Nonlinear Production Planning of Oil-Refinery Units for the Future

Article pubs.acs.org/IECR

Nonlinear Production Planning of Oil-Refinery Units for the Future Fuel Market in Brazil: Process Design Scenario-Based Model Brenno C. Menezes,*,†,§ Lincoln F. L. Moro,† Whei O. Lin,‡ Ricardo A. Medronho,§ and Fernando L. P. Pessoa§ †

Refining Planning, PETROBRAS Headquarters, Av. Henrique Valadares 28, 20231-030 Rio de Janeiro, Brazil Chemical Engineering Department, Unigranrio, 25071-202 Duque de Caxias, Brazil § Chemical Engineering Department, Federal University of Rio de Janeiro, 21949-900 Rio de Janeiro, Brazil ‡

S Supporting Information *

ABSTRACT: The oil industry in Brazil has accounted for US$300 billion in investments over the past 10 years, and further expansions are planned to supply the needs of the future fuel market in terms of both quantity and quality. In this paper we analyze the Brazilian fuel production and market scenarios considering the country’s planned investments to prevent a fuel deficit of around 30% in 2020. A nonlinear programming model is proposed to predict the national overall capacity for different oilrefinery units considering four future market scenarios. Single-period operational planning cases for the present and intermediate scenarios and those considered in 2020 are proposed by changing demands, costumer preference, and planned investments to calculate fuel production and import figures for the current and future production and market scenarios in the country. The results indicate a different processing outline in the national overall unit capacity planning.

1. INTRODUCTION

higher than their demanded throughputs, the projects require refitting. Different fuel market scenarios, including the gasoline− ethanol customer preference, are proposed to cover all limiting demands. The final overall capacity planning results may indicate project alteration once the refineries currently in the conceptual phase can still be modified to find the best investment portfolio considering the future official and proposed market scenarios defined in this work. In the next section, the nonlinear operational planning model proposed for all scenarios is presented. In section 3, we describe the Brazilian oil-refining industry, and a review of the national data considered is shown. In section 4, the daily operational profit objective and the scenario tree for all cases are presented. Crude and fuel balances for all scenarios and the units’ demanded throughputs for the 2020 cases are shown in section 5. Finally, the conclusion and further work are discussed in section 6.

Production planning and scheduling is an essential tool in the modern oil-refining industry to predict production and logistics settings for refineries and terminals in the supply chains. To define the overall demanded capacity of oil-refining and logistics assets of a country or a company, crude and fuel balances can be modeled considering scenario-based optimization regarding future scenarios for the overall production framework and market, including refineries and terminals for a whole country or company, or even for regions or segments. In this sense, this work proposes a scenario-based production planning model for the Brazilian oil-refining industry to supply fuel market needs within this decade. Today, as seen in Figure 1,1 the national oil-refining asset expansion includes two grassroots refineries currently under construction and three additional sites currently in the conceptual project phase to prevent a fuel deficit of around 30% in 2020, according to recent forecasts in the country. National planned investments project an increase of 1595 kbpd (kilobarrels per day) in crude distillation capacity, which only includes refineries of the national oil and energy company known as PETROBRAS, which accounted for 98% of the total crude distillation capacity in 2013. The proposed approach predicts the overall capacity of oilrefinery units considering current Brazilian refineries aggregated in one hypothetical refinery called REBRA to approximate the national overall capacity for several types of units. The singleperiod nonlinear programming (NLP) operational planning model for REBRA defines the national crude diet, unit throughputs, fuel production, and import amounts, taking into account profit maximization in 2013, 2016, and 2020. Regarding the expected and proposed fuel market scenarios for the conceptual projects in 2020, with planned overall capacities © 2014 American Chemical Society

2. NLP OPERATIONAL PLANNING MODEL Production and market scenarios are tested considering the NLP operational planning model presented in this section. Figure 2 shows the oil-refinery units’ framework considered. For the cases in analysis, the hydrocracker (HCC) is excluded from the hypothetical refinery for the current scenario in 2013 as well as atmospheric residuum (ATR) to delayed coker (DC). For the 2016 and 2020 scenarios, both are considered. 2.1. Swing-Cut Distillation Modeling. Distillation units are often modeled into several operational modes, such as Received: Revised: Accepted: Published: 4352

July 17, 2013 December 20, 2013 February 17, 2014 February 17, 2014 dx.doi.org/10.1021/ie402291v | Ind. Eng. Chem. Res. 2014, 53, 4352−4365

Industrial & Engineering Chemistry Research

Article

Figure 1. Oil product deficit of 30% without the refineries in the conceptual project phase.

Figure 2. Hypothetical refinery REBRA.

