quantitative methods for strategic and investment...

Goal: develop high-level decision-making optimization to predict structural modifications in refining and logistics assets using more rigorous formulations

PETROBRAS Current Tool for Strategic Planning (PLANINV) – LP

No Process Design Synthesis Quantitative Methods

PLANINV Process Design Opt. (MILP)

1

Delayed Coker

Terminal/Pipeline

Atmospheric Distillation

EWO Meeting – March 2014

What, Where, When to Invest?

Only optimize streams transfers (oil and fuels import/export, market supply)

+ NLP Processing

Blending

Quantitative Methods for Strategic and Investment Planning in the Oil-Refining Industry

Brenno C. Menezes, Ignacio E. Grossmann, Lincoln F. Moro and Jeffrey D. Kelly

𝐲𝐲𝐲𝐲=expansion of an existent unit

𝐲𝐲𝐲𝐲𝐫𝐫,𝐮𝐮,𝐧𝐧,𝐭𝐭 𝐐𝐐𝐐𝐐𝐮𝐮𝐋𝐋 ≤ 𝐐𝐐𝐐𝐐𝐫𝐫,𝐮𝐮,𝐧𝐧,𝐭𝐭 ≤ 𝐲𝐲𝐲𝐲𝐫𝐫,𝐮𝐮,𝐧𝐧,𝐭𝐭 𝐐𝐐𝐐𝐐𝐮𝐮𝐔𝐔

𝐐𝐐𝐐𝐐𝐫𝐫,𝐮𝐮,𝐧𝐧,𝐭𝐭 = 𝐐𝐐𝐄𝐄𝐐𝐐𝐄𝐄𝐄𝐄𝐫𝐫,𝐮𝐮,𝐧𝐧 + 𝐐𝐐𝐐𝐐𝐫𝐫,𝐮𝐮,𝐧𝐧,𝐭𝐭−𝟏𝟏 + 𝐐𝐐𝐐𝐐𝐫𝐫,𝐮𝐮,𝐧𝐧,𝐭𝐭−𝟏𝟏 𝐲𝐲𝐞𝐞𝐞𝐞𝐞𝐞𝐧𝐧𝐞𝐞𝐞𝐞𝐞𝐞𝐧𝐧: 𝐮𝐮,𝐧𝐧 𝐲𝐲𝐞𝐞𝐞𝐞

𝐐𝐐𝐐𝐐𝐫𝐫,𝐮𝐮,𝐧𝐧,𝐭𝐭 ≤ 𝐐𝐐𝐐𝐐𝐫𝐫,𝐮𝐮,𝐧𝐧,𝐭𝐭 𝐮𝐮,𝐧𝐧 𝐲𝐲𝐞𝐞𝐞𝐞

QF= operational flow Q𝐐𝐐= expanded capacity QC= total capacity

Capital Investment Planning Formulation

(R,U,N,T) R=Refinery U=Unit type N=Number of an unit type T=Time

Crude dieting

ISW

Processing

Blending

ON

QC=QCt-1+QNEW MILP

QF≤QC NLP

INVREF

OPREF

𝐐𝐐𝐐𝐐𝐫𝐫,𝐮𝐮,𝐧𝐧,𝐭𝐭 = 𝐐𝐐𝐐𝐐𝐫𝐫,𝐮𝐮,𝐧𝐧,𝐭𝐭−𝟏𝟏 + 𝐐𝐐𝐐𝐐𝐫𝐫,𝐮𝐮,𝐧𝐧,𝐭𝐭−𝟏𝟏 𝐞𝐞𝐧𝐧𝐞𝐞𝐭𝐭𝐞𝐞𝐢𝐢𝐢𝐢𝐞𝐞𝐭𝐭𝐞𝐞𝐞𝐞𝐧𝐧: 𝐮𝐮,𝐧𝐧 𝐞𝐞𝐧𝐧𝐞𝐞

𝐲𝐲𝐞𝐞𝐫𝐫,𝐮𝐮,𝐧𝐧,𝐭𝐭 𝐐𝐐𝐐𝐐𝐮𝐮𝐋𝐋 ≤ 𝐐𝐐𝐐𝐐𝐫𝐫,𝐮𝐮,𝐧𝐧,𝐭𝐭 ≤ 𝐲𝐲𝐞𝐞𝐫𝐫,𝐮𝐮,𝐧𝐧,𝐭𝐭 𝐐𝐐𝐐𝐐𝐮𝐮𝐔𝐔

Maximize: NPV = DemandSales - SupplyCosts - OperatingCosts - InvestmentCosts

Subject to:

Where:

Sahinidis et. al., CACE, 13, (1989) and Sahinidis & Grossmann, CACE, 15, (1991).

