Naphtha-Mogas pool optimization
István KÁTAI, István RABI, dr. László SZIRMAI, Zsolt Németh, Szabolcs SIMON MOL Group
2nd Annual
European Petrochemicals Conference Düsseldorf
March, 2015
V6: Feb 27. 2015
► 2
►MOL Downstream – Petrochemicals synergy
►Utilisation options for naphtha
►How to calculate Steam Cracking economics as a function of naphtha
quality ?
►Steps of a naphtha pool linear programming model revision
►Conclusions
Agenda
► 3
Refinery Petchem unit
UP
ST
RE
AM
D
OW
NS
TR
EA
M
GA
S M
IDS
TR
EA
M
REGION EBITDA 2013 KEY DATA
► Operation in 40 countries
► 38 million barrels of oil-
equivalent hydrocarbon
produced
► 8 bn USD market capitalisation
► ~30 000 employees
► 750 000 retail transactions daily
► 24,1 bn USD revenue
► 2,3 bn USD EBIDTA
► 576 MMboe SPE 2P
reserves (1)
► 960 MMboe Recoverable
Resource Potential (2)
► 96 mboepd production (3)
► Production in 8, exploration
in 13 countries (2)
MOL Group in numbers
► 4 refineries, 417 thbpd
► 19 Mtpa sales
► 1 900+ (4) service stations
► 2 petrochemical plants
► Gas Transmission:
5.560 km pipeline in
Hungary
(1) End of 2013 SPE-2P, 2P reserves of North Sea assets not included yet, to be booked in 2014
(2) Already including the North Sea assets (UK) of Wintershall which deal was closed in Q1 2014
(3) Excluding ZMB and S7 fields, divested in August 2013; & excluding 49% of Baitex LLC, deal closed in Q1
2014
(4) Including the 208 service stations, acquired from ENI Group; deal has not closed yet
MOL GROUP UPSTREAM-DRIVEN, INTEGRATED COMPANY
► 4
Bratislava
Danub
e Sisak
Rijeka
KEY STRENGTH
Complex, diesel geared refineries
Integrated petrochemical units to handle surplus gasoline/naphtha pool
Strong land-locked market presence – 20% motor fuel market share in the CEE; market leader in 4 countries
Region-wide Logistics, Wholesale and Retail network serve the market - above 55% end-user share
Refinery Mtpa thbpd NCI
MOL Group
20.9 417 10.0
Danube 8.1 161 10.6
Bratislava 6.1 122 11.5
Rijeka 4.5 90 9.1
Sisak 2.2 44 6.1
REFINERY YIELD 2014E
over
80% white prd.
19.4 Mt refined product & petrochemical sales
Retail: 1.900+ FS (1) over 3.5 Mtpa sales
Petchem: 1.3 Mt ext. sales
2013 FIGURES REFINERY CAPACITY & COMPLEXITY
3% 9%
20%
52%
4% 3%
3% 6% LPG
Naphtha
Motor Gasoline
Middle Distillates
Fuel Oil
Bitumen
Other
Other chemical prds.
(1) Including the recently acquired CZE, SVK, ROM Agip network
TWO LARGEST ASSETS AMONG THE BEST IN EUROPE Integrated operation in adjacent markets
► 5
MOL IS AMONG THE TOP TEN POLYOLEFIN PRODUCERS IN EUROPE
LDPE capacities in Europe (2012)
145
150
160
196
220
245
310
420
500
505
645
835
845
870
873
DIOKI dd.
TDEASA
Total Petrochemicals
KazanorgsintezJSC
Sibur ZAO
10. MOL
Petkim
Repsol
INEOS
Dow Chemicals
Borealis
Versalis
ExxonMobil
LyondellBasell
SABIC Europe
HDPE capacities in Europe (2012)
100
120
140
220
230
355
375
400
420
510
535
615
920
1100
1630
Rompetrol
Gazprom
Dow Chemicals
Versalis
Nizhnekamsknefekhim
PKN Orlen (Unipetrol)
Repsol
Lukoil
7. MOL
Kazanorgsintez JSC
Borealis
SABIC Europe
Total Petrochemicals
INEOS
LyondellBasell
PP capacities in Europe (2012)
180
180
200
230
250
260
290
490
535
545
905
1150
1270
1885
2855
DOMO Int.