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dx.doi.org/10.1021/ie402291v | Ind. Eng. Chem. Res. 2014, 53, 4352−4365


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maximum gasoline, kerosene, or diesel.2 Each mode is based on a set of temperature cut points defined by previous experience, experimental data, or process simulators, so that internal fractions can be managed to both adjacent distillates, creating swing cuts, which can be included in a distillation model instead of considering the final distillates with fixed yields and fixed properties based only on the pooled crude-oil feed. Thus, the swing-cut modeling approximates the temperature cut-point variation without the need to recut the crude assay.3 The swing-cut approximation models the consequence of the thermodynamic variation inside the distillation column, which, driven by pressure and temperature, allows some hydrocarbon fractionation by creating internal cut swinging, which can be expressed as a volume ratio on crude feed, as a certain boiling temperature range, or as volume/mass transfer ratios. Zhang et al.4 used 5% and 7% volume ratios on crude feed as the sizes of the naphtha/kerosene and kerosene/diesel swing cuts, respectively. Li et al.5 considers that the swing-cut ranges are bounded by the union of three operational modes in the CDU (crude distillation unit) taking into account maximization of naphtha, light diesel, and heavy diesel to find mass transfer ratios for the swing cuts. Despite the CDU processing-variance consideration by the swing-cut modeling, these authors considered a fixed crude diet for their cases, which reduces decision-making in planning and scheduling cases, and used Watkis6 correlations based on yields to predict the final distillate properties and not the crude-oil assay property data. Differently, in this work the mixed crude oil given by the CDU feed diet found in the model defines the blended fractions inside the distillation towers (cuts, including the swing cuts), and then a new pooling occurs to get the final distillate yield and properties for an optimal volume yielding distribution given by the exchangeable cuts. These swing cuts are split into light and heavy fractions, the former representing the swing-cut part flowing to the light distillate and the latter representing the one flowing into the heavy distillate. Optimization involving quantity variables for the light and heavy swing cuts and quality variables for the whole swing cut as presented in this work is found in commercial planning platforms such as GRTMPS (Haverly), Aspen PIMS (Aspen Technology), and RPMS (Honeywell). Their solutions include the crude diet and final distillate quality optimization, both related to the cut and swing-cut internal blending modeling, by employing home-grown successive linear programming algorithms to solve the nonlinearities. The mentioned authors do not consider properties for the swing cuts4 or calculate the final distillates properties by empirical correlations based on distillate yields,5 nor do they consider crude-oil assay data as in this work, in which a typical 30−40 °C true boiling temperature (TBP) range is set to the swing-cut fractions, which is already expressed in the given crude-oil assay yields and properties. Figure 3 shows the material flow for cuts c and final cuts fc of the CDU. The light and heavy swing-cut fractions mix with their upper (light) and lower (heavy) cuts, respectively, internally to the tower and not as schematically presented. Given the CDU and VDU (vacuum distillation unit) configurations in Figure 2, the crude is separated into different internal cuts; some of them can swing (SW1, SW2, SW3) between their adjacent final cuts or final distillates as seen in Figure 3. Each crude cr to be processed (Qcr,CDU) is defined as a CDU throughput (QFCDU) volume ratio (υcr) as in eq 1, whose sum is calculated by eq 2.

Figure 3. Cut and swing-cut material flow modeling for CDU yields.

Q cr,CDU = υcrQFCDU ∀ cr

(1)

∑ υcr = 1 ∀ CDU

(2)

cr

The quantity of CDU/VDU internal cuts c flowing to final cuts fc is given by eq 3 using the crude-oil assay data in Table S-1 (see the Supporting Information). Ycr,c represents the yields for the CDU and VDU cuts based on the crude feed. Only the swing cuts flow to both adjacent final cuts. For other cuts, the first sum in eq 3 has only one term, because the no-swing cuts flow only to their respective final cuts as represented in Figure 3.

∑ Q c,fc = ∑ Q cr,CDUYcr,c ∀ c fc

cr

(3)

Gcr,c, Scr,c, and Acr,c in Tables S-2−S-4 (see the Supporting Information) represent the specific gravity, sulfur content, and acidity of each cut in the crude assays. Gc and Sc in eqs 4 and 5 are the specific gravity and sulfur content values for each internal cut considering the CDU crude diet pool found in the model. The acidity value for each cut Ac, which is not represented, is a massbased property like the sulfur content. Gc =

Sc =

∑cr Gcr,cQ cr,CDUYcr,c ∑cr Q cr,CDUYcr,c

∀c

∑cr Scr,cQ cr,CDUGcr,cYcr,c ∑cr Q cr,CDUGcr,cYcr,c

(4)

∀c (5)

After the CDU feed defines the quantity of cuts flowing to the final cut pools (Qc,fc), as in eq 3, and cut qualities, as in eqs 4 and 5, another blending occurs to find the final cut fc quantities and qualities, eqs 6−8. Only in the final cuts with swing cuts in between can the qualities Gfc, Sfc, and Afc change. All VDU final cut qualities are equal to those of their respective cuts, calculated by eqs 4 and 5. 4354


