater
Product
Condenser
HPS LPS
Debutanizer
Debutanizer
Heater
Debut. gas
Fractionator
Recycle gas comp.
H2 Make up
Feed
Pre-Heater
HVGO
Feed
GO
Pump Around
H Kero
Pump Around
Net H Kero
Fr. Btm
Lt Kero
Pump Around
GO
Naphtha
Lt Kero
Hy Kero
Fr. Ovhd gas
Naphtha
Splitter
Hy Naph.
UCO
Quench H2
Naph. Split. gas
Lt Naphtha
To FG

KEY FUNCTIONS
Hydrotreating
 De-metallization
 Saturations
 De-sulfurization
 De-nitrogenation
Hydrocracking
 Hetero Aromatics
 Multi-ring Aromatics
 Mono Aromatics
 Paraffins
HCK Aims at decreasing the average molecular weight of the
feed stock to produce lighter cuts

KEY PARAMETERS
Hydrocrackers are
designed to run at a
variety of conditions
depending on type of
feed, desired cycle
length, expected
product slate, typical
range of different
parameters:
Sr. No. Process Parameter Range
1
Liquid Hourly Space Velocity
(LHSV) :
0.5 to 2.0 hr-1
2 Gas to Oil Ratio: 850 to 1,700Nm3/m3
3 Hydrogen partial Pressure: 103 to 138 bars
4 SOR temperatures : 357 to 385oC

ADVANTAGES OF HYDROCRACKING
REAL MONEY MAKER
 Convert low value feedstock to high value saleable products
 Hydrocracker gives nearly 15% volume increase in products
 Products are clean and do not require any further treatment
 Hydrocrackers normally give 97-99% conversion with recycle of
Unconverted Oil (UCO)
Alternately, UCO is an excellent raw material for production of base oil.

CATALYSTS
• HDM
• HDT (hydrotreating)
 NiMo / Alumina
 CoMo / Alumina
• HCK (hydrocracking) Bi‐functional Catalyst
 Acidic Function (Cracking): Amorphous Silica Alumina
catalyst + Zeolite based catalyst for higher cracking
activity.
 Hydrogenation Function: Metals or sulfided metals
Quench
HCK
HDM
HDT

TYPICAL REACTIONS
Cracking Reactions
H2
H2 H2

CHOICE OF THE CATALYST
Catalysts are selected based on specific properties
 Activity
Ability to increase the rate of the reactions involved
 Selectivity
Ability to favor desirable reactions rather than others
 Stability
Degradation of the activity and Selectivity over time by:
 Formation of coke, adsorption of poisons (metals)
 Loss of volatile active agents
 Change in crystalline structure

NEED OF SIMULATION MODEL
Simulation Models Assist us in:
• Optimization studies
• Data generation for production planning purposes.
• Debottlenecking studies.
• Revamp Studies
• Capacity enhancement studies

DATA REQUIRED FOR MODEL
Feed and Product properties
Operating conditions
• Flow Rates and yields
• LHSV
• WABT
Catalyst loading details
Reactor temperature and pressure profiles

PETROSIM HCR MODULE

DATA INPUT SIMULATION ENVIRONMENT

DATA INPUT EXCEL INTERFACE

MODEL CALIBRATION

MODEL
CALIBRATION
Tuning Factors

VALIDATED HCR MODEL

MODEL VALIDATION

UTILIZATION OF MODEL

DATA GENERATION FOR DOE
WABT AGE T90
o
C o
C/(m3/kg) o
C
1 401 0.514 525
2 405.204 0.514 525
3 403.5 0.505 530
4 403.5 0.523 530
5 401 0.514 516.591
6 401 0.514 525
7 396.796 0.514 525
8 398.5 0.505 530
9 401 0.514 525
10 401 0.498864 525
11 398.5 0.523 520
12 398.5 0.523 530
13 403.5 0.505 520
14 401 0.514 525
15 401 0.529136 525
16 401 0.514 525
17 401 0.514 533.409
18 403.5 0.523 520
19 401 0.514 525
20 398.5 0.505 520
Run No.
VI Conv.
H2
Consumption
- % %
150.822 78.23 230.376
150.822 78.23 230.376
151.133 78.27 231.915
151.133 78.27 231.915
148.535 74.54 223.550
148.749 74.52 224.692
148.535 74.54 223.550
148.535 74.54 223.550
148.535 74.54 223.550
148.535 74.54 223.550
148.535 74.54 223.550
148.535 74.54 223.550
148.338 74.54 222.498
148.535 74.54 223.550
153.199 80.48 238.096
144.912 68.55 212.089
146.273 71.08 216.166
146.492 71.02 217.446
146.273 71.08 216.166
146.492 71.02 217.446