Hellenic Petroleum
Polychim
Sibur ZAO
ExxonMobil
Lukoil
PKN Orlen (Unipetrol)
Repsol
7. MOL
Braskem Europe
INEOS
SABIC Europe
Total Petrochemicals
Borealis
LyondellBasell
Butadiene capacities in Europe (2015 estimated)
124130141
176178
207210
230260270
300300302
320415
TITAN GroupMOL
PKN OrlenTotal Petrochemicals
NizhnekamskneftekhimRepsol
ENI (Versalis)SABICBASF
DowSIBUREvonik
OMVLyondellBasell
INEOS
► 6
REFINING – PETROCHEMICALS INTEGRATION AT MOL
• Optimization of product placement between:
– 4 Refineries
– 2 Petchem sites
REF
ININ
G
• NAPHTHA • LPG
• GASOIL • PROPYLENE
Petrochemicals processing chain
• PYGAS: AROMATICS • HYDROGEN
• I-BUTENE
PLASTIC PRODUCERS
STEAM CRACKERS
OLEFINS POLYOLEFINS
ETHYLENE SALES
POLYMER UNITS
• LDPE • HDPE • Polypropylene
BRATISLAVA SITE - 220 ktpa LDPE - 250 ktpa PP
TVK SITE
- 420 ktpa HDPE - 65 ktpa LDPE - 140 ktpa ethylene - 280 ktpa
polypropylene
► 7
Naphtha-Mogas pool optimization – Utilisation options for naphtha
Naphtha components
Steam Cracking prefers • paraffinic • low boiling point naphtha Quality measured by: monomer yields
Reforming prefers • aromatic • naphthenic naphtha Quality measured by: Reformate RON, MON = f(N+2A)
Some naphtha may go to Diesel / Kero pool: • paraffinic • higher boiling point naphtha
components
► 8
Reforming economics is determined by feed N+2A
Reforming prefers aromatic and naphthenic naphtha
Higher reformer feed N+2A ↓
Higher reformate RON and MON ↓
Better reformer economics
Aromatics: B,T, X, …
Naphthenics: Cyclo-paraffins
► 9
How to calculate SC economics as a function of naphtha quality ?
Steam Cracking prefers • paraffinic (iso and n-paraffins) • low boiling point naphtha
Normal paraffins Isoparaffins
Steam Cracker margin is determined by the monomer yields.
Which naphtha parameter correlates best with Steam Cracker yields?
Candidates:
- Density
- Boiling points - IBP - T50 - FBP (T95)
- Group composition - Isoparaffin - n-paraffin - aromatics - naphthenic
- Combinations of the above parameters
► 10
Steps of a naphtha pool linear programming model revision
Concept Data
acquisition
Steam Cracker
modelling
Correlation analysis
Regression analysis
Prepare PIMS data
Start up and use new model
Target • Improved Naphtha pool optimisation based on Steam Cracker economics calculated
from expected product yields • Consistent handling of own produced and external naphtha components Implementation steps
► 11
Step 1: Concept
An internal MOL “Lean” study statement:
Less aromatic naphtha Higher Petchem profitability
Source: MOL internal Lean study: L. Szirmai, M. Bubálik, G. Gárdonyi, Sz. Simon
► 12
Step 2: Data acquisition