Industrial & Engineering Chemistry Research QSfc =

∑ Q c,fc ∀ fc c

Gfc =

Sfc =

∑c GcQ c,fc ∑c Q c,fc

∑c ScGcQ c,fc ∑c GcQ c,fc

Article

modification of properties in hydrotreaters (HTs). The unit removes sources of molecular instability and contamination such as sulfur, nitrogen, metals, and unsaturated hydrocarbons. As the unit operates at high pressure and temperature, some molecular cracking inevitably occurs; thus, some properties such as specific gravity and octane number change. In this work, we modeled the sulfur reduction promoted by the hydrotreaters as a variable (ΥHT),8−10 as seen in eq 12. The lower and upper bounds for the medium and high severity hydrotreaters for diesel are 0.800 ≤ ΥD1HT ≤ 0.980 and 0.960 ≤ ΥD2HT ≤ 0.998, respectively. For gasoline hydrotreaters (LCNHT and CLNHT) are 0.500 ≤ ΥLCNHT/CLNHT ≤ 0.990. Severity in kerosene hydrotreater (KHT) is not being considered. The specific gravity reduction factor (ΔHT) promoted by residual molecular cracking is treated as a fixed value for all hydrotreaters like in eq 13 and its value in this work is 0.99.

(6)

∀ fc (7)

∀ fc (8)

The final cut acidity Afc is calculated as shown for Sfc. As noticed, the properties Gc, Sc, and Ac for each swing cut, based on the crude diet and assay, are the same in both adjacent final cuts. This is the main simplification in the swing-cut model, which considers that the light and heavy swing-cut fractions have the same qualities. If the swing cut flows to its adjacent lighter distillate, it carries heavy ends, which will influence properties such as the sulfur content and gravity of the lighter distillate. Similarly, moving the swing cut to its adjacent heavier distillate will bring light ends, particularly affecting properties such as the viscosity and flash point, which are more sensitive to light ends.5 In the context of this work, this simplification is quite acceptable for strategic plans, and an improved swing-cut model addressing quality variations for both light and heavy swing-cut streams can be found in ref 7 as well as a full review on distillation models in planning and scheduling problems. An approach similar to that of the swing-cut model described in this work is used by the Energy Information Agency (EIA) to plan the fuel balance in the United States;3 however, it considers average fixed qualities for the final distillates, as we are doing for other unit output stream properties. We consider the yield and property variations to predict distillation tower outputs by permitting crude-oil diet and final cut variation, a big step toward increasing the planning results in a high-level decision-making model in the oil-refining industry. Despite the model improvement by considering nonlinearities, which in turn tend to be ill-conditioned when their size increases, global optimal solutions are not a guarantee in nonconvex models. Initial value randomization and do/loop searching can find better results iteratively. 2.2. Other Oil-Refinery Units. As mentioned, product yields Yu,s and properties for other oil-refinery units are considered averaged fixed values. The yield for each stream s is given as a unit throughput ratio, as seen in eq 9. All necessary data are given in Table S-5 (see the Supporting Information). QS u,s = Yu,sQF u ∀ u, s

s

QS u,s =

∑ Q u,s ∀ u, s ∈ do s

(12)

G HT,s = G HT(1 − ΔHT) ∀ HT, s

(13)

The sulfur content and specific gravity, SHT and GHT, respectively, in the hydrotreater feed are volume-based properties, which can be calculated by eqs 7 and 8 considering the final distillates or final cuts fc as a unit feed mix (mixer) and the cut properties (Gc and Sc) as the properties of each upstream flowing to the unit. The properties for the blended crude-oil processed and intermediate and final products are found in the same way. Mixing and splitting involving oil-refinery units, other blending rules and properties, and other processing transformations can be found in refs 8−10. 2.3. Octane Number Calculation: Ethyl Equation. The octane number calculations presented in eqs 14 and 15 are the ethyl formulations11 to predict the research octane number (RON) and motor octane number (MON) needed to determine the retailed gasoline recipe. The octane rating or number is a gasoline property related to the combustion efficiency in Otto cycle engines in which the maximum compression ratio is intended at the ignition spark, avoiding spontaneous ignition under high-pressure conditions. RONVs = RONs + a[(RONs − RONV )(Js − JV )] + b(AROs − ARO V )2 + c(OLEs − OLE V )2 + d[(AROs − ARO V )(OLEs − OLE V )] ∀ s (14)

(9)

Moro et al.8 and other authors9,10 modeled operational planning and scheduling cases for refining process units considering a network whose streams are linked to them by mixes and splitters as shown in eqs 10 and 11; the former concentrate upstream streams (up), and the latter distribute downstream ones (do). The units are connected considering the framework shown in Figure 2.