DATA GENERATION FOR DOE

RESPONSE SURFACE DOE
Design-Expert® Software
Factor Coding: Actual
VI (-)
Actual Factors
A: WABT = 401
B: AGE = 0.514
C: T 90 = 520
-1.000 -0.500 0.000 0.500 1.000 1.500 2.000
146
147
148
149
150
151
152
A A
B BC
C
Perturbation
X: Deviation from Reference Point (Coded Units)
Y: VI (-)
VI
Color points by value of
VI:
151.133
146.273
X1: Actual
X2: Predicted
Predicted vs. Actual
146
147
148
149
150
151
152
146 147 148 149 150 151 152

RESPONSE SURFACE
R-Squared 0.9978
Adju. R-Squared 0.9934
Predicted R-Squared 0.9493
VI (-)
Design points above predicted value
151.133
146.273
X1 = A: WABT
X2 = B: AGE
Actual Factor
C: T 90 = 520
0.505
0.508
0.511
0.514
0.517
0.52
0.523
398.5
399.5
400.5
401.5
402.5
403.5
146
147
148
149
150
151
152
VI(-)
A: WABT (0 C)
B: AGE (oC /(m3/kg))
VI =
-5.47375E+006
+26128.77846 * WABT
+9.18909E+005 * AGE
+10445.67108 * T 90
-2290.81894 * WABT * AGE
-49.84343 * WABT * T 90
-1733.78948 * AGE * T 90
-31.14632 * WABT2
-0.016462 * T 902
+4.32230 * WABT * AGE * T 90
+0.059446 * WABT2* T 90

HCK OPTIMIZATION
Based on multiple responses using simultaneous optimization technique by
Derringer& Suich(1).
Desirability function procedure:
1- convert each response yi into individual desirability di
2- 0 ≪ 𝑑𝑖 ≪ 1 if di at its goal or target then di = 1
if di outside its goal or target then di = 0
3- Then the design variables are chosen to maximize the overall desirability
𝐷 = 𝑑1 ∙ 𝑑2 ∙∙∙ 𝑑 𝑚
1
𝑚
(1): Derringer , G. and Suich, R. (1980)” simultaneous optimization of several responses variables”, Journal of quality technology, 12, 214-219.

HCK OPTIMIZATION CONTD
Solution
WABT AGE T 90 VI Conv.
H2
consump-
tion
Desirability
400.000 0.515 530.0 147.4 72.65 219.921 0.856
fixed fixed varies varies varies minimize
Response VI
VI (-)
151.133
146.273
X1 = C: T 90
X2 = A: WABT
Actual Factor
B: AGE = 0.516432
398.5
399.5
400.5
401.5
402.5
403.5
520
522
524
526
528
530
146
147
148
149
150
151
152
VI(-)
C: T 90 (o C)
A: WABT (0 C)
148.658
150.873
147.857
150.873
148.658
147.857

SUMMARY
 Hydrocrackers are among key the money-makers in a
refinery.
 Models can be used for parametric studies to predict unit
performance and decision making for any change in feed
quality, throughput or desired conversion.
 Models can be used to provide inputs for planning in
generation of LP vectors and data generation.
 Using RSM, a useful model can enable process engineers
to optimize the HCK operation for base oil production.

Europe User Conference: ADNOC optimization of hydrocracking process as a function of operating conditions

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Europe User Conference: ADNOC optimization of hydrocracking process as a function of operating conditions