Naphtha stream lists, sampling points
Laboratory analyses Data preparation
Danube Refinery naphtha streams • DCDU3 Light NAPHTHA • DCDU2 Light NAPHTHA • DCDU1 light Naphtha • …
Slovnaft naphtha streams • Heavy naphtha from BADU5 • Heavy naphtha from BCDU6 • Heavy naphtha from SPL • C5/C6 fraction from AD 5 • …
Import naphthas • Source A • Source B • …
Naphtha tank data • Refinery tanks • Petchem site tanks
• Density
• Boiling points • IBP • T50 • FBP (T95)
• Group composition • Isoparaffin • n-paraffin • aromatics • naphthenic
• Yearly averages for main streams in naphtha pool
• Define “typical” naphtha compositions for each petchem site
► 13
SPYRO yields for • each steam cracker and • each naphtha component
Expected once-through product yields for • each steam cracker and • each naphtha component
SC-1 yields • Naphtha stream A • Naphtha stream B
SC-2 yields • Naphtha stream A • Naphtha stream B …
Grouping of individual components to real components
Apply empirical corrections, simplifications
Step 3: Steam Cracker simulator (SPYRO) calculations
Product yieldsNaphtha
stream A
Naphtha
stream B
Naphtha
stream C
Hydrogen 0,0021 0,0021 0,0021
Methane 0,1728 0,1562 0,1461
Ethylene 0,2868 0,2670 0,2436
Propylene 0,1661 0,1507 0,1341
BT 0,0533 0,0775 0,0912
C8 0,0036 0,0065 0,0132
C9+ 0,0107 0,0222 0,0343
Quench 0,0113 0,0294 0,0577
Repyrolysis ethane 0,0351 0,0326 0,0301
Repyrolysis propane 0,0161 0,0148 0,0135
Repyrolysis C4 0,1498 0,1459 0,1294
Repyrolysis C5 0,0686 0,0583 0,0534
Yields of physical products
Compound yieldsNaphtha
stream A
Naphtha
stream B
Naphtha
stream C
wt% (dry) wt% (dry) wt% (dry)
Hydrogen 0,9443 0,93947 0,93452
Methane 16,438 14,854 13,886
Acetylene 0,46549 0,44746 0,41425
Ethylene 30,582 28,464 25,961
Ethane 3,5686 3,3087 3,0521
Methyl-Acetylene 0,51535 0,46407 0,41622
Propadiene 0,34563 0,31213 0,28064
Propylene 18,152 16,468 14,653
Propane 0,43352 0,40291 0,37228
Vinyl-Acetylene 0,054135 0,059282 0,053548
Butadiene 4,89 5,5696 5,4787
Butene (sum) 6,2547 5,5615 4,8236
Butane (sum) 1,0767 0,7642 0,24466
Total C5-C9's 14,979 19,517 24,482
Total C10+ 1,2182 2,7881 4,8699
Carbon Oxide 0,078102 0,07473 0,072642
Carbon Dioxide 0,004716 0,00434 0,004181
…
Yields of cca. 150 individual chemical compounds
► 14
Ar, w% N, w% iP, w% nP, w% i+n, w% N+A, w% N+2A, w% SpGr @ 15°C,
g/cm3
Ethylene
yield, w%
Propylene
yield, w%
Butadiene
yield, w%
Benzene
yield,
w%
Toluene
yield,
w%
Styrene
yield,
w%
B+T+S
yield,
w%
Olefins
yield,
w%
E/P ratio,
(-)
Ar, w% 1
N, w% 0,4203 1
iP, w% -0,5599 -0,4361 1
nP, w% -0,5750 -0,7348 -0,0461 1
i+n, w% -0,8153 -0,8681 0,5846 0,7835 1
N+A, w% 0,8153 0,8681 -0,5846 -0,7835 -1 1