∑ Q s,u = QFu ∀ u, s ∈ up

SHT,s = SHT(1 − ΥHT) ∀ HT, s

MONVs = MONs + e[(MONs − MONV )(Js − JV )] + f (AROs − ARO V )2 + g[2(OLEs − OLE V )2 (ARO VQ − ARO V 2 ) − (ARO VQ − ARO V 2 )2 ] ∀s

(15)

The blending correlation for RON and MON is highly nonlinear and depends on the recipe components to be calculated, because it considers molecular interactions based on the aromatic (ARO) and olefin (OLE) concentrations. RONVs and MONVs represent blending values for RON and MON of each component in the gasoline pool, whose final RON and MON values are volume-based properties considering blending values RONVs and MONVs as the component properties. The blending value of a blending component changes with the

(10)

(11)

As specific gravity and sulfur content are included in the model to allow variations in the final product qualities based on crude diet and swing-cut variations, it is important to consider 4355



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in 2013 and the 2008−2009 economic crisis uprising return worldwide. 3.2. Gasoline−Ethanol Mix and Ethanol for Fueling. Ethanol commercialization presents particular dynamics for small- and medium-car fleets in Brazil. It can be part of the retailed gasoline and 100% fuel in flex-fuel fleets. The ethanol content in the retailed gasoline varies between 25% to 18% in volume, a proportion controlled by the ethanol market demand and prices. During periods when the country needed to import ethanol to blend into gasoline A (pure gasoline), the ethanol content was 18%. The value considered in this work is 25%, which is the limit situation without any influence from the ethanol market, a preferred condition due to the gasoline deficit in the country. The definition of retailed gasoline (GLNC) is any mix of gasoline A (GLNA) and ethanol permitted by the blended volume range. An ethanol content higher than 25% in gasoline can negatively affect the performance of a non-flex-fuel automobile and intensify corrosion problems inside the motor. Today in Brazil, flex-fuel fleet sales represent 90% of the market in both the small- and medium-car categories, and it is expected that the total flex-fuel fleet in these categories, currently at around 60%, will reach 80% within the next 5 years. In addition to the normal demand increase in fuels, the GLNC demand increase in the period 2009−2012 (11.8% p.a.) was influenced by the ethanol for fueling (ETH) to GLNC consumer preference shift. This occurred because of the ethanol price surge, which was an effect of nationwide sugar cane harvesting problems in 2009 and of the escalation of sugar demand overseas, both of which shaped the current ETH−GLNC fuel preference. Besides, the widening demand for retailed gasoline maintains the ethanol demand and price growth once ethanol is included in its formulation. As a consequence, the relative price between ethanol for fueling and retailed gasoline remains higher than 0.7, so that gasoline is more attractive for fueling. Considering the heats of combustion and engine efficiency with both fuels, when ETH prices are higher than 70% of those of GLNC, GLNC is considered more profitable to fuel flex-fuel cars. For values lower than 70%, ETH is preferred. This fact is well-known to the consumer who compares the ETH/GLNC relative price and decides which fuel to buy. Considering the gasoline/ethanol market stabilization, the retailed gasoline demand growth considered in Figure 4 is 7.2%, which is its increase discounting the ethanol for fueling demand decrease during 2009−2012, hence excluding the influence of the ETH (100% ethanol) shift. As shown, the ETH shift to GLNC reached its steady state in 2012, and on the basis of this fact, we consider that the range of demand for retailed gasoline plotted in the graph represents the equilibrium between GLNC and ETH driven by the consumer of the flex-fuel fleet. GLNC is the current situation considering the ETH shift stabilization, and GLNCETH is the situation where the ETH demand decrease is discounted from the retail gasoline demand between 2009 and 2012. 3.3. Future Fuel Demands. This work simulates the production and market scenarios in an operational planning manner to find the best operational setting, so the future fuel demands are given in a daily flow considering the demand scenarios shown in Table 1. Table 2 shows the diesel grade evolution for the next several years,14 which is taken into consideration when future diesel demands are defined. 3.4. Current and Planned Capacities in 2013, 2016, and 2020. The refinery design considered in this work is shown in Figure 2. Table 3 shows the capacities of the units for the three production scenarios aforementioned. All data regarding the

molecular composition and recipe; thus, it is different for every blend.12 RONs, MONs, OLEs, and AROs represent properties for each stream s, which compounds the gasoline formulation as shown in Figure 2. They are fixed for the streams in Table S-5 (see the Supporting Information) and calculated as a blend for coker light naphtha hydrotreater (CLNHT) output and for the gasoline C (GLNC) blender stream. Js is the octane number sensibility defined as RONs − MONs. JV, RONV, MONV, OLEV, and AROV are volume-based gasoline mix properties. AROVQ is the volumebased property for ARO2. The coefficients a−g are given in Table S-6 (see the Supporting Information), but they can be determined experimentally. RON and MON decrease by values of 1.2 and 1 after hydrotreating units.

3. PROBLEM STATEMENT: THE BRAZILIAN OIL INDUSTRY SCENARIO Considering the fuel production and market scenarios in 2013, 2016, and 2020, in this work we analyze, using the NLP operational model proposed, the national fuel balance for each scenario taking into account the situation in 2013 and the planned investments and different future demands for 2016 and 2020. Data from several official sources were considered to represent the situation in 2013, which is the baseline on which the other ones were drawn. The national fuel production and processed crude-oil results are used to validate the scenario in 2013. In the next subsections the available data on the Brazilian oil-refining industry and the production and market scenarios are presented. 3.1. Fuel Demands and Production. Figure 4 shows the national fuel production and demands during the period 2000−

Figure 4. Demand and production levels13 and forecast considering the 2009−2012 trends.