T1 T2

Take an investment decision (binary)

Count on the additional production

Project execution Improved Formulation: - Installations;

year

- Project Execution; - NLP Operational Layer.

Q𝐐𝐐= installed capacity

𝐲𝐲i= installation of a new unit

⋀ 𝐮𝐮,𝐧𝐧 𝐞𝐞𝐧𝐧𝐞𝐞

3 EWO Meeting – March 2014

Options to Formulate the Problem

(1) Menezes, et. al. (2014). Nonlinear Production Planning of Oil-Refinery Units for the Future Fuel Market in Brazil: Process Design Scenario-Based Model. Ind. Eng. Chem. Res. DOI: 10.1021/ie025775. Published online: Feb, 17, 2014.

1st- NLP Operational Problem Z=profit (m3/d) and QFu=unit throughputs to control capacity expansion

2nd- MINLP Investments Problem (NLP Operational Problem Embedded) Z=NPV ($) and QEu,t and QCu,t to control capacity expansion

3rd- MILP Investments Problem + NLP Operational Problem (Phenomenological Decomposition Heuristics) Z=NPV ($) and QEu,t, QIu,t and QCu,t to control capacity expansion and installation

Crude Diet

Processing

Blending - Crude - Cuts/Final Cuts - Final Products

NLP

Investment

Operational

MILP

QFu,t ≤ QCu,t link constraint

Full Space Problem MINLP

Aggregated Multi-Site Approach

Multi-Site Approach

4


Refining Process Capacities (k m3/d) 2013 2016 2020Crude Distillation Unit CDU 310 372 536Vacuum Distillation Unit VDU 140 153 260Residue Fluid Catalytic Cracking RFCC 22 22 22Fluid Catalytic Cracking FCC 76 76 76Hydrocracking HCC 10 74Propane Deasphalting PDA 10 10 10Delayed Coker DC 42 74 124Light Cracked Naphtha Hydrotreater LCNHT 54 54 54Coker Light Naphtha Hydrotreater CLNHT 22 34 60Stabilizer ST 22 34 60Kerosene Hydrotreater KHT 15 15 15Diesel Hydrotreater (medium severity) D1HT 60 60 60Diesel Hydrotreater (high severity) D2HT 30 68 135Reformer REF 7 10 10EWO Meeting – March 2014

Conceptual Projects under reevaluation

5

Perform linear regression to convert the nonlinear Power Law to Fixed-Charge Relation: Investment Cost = a*Capacity+b*Setup


Unit (u) αu βumi US$/1000m3 mi US$

CDU 11.7 227.1VDU 15.1 146.4FCC 57.5 241.2HCC 150.6 747.6RFCC 70.2 588.8DC 108.9 456.5KHT 21.8 115.9DHT 29.6 162.6LCNHT 14.0 69.3CLNHT 14.0 69.3ST 11.5 193.9REF 46.0 80.0

FK

FLD

ATR

CDU C1C2C3C4

SW2

VR

VDU

N

K

LD

HD

LCO

DO

HTD

HTK

FCC

D1HT

KHT

CLNCHN

CLGO

CHGO

CMGO

D2HT

DCREF

LCNHT

CLNHT

PQN

C1C2C3C4

HCNLCN

C1C2C3C4

FN

FHD

GLN(GLNC)

MSD

HSD

JET

LSD

HTCLN

HTLCN

FO

REFOR

C1C2 FG

LPGC3C4

LVGO

HVGO

00

ASPR

DAO

PDA

RFCC

SW3

SW1

C1C2C3C4

HCCO

Crude

HCCDHCCK

HCCN

HCC

USD

COKE

H2

COKE

LSDimp

GLNimp

(GLNA)

ETH

For RNEST

JETimp

LPGimp

ST

GOST

LNST

HNST

max NPV = �� 1− tr Ft0�1

(1 + ir)t0 ��(prp,t0−Refct0)Demp,tpt0t

−�prcr,t0υcr,tQFCDU,tcr

−� primp,t0QFimp,timp

−�ΥHT,tQFHT,tHT

�

−1

(1 + ir)tit�(αu,titQEu,t + βu,tityu,t)u t<Tend

�

12 discrete variables; 1127 continuous variables 1019 equations 4463 nonzero elements; 2552 nonlinear elements


6


Crude Diet

Processing


NLP Operational

QFu,t ≤ M (=1000)