N+2A, w% 0,9203 0,7418 -0,6018 -0,7418 -0,9768 0,9768 1
SpGr @ 15°C, g/cm3 0,6720 0,8729 -0,4240 -0,8138 -0,9247 0,9247 0,8730 1
Ethylene yield, w% -0,8900 -0,6875 0,3774 0,8506 0,9256 -0,9256 -0,9542 -0,8053 1
Propylene yield, w% -0,9492 -0,6739 0,6031 0,7069 0,9493 -0,9493 -0,9921 -0,8458 0,9415 1
Butadiene yield, w% -0,5173 0,3616 0,5579 -0,3630 0,0522 -0,0522 -0,2264 0,1531 0,1374 0,2869 1
Benzene yield, w% 0,6077 0,9030 -0,3231 -0,8714 -0,9087 0,9087 0,8384 0,8757 -0,8392 -0,7983 0,2929 1,0000
Toluene yield, w% 0,8339 0,5220 -0,4211 -0,6494 -0,7893 0,7893 0,8414 0,5877 -0,8732 -0,8606 -0,2331 0,7147 1
Styrene yield, w% 0,9529 0,6077 -0,5713 -0,6818 -0,9091 0,9091 0,9663 0,8407 -0,9115 -0,9723 -0,3394 0,7305 0,7422 1
B+T+S yield, w% 0,8165 0,7543 -0,4284 -0,8145 -0,9280 0,9280 0,9287 0,7988 -0,9408 -0,9209 -0,0209 0,9135 0,9345 0,8291 1
Olefins yield, w% -0,9694 -0,6023 0,5464 0,7077 0,9146 -0,9146 -0,9761 -0,7718 0,9673 0,9818 0,3544 -0,7493 -0,8784 -0,9622 -0,9067 1
E/P ratio, (-) 0,8049 0,3416 -0,7839 -0,2102 -0,6583 0,6583 0,7422 0,6082 -0,5491 -0,7934 -0,5565 0,4109 0,5681 0,7991 0,5692 -0,7200 1
Abs values Ar, w% N, w% iP, w% nP, w% i+n, w% N+A, w% N+2A, w% SpGr @ 15°C, g/cm3
Ethylene yield, w% 0,8900 0,6875 0,3774 0,8506 0,9256 0,9256 0,9542 0,8053
Propylene yield, w% 0,9492 0,6739 0,6031 0,7069 0,9493 0,9493 0,9921 0,8458
Benzene yield, w% 0,6077 0,9030 0,3231 0,8714 0,9087 0,9087 0,8384 0,8757
Toluene yield, w% 0,8339 0,5220 0,4211 0,6494 0,7893 0,7893 0,8414 0,5877
St 0,9529 0,6077 0,5713 0,6818 0,9091 0,9091 0,9663 0,8407
E/P 0,8049 0,3416 0,7839 0,2102 0,6583 0,6583 0,7422 0,6082
Averages Ar, w% N, w% iP, w% nP, w% i+n, w% N+A, w% N+2A, w% SpGr @ 15°C, g/cm3
Avg Et,Pr 0,9196 0,6807 0,4902 0,7788 0,9375 0,9375 0,9732 0,8256
Avg BTS 0,7982 0,6776 0,4385 0,7342 0,8690 0,8690 0,8820 0,7680
Avg EPBTS 0,8468 0,6788 0,4592 0,7520 0,8964 0,8964 0,9185 0,7910
Step 4: Correlation analysis – R values
N+2A selected as best base for yield estimation
► 15
Step 5: Regression analysis Definition of product yields as the function of N+2A parameter (linear regression on SPYRO results): Good correlation for most products
► 16
Set Steam Cracker once through yields
Set recirculation yields
Start-up and use new model
• Transformation of the regression equations into the “Base vector” – “Shift vector” format, used by PIMS
• Normalisation
• Consistency check
• Problem: Using real “once through” yields would increase PIMS calculation time due to infinite recycles
• Solution: For each recycle streams the “equal and heavier” recycle stream yields are set to 0 (“half zero approximation”).