2012 and the future demands considering the 2009−2012 boost in fuel demands in the country. In this work we propose future fuel market scenarios considering (1) 4.2% p.a. for all fuels, based on the Brazilian GDP forecast,13 and (2) 2009−2012 trends for each fuel as in Figure 4 considering GLNC and GLNCETH for gasoline. The boost in national fuel demands after the 2008−2009 global crisis is considered a representative alternative for the future market scenarios. This situation is a consequence of the economic growth in the country and is still expected to rise even with the economic and political instability in Brazil that emerged 4356



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Table 1. Demand (k m3/d) Forecast for 2016 and 2020 2009−2012 trends

4.2% p.a.

product

2013a

2016

2020

2016

2020

liquid petroleum gas (LPG) gasoline C (GLNC) gasoline C − ethanol ↓ (GLNCETH) jet fuel (JET) diesel (DSL) 10 wppm S 500 wppm S 1800 wppm S 3500 wppm S diesel total

36.2 112.4 90.6

38.6 148.8 119.9

41.2 196.8 158.6

42.6 132.5 106.8

50.2 156.2 125.9

19.7

26.4

35.5

23.2

27.3

16.0 80.1 56.0 8.0 160.1

60.6 90.9 40.4 10.1 202.0

153.0 51.0 45.9 5.1 254.9

56.6 84.9 37.7 9.4 188.7

133.5 44.5 40.1 4.5 222.5

a

Figure 5. Crude oil produced, imported, and processed and import and export prices.13

Last 12 months until October 2013.13

Table 2. Diesel Grade Market Share (%)14 diesel grade (wppm sulfur)

2013

2016

2020

10 500 1800 3500

10 50 35 5

30 45 20 5

60 20 18 2

In Figure 6 the national oil production distribution by °API range is shown, as well as the average °API and sulfur content for each group.13

Table 3. Overall Refining Process Capacities (k m3/d) for the Three Production Scenarios crude distillation unit (CDU) vacuum distillation unit (VDU) residue fluid catalytic cracking (RFCC) fluid catalytic cracking (FCC) hydrocracking (HCC) propane deasphalting (PDA) delayed coker (DC) delayed coker with atmospheric residue as feed (DCA) light cracked naphtha hydrotreater (LCNHT) coker light naphtha hydrotreater (CLNHT) stabilizer (ST) kerosene hydrotreater (KHT) diesel hydrotreater (medium severity) (D1HT) diesel hydrotreater (high severity) (D2HT) reformer (REF)

2013

2016

2020

310* 140* 22 76

372* 153* 22 76 10 10 50 24*

536 260 22 76 74 10 100 24*

54 34 34 15* 60* 68* 12

54 62 62 15* 60* 135 20**

10 42

54 22 22 15* 60* 30* 7

Figure 6. National crude-oil production in 2012.

In this work we consider five different crude oils to represent the national and imported groups, taking into account the current national production limit. The national medium crude is split into presalt and medium. The presalt crude is considered medium-light to light oil and represented 14% of the entire national production in 2012.1 An ultralight crude is given to represent the imports to reach the °API optimal for the crude oil to be processed in the Brazilian fuel market and production scenarios. The crude oils considered are shown in Table 4. The

*

Upper and lower bounds are the same to avoid local solution. Added 8 k m3/d arbitrarily (different from conceptual project) to yield feasible results. **

Table 4. Crude Oils Considered in This Work crude national light presalt medium heavy imported ultralight

overall oil-refinery unit capacities considered for the productive scenarios are presented in the Oil and Gas Journal15 and ref 16. In the cases of studying, the minimum feed permitted in the units is 68% of their overall capacity, excepted in the units showed in Table 3 and in the propane deasphalting unit (PDA), which is 30%. 3.5. National and Imported Crude Oils. Figure 5 shows the crude-oil amounts by national production, imports, and processing as well as the export and import prices. As shown, since 2008, the country has been self-sufficient in equivalent oil but still needs to import ultralight oil to increase medium distillate (jet fuel and diesel) production and paraffinic oil for lubricants.

°API

G (g/cm3)

S (%)

A (mg of KOH/g)

υmax

33.2 29.7 25.2 20.5

0.859 0.878 0.903 0.931

0.091 0.344 0.519 0.608

0.226 0.255 0.196 1.007

0.09 0.14 0.52 0.25

45.2

0.801

0.049

0.096

upper bounds (υmax) for the national crude to be processed are defined by their production in 2012 given in Figure 6. For the 2016 and 2020 scenarios, the presalt crude production is predicted to increase to 22% and 33%, respectively, as expected by PETROBRAS.1 4357



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specifications of the final product properties are shown in Table 6. 3.8. Other Relations. Two other constraints are modeled related to the current official ethanol content in the retailed gasoline pool (GLNC), eq 16, and the light coker naphtha maximum concentration in the coker light naphtha hydrotreater (CLNHT), eq 17. This upper bound for the CLN stream protects the catalyst bed from high metal concentration.