Crude Diet

Processing


NLP

Investment

Operational

MILP

QFu,t ≤ QCu,t link constraint

Full Space Problem MINLP

Expansions, QE (k m3/d) 2016 Unit GLNC GLNCETH GLNC GLNCETH

372 CDU 544 508 400 400153 VDU 202 223 180 18222 RFCC 82 25 15 1576 FCC 52 52 72 6110 HCC 97 114 45 4574 DC 128 126 114 10515 KHT 18 15 15 1568 D2HT 122 116 65 6554 LCNHT 72 42 47 4034 CLNHT 72 72 54 4034 ST 72 72 54 4012 REF 33 13 24 18

Profit (mi US$/d) 38.992 33.376 21.432 13.420CPU (s) 0.733 0.795 0.468 0.546

Expansions, QE (k m3/d) 2016 Unit GLNC GLNCETH GLNC GLNCETH

372 CDU 592 554 492 468153 VDU 204 267 206 21822 RFCC 107 22 49 2276 FCC 76 91 76 5410 HCC 53 76 54 9474 DC 102 117 105 10015 KHT 26 19 18 1068 D2HT 130 122 111 10054 LCNHT 99 61 67 4234 CLNHT 48 55 49 4734 ST 48 55 49 4712 REF 18 26 21 23

NPV (bi US$) 8.387 5.2465 11.624 6.451Investment (bi US$) 25.000 24.681 19.170 21.312Profit (mi US$/d) 27.123 23.100 22.747 20.701CPU (s) 1.06 1.746 1.077 0.734

2009-2012 trends 4.2% p.a.

2020

2009-2012 trends 4.2% p.a.

2020

CONOPT

DICOPT (NLP: CONOPT)


Multi-Site Approach – PDH (MILP+NLP)

PDH = Phenomenological Decomposition Heuristics = Quantity + Quality problems Decomposed

MILP NLP Quantity Problem: Logic + Quantity variables Quality Problem: Quantity + Quality Variables

REVAP CDU.1 VDU.1 FCC.1 PDA.1 DC.1 LCNHT.1 CLNHT.1 KHT.(1,2) DHT.(1,2) REF.1

12 units

REPLAN CDU.(1,2) VDU.(1,2) FCC.(1,2) DC.(1,2) LCNHT.(1,2) CLNHT.(1,2) DHT.(1,2) REF.1

15 units

RPBC CDU.(1,2,3) VDU.(1,2) FCC.1 DC.(1,2) LCNHT.(1,2) CLNHT.1 DHT.(1,2) REF.1 ALK.1

15 units

CDU.2.2 VDU.1.2 FCC.1.1 DHT.2.1 LCNHT.1.1 CLNHT.1.1 CDU.3.1 VDU.3.1

6 expans 2 install

CDU.2.1 VDU.1.1 FCC.1.1 LCNHT.1.1 CLNHT.1.1 KHT.1.1 KHT.2.2 DHT.2.1 CDU.3.2

8 expans 1 install

CDU.1.1 VDU.1.1 FCC.1.1 LCNHT.1.1 CLNHT.1.1 DHT.1.1

6 expans

GAMS @ Intel i7-3820QM 2.7GHz 16GB

(R,U,N,T) Refinery Unit Type Number of the Unit type Time

8

U.N.T U.N.T U.N.T (20 exp / 3 inst)

MILP/NLP (T=3)* step 1 2

CPU (s) MILP (CPLEX) 0.22 0.14

NLP (CONOPT) infeas 87.14

NLP (SNOPT) 92.09 -------

NLP (IPOPT) 345.68 -------

NPV (bi U$)

MILP (CPLEX) 30.995 30.995

NLP (CONOPT) infeas 31.273

NLP (SNOPT) 31.273 -------

NLP (IPOPT) 31.273 -------

Discrete Var.

Eq. Var. Non-zero

Non-Linear

--- 1,538 1,986 14,888 9,964

444 5,960 7,620 28,693 ---

--- 7,223 10,751 70,376 45,446

MILP (T=3)

NLP (T=3)

NLP (T=1)

*MINLP (DICOPT) problem does not converge due to NLP solver infeasibilities


Conclusions

Novelty:

• Aggregated multi-site approach for capacity expansion of a country/company

• Nonlinearities from processing and blending to evaluate the capability

• Includes project execution time (excluding the production from expanded units during this period)

• Expansion and Installation to control the capacity increment of units

• Phenomenological decomposition (quantity + quality problems segregated)

• More realistic approach (in a quantitative manner) for strategic and investment planning in the oil-refining industry

10

Impact for industrial applications:

• Aggregated model (NLP and MINLP cases) used to define the overall capacity increment per type of oil-refinery unit demanded in the conceptual projects

• Realistic formulation to predict investments in oil-refinery units

• Avoids overestimating/underestimating capacity expansion/installation

• Evaluates the capability (not only the capacity) by including nonlinearities


Conclusions

quantitative methods for strategic and investment...

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