• Other yields adjusted accordingly (normalized)
• Integrate new model into planning framework
• Debugging, model validation
• Adjust related work instructions
• Assessment of results
Step 6: Prepare PIMS data Step 7: Start up and use new model
Base vector Shift vector
AVG N+2A +1 Δ in N+2A C2 C3 C4 C5
Hydrogen 1-30 % ± 0.1 % 1-30 % 1-30 % 1-30 % 1-30 %
Methane 1-30 % ± 0.1 % 1-30 % 1-30 % 1-30 % 1-30 %
Ethylene 1-30 % ± 0.1 % 1-30 % 1-30 % 1-30 % 1-30 %
Propylene 1-30 % ± 0.1 % 1-30 % 1-30 % 1-30 % 1-30 %
BT 1-30 % ± 0.1 % 1-30 % 1-30 % 1-30 % 1-30 %
C8 1-30 % ± 0.1 % 1-30 % 1-30 % 1-30 % 1-30 %
C9+ 1-30 % ± 0.1 % 1-30 % 1-30 % 1-30 % 1-30 %
Quench oil 1-30 % ± 0.1 % 1-30 % 1-30 % 1-30 % 1-30 %
Rep. C2 1-30 % ± 0.1 % 1-30 % 1-30 % 1-30 % 1-30 %
Rep. C3 1-30 % ± 0.1 % 1-30 % 1-30 % 1-30 % 1-30 %
Rep. C4 1-30 % ± 0.1 % 1-30 % 1-30 % 1-30 % 1-30 %
Rep. C5 1-30 % ± 0.1 % 1-30 % 1-30 % 1-30 % 1-30 %
Recycle streams Base vector Shift vector
AVG N+2A +1 Δ in N+2A C2 C3 C4 C5
Hydrogen 1-30 % ± 0.1 % 1-30 % 1-30 % 1-30 % 1-30 %
Methane 1-30 % ± 0.1 % 1-30 % 1-30 % 1-30 % 1-30 %
Ethylene 1-30 % ± 0.1 % 1-30 % 1-30 % 1-30 % 1-30 %
Propylene 1-30 % ± 0.1 % 1-30 % 1-30 % 1-30 % 1-30 %
BT 1-30 % ± 0.1 % 1-30 % 1-30 % 1-30 % 1-30 %
C8 1-30 % ± 0.1 % 1-30 % 1-30 % 1-30 % 1-30 %
C9+ 1-30 % ± 0.1 % 1-30 % 1-30 % 1-30 % 1-30 %
Quench oil 1-30 % ± 0.1 % 1-30 % 1-30 % 1-30 % 1-30 %
Rep. C2 1-30 % ± 0.1 % 0 1-30 % 1-30 % 1-30 %
Rep. C3 1-30 % ± 0.1 % 0 0 1-30 % 1-30 %
Rep. C4 1-30 % ± 0.1 % 0 0 0 1-30 %
Rep. C5 1-30 % ± 0.1 % 0 0 0 0
Recycle streams
► 17
Team work – integration of different competencies
Concept Data acquisition
Steam Cracker modelling
Correlation analysis
Regression analysis
Prepare PIMS data
Start up and use new model
Lean project ⃝ ⃝
Quality Control ⃝ ⃝ ⃝
Engineering + Development ⃝ ⃝ ⃝ ⃝ ⃝ ⃝
Supply Chain Management ⃝ ⃝ ⃝ ⃝ ⃝ ⃝
► 18
Conclusions
Results
Naphtha composition
Decreasing N+2A content in petchem naphtha pool. Heavy aromatic swing cuts eliminated from naphtha pool Extendable to naphtha streams from alternative crudes
Economics Improved monomer/naphtha ratio Better profitability without CAPEX Firm base for naphtha make or buy decisions
N+2A is proposed for the characterization of Naphtha as Steam Cracking feedstock
the same way as it is used to qualify naphtha as a Reforming feed
N+2A
correlates well with most SC product yields
is an additive parameter for refinery streams
can easily be calculated automatically for each naphtha stream in PIMS
is traditionally used to qualify Reformer feed, thus
a coherent optimisation can be established for Reformers and Steam Crackers
A simplified method is suggested to calculate Steam Cracker recirculation streams in LP models
no infinite recirculation loops
less than 2% relative error in product yields
► 19
Q&A
Thank you!
István Kátai | Senior Expert MOL GROUP – Downstream Development [email protected] molgroup.hu
Acknowledgements
SPYRO © is a trademark of Pyrotec.
PIMS © is a trademark of AspenTech