3.6. Crude Oil and Fuel Prices. The crude prices per cubic meter considered were US$702 for the imported oil and US$587 for the exported oil (average values in the past 12 months until October 2013).13 The country exports unprocessed heavy and medium crude oil, and considering the national heavy crude price as US$570 and the exported crude to be composed in volume by 30% medium and 70% heavy, the medium oil price considered is US$592. For the national light and presalt crude, prices are somewhat intermediate between the national medium and Brent oils, closer to the former once they are national crude and heavier than the latter. The standard crude oil Brent (38°API, 0.45% S) price considered is US$687 (US$109.2 per barrel) in the North Sea port.17 The fuel prices considered were the retail prices in the past 12 months until October 2013,18,19 and the producer percentages are shown in Figure 7.20,21 The imported prices consider the U.S.

Gulf region,17 adding transport, internalization, and tax expenses. All the sources of official prices are shown in Table 5. The forecast prices for 2016 and 2020 grow at 4.2% p.a. as per the expected GDP rate considered in this work. 3.7. Crude and Fuel Quality Specifications. The upper and lower bounds of the CDU feed properties and the Table 5. Producer Prices (US$/m3; US$1 = R$2.2) of Crude, Products, and Imports in Brazil Grow at a Rate of 4.2% p.a.*

products13

national

imported FG LPG naphtha gasoline C jet fuel diesel

light presalt medium heavy ultralight

10 wppm S 500 wppm S 1800 wppm S 3500 wppm S

FO coke ethanol19 imports17

LPG gasoline A jet fuel diesel

15 wppm S

2013

2016

2020

600.0 595.0 592.0 570.0 702.0

678.8 673.2 669.8 644.9 794.2

800.2 793.6 789.6 760.2 936.3

380.0 545.0 618.3 760.0 662.0 636.0 623.3 610.8 416.0 530.0 590.0 398.2 762.3 860.0 834.1

429.9 616.6 699.5 859.8 749.0 719.5 705.2 691.1 470.6 599.6 667.5 450.5 862.4 973.0 943.7

506.8 726.9 824.7 1013.6 882.9 848.3 831.3 814.7 554.8 706.9 786.9 531.1 1016.7 1147.0 1112.5

(16)

Q CLN,CLNHT ≤ 0.30QFCLNHT

(17)

4. OPERATIONAL PLANNING OBJECTIVE: DAILY OPERATIONAL PROFIT Eight future scenarios in 2020 are illustrated in Figure 8. At first, consumer preference between ethanol for fueling and retail gasoline is taken as a starting point by discounting and not the ethanol shift proposed previously. The second choice is the demand increase rate considering the 2009−2012 trends for each fuel and 4.2% for all, and the last option is to consider the refineries in the conceptual project to be ready or not in 2020 to avoid the 30% deficit in fuels. Crossover over time is not permitted, so that once a scenario option is selected it is true for the following scenarios. The single-period objective function given in millions of U.S. dollars per day is summarized in eq 18. Operational costs from inventory, utilities (vapor, water, hydrogen, nitrogen, fuel gas, electricity), and materials such as catalysts and caustic solution are not directly considered in the model. Refcost approximates the overall refining costs for a standard production cost in the American refineries, considering an overall operational cost of US$4.5 per barrel of product (US$28.3/m3).22 Additionally, HT operational costs defined as a function of its severity (ΥHT) and feed flow (QFHT)8−10 are needed to avoid sulfur concentration giveaways when we have different grades of diesel. Also, a good practice is to consider one hypothetical blender for each grade; both were used in the models. The taxes are discounted from the profit considering a fixed tax ratio (tr) of 25%, which is practiced in Brazil for companies’ profit taxes. The refining operational costs represented by Refcost increase 4.2% p.a.

Figure 7. Fuel price percentage.20,21

crude13

QFETH = 0.25QFGLN

max profit = (1 − tr)(∑ (pr p − Refcost)Dem p p

−

∑ pr crυcrQFCDU − ∑ primpQFimp cr

−

∑ ΥHTQFHT) HT

imp

(18)

The crude diet (υcr) is influenced by the overall prices (pr), product demands (Demp), processing structure, CDU feed ratio (QFCDU), and product specifications such as specific gravity, octane number, sulfur content, and acidity. The operational planning model includes eqs 1−18. Fuel demands lower and upper bounds are set to be only 0.01% apart, avoiding surplus of any fuel. For the 2020 cases with the projects in the conceptual phase performed, if the throughput is lower than the capacity, which in the model is the throughput upper bound (QFUu ), the country’s planned capacity expansion for u in 2020 needs refitting. Seasonal variations regarding demands can be considered by verifying the level of flexibility for the refining framework for

*

Past 12 months until October 2013. 4358



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Table 6. CDU Feed and Final Product Property Specifications CDU feed GLN (GLNC) jet fuel diesel 10 wppm S 500 wppm S 1800 wppm S 3500 wppm S

Gmin (g/cm3)

Gmax (g/cm3)

Smax (mg of KOH/g)

Amax (mg of KOH/g)

0.850 0.720 0.771

0.950 0.775 0.836

0.800 0.005 0.300

0.60

0.820 0.820 0.820 0.820

0.850 0.865 0.880 0.880

0.001 0.050 0.180 0.350

MONmin

RONmin

82

88

Table 7. REBRA Profit for the Different Gasoline and Diesel Pricing Policiesa last 12 months until October profit sales crude costs import costs operational costs HT costs profit/crude US$/m3 US$/barrel no. of equations no. of variables no. of nonzero elements no. of nonlinear elements CPU (s) CONOPT IPOPT SNOPT

Figure 8. Initial, intermediate, and final scenarios considered.

gasoline and diesel maximization modes. Annual average demand data of 2013 are used as the baseline for the cases in analysis, so these seasonal variations are not being included in the scope of this work.

5. RESULTS AND DISCUSSION The national refining framework and fuel market demands in October 2013 are the baselines to find the intermediate scenarios for 2016 and the eight scenarios for 2020 as previously defined. All case studies were implemented in the GAMS modeling language,23 version 23.9.3, on an Intel Core 2 Duo (3.00 GHz, 16.0 GB of RAM) and used as starting points the unit capacities and output standard values for flow variables and given specifications for calculated properties. The overall data given in this work can be used to find initial values. The NLP problems are solved by nonlinear general-purpose optimizers such as CONOPT,24 which is based on the reduced gradient method, IPOPT,25 which utilizes interior point methods, and the solver SNOPT,26 which is based on successive quadratic programming. 5.1. Pricing Policy in 2013. Table 7 shows the profit obtained in the country considering different pricing scenarios. The average pricing data in the past 12 months until October 2013, as given in Table 5, are considered for gasoline and diesel sale prices in Brazil and the U.S. Gulf. As indicated, the current refining activity in Brazil is not lucrative. This is a well-known condition due to the lower producer sale prices of national gasoline and diesel in comparison with import prices. If prices in the U.S. Gulf are considered for gasoline and diesel 15 wppm S sales (682.43 and 760.89 US$/m3, respectively), the loss difference (−US$16.761 million/d) is close to the Brazilian Center of Infrastructure estimate,27 which evaluates losses from January to May 2013 of US$2.2 billion (−US$14.379 million/d). The prices for other grades of diesel have the same price relation found in Table 5. As the country needs to import gasoline and

Nov 29th policy

demanded policy

Brazil

U.S. Gulf

Brazil

Brazil

−21.951 228.901 186.213 60.518 11.289 0.150

−5.190 251.249 186.213 60.518 11.289 0.150

−13.786 239.788 186.213 60.518 11.289 0.150

1.068 259.594 186.213 60.518 11.289 0.150

−70.81 −11.26

−16.74 −2.66

0.328 0.952 0.125

0.421 1.123 0.063

−44.47 −7.07 406 460 1772 1061 0.452 0.827 0.062

3.45 0.55

0.359 2.012 0.156

a

Values are given in millions of U.S. dollars per day unless otherwise noted.

diesel to supply the market, the price difference between the national produced fuels and the imports is assimilated by the national oil and energy company PETROBRAS. Although the country needs to import LPG and jet fuel, these fuels do not damage the company’s profitability because the controlled LPG price in Brazil is similar to the international price and the jet fuel prices are not controlled by the government. Currency variations along the year are not being considered. The unusual controlled price situation inside the oil-refining industry in Brazil threatens companies’ sustainability. The new pricing policy announced Nov 29, 2013, increased the producer price of retailed gasoline by 4% and diesel by 8%, but this move was not sufficient to overcome the daily losses (−US$13.786 million/d) in the country. In this work we consider price increases of 15% for retail gasoline and 20% for diesel in its different grades to yield a positive gain (US$1.068 million/d). This price increase is evaluated by the market as the demanded price policy to avoid losses and to get minimal expected margin returns (of around US$0.5/barrel). This price scenario, with positive gains, is considered to calculate the future scenarios in the daily single-period NPL operational planning model for fixed production frameworks in 2013, 2016, and 2020. 5.2. Current and Planned Production Scenarios in 2013 and 2016. Table 8 shows the 2013 scenarios and all possible intermediate scenarios until 2016. The limit demands for the 4359



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Table 8. Production (k m3/d) and Market Scenarios for 2013 and 2016 2013

2016 2009−2012 trends

crude diet (vol %)

LPG

gasoline C (GLNC)

jet fuel (JET)

diesel (DSL)

national

imported national imported sales national imported sales national imported sales national

imported sales


GLNA GLNA ethanol GLNC

10 wppm S 500 wppm S 1800 wppm S 3500 wppm S 15 wppm S

GLNC

GLNCETH

GLNC

GLNCETH

GLNC

GLNCETH

9.0 14.0 40.1 25.0 11.9 16.8 19.3 36.2 72.1 12.3 28.1 112.4 15.0 4.7 19.7 16.0 52.6 56.0 8.0 27.4 160.1

9.0 14.0 40.1 25.0 11.9 16.3 19.9 36.2 68.0

9.0 22.0 33.6 25.0 10.4 20.7 17.9 38.6 84.0 27.6 37.2 148.8 16.8 9.6 26.4 60.6 56.9 40.4 10.1 34.0 202.0

9.0 22.0 33.6 25.0 10.4 20.7 17.9 38.6 83.6 6.3 30.0 119.9 16.8 9.6 26.4 60.6 56.9 40.4 10.1 34.0 202.0

9.0 22.0 33.6 25.0 10.4 20.7 21.9 42.6 83.6 15.6 33.1 132.5 16.8 6.4 23.2 56.6 64.2 37.7 9.4 20.7 188.7

9.0 22.0 30.9 25.0 13.1 20.3 22.3 42.6 80.1

22.7 90.6 15.0 4.7 19.7 16.0 51.1 56.0 8.0 29.0 160.1

Table 9. Actual and Calculated Data for the Current Scenario (GLNC) in 2013 Processed Crude 27.9 12.2

a

calcd °API imported (%) National Production (k m3/d)

27.6 11.9

actuala

product

calcd

22.2 76.4 15.3 133.3

LPG GLNA JET DSL

16.8 72.1 15.0 132.7

26.7 106.8 16.8 6.4 23.2 56.6 65.3 37.7 9.4 19.7 188.7

Considering the main fuel production, the lower LPG found is expected once the model excludes LPG from CDU for lubricants, lubricant plants, and production from liquid natural gas plants and condensates. The lower GLNA obtained is explained because the production from non-PETROBRAS refineries, whose daily gasoline A production reaches around 4 km3/d, is not included in the model. For JET and DSL the model presented good precision compared with the actual data. The GLNCETH case in 2013 shows that, without the ethanol to gasoline market shift after 2009, the country would be able to cease gasoline imports, as in the period before the current flexfuel fleet consumer preference setup between retailed gasoline and ethanol for fueling. The economic data for the 2013 and 2016 (4.2% p.a.) scenarios in Table 10 indicate that the cases with higher profits are those with the ethanol−gasoline shift (GLNC), because of the lower ethanol price, which is mixed at 25% in volume with refined gasoline (GLNA), to produce GLNC. As the GLNC price in the demanded pricing policy (US $711/m3) is higher than that of the blended ethanol (US$590/ m3), higher profits in the GLNC scenarios are expected, compared with those of their respective GLNCETH pair. For the 2016 (2009−2012 trends) scenarios, the gasoline-ethanol price difference was not sufficient to overcome the extra expenses with higher operational costs in the GLNC case comparing with its GLNCETH pair. Additionally, higher expenses with gasoline import due to higher demands is a result of imported gasoline delta between the GLNC and GLNCETH pair higher if compared with the other cases with GLNC more profitable. This outcome is a consequence of the higher import prices for gasoline in comparison with the producer price sales. 5.3. Conceptual Project Scenarios in 2020. The proposed scenarios in 2020 take into account the projects in the conceptual phase and whether they were executed for the four market scenarios; hence, eight cases are presented, as shown in Figure 8, with maximum and minimum investment and import cases indicated. For the four cases in which the projects in the

retailed gasoline are represented by GLNC and GLNCETH. The former is the current market and production scenario in which the ethanol to GLNC shift reached its steady state. GLNCETH discounts the ethanol decrease since its price surge in 2009. The current scenario in 2013 (GLNC) is compared with the national actual data for fuel production and crude-oil processed as shown in Table 9.

actuala

4.2% p.a.

Source: ANP26 (past 12 months until October 2013).

The calculated data presented reasonable accuracy when compared with the national results in Table 9. The imported crude represented 19% of all processed crude by the country in the past 12 months until October 2013,13 but discounting the distillation for lubricants, which are not considered in this work, the national imported crude for fuel production is around 12.2%, which is very close to the value found in the proposed model (11.9%). Also the °API calculated for the processed crude presented good precision. When compared with the national crude production (average of 24.36 °API), it became clear that importing light or ultralight crude for the demanded crude-oil blend to be processed by the current refining assets is a necessity. 4360



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Table 10. Economic and Model Data for the 2013 and 2016 Scenariosa 2013

2016 2009−2012 trends

profit sales crude costs import costs operational costs HT costs profit/crude US$/m3 US$/barrel no. of equations no. of variables no. of nonzero elements no. of nonlinear elements CPU (s) CONOPT IPOPT SNOPT a

4.2% p.a.

GLNC

GLNCETH

GLNC

GLNCETH

GLNC

GLNCETH

1.068 259.594 186.213 60.518 11.289 0.150

0.161 246.861 186.213 49.445 10.843 0.146

3.529 371.013 252.249 98.063 15.797 0.199

4.177 347.801 252.249 74.911 14.873 0.199

5.150 345.381 252.249 71.189 14.878 0.199

4.482 326.415 253.475 52.625 14.145 0.195

3.45 0.55

0.52 0.08

9.49 1.51

11.23 1.79

13.84 2.20

12.05 1.92

0.608 0.639 0.110

0.514 0.874 0.140

406 460 1772 1061 0.515 0.469 0.078

466 532 2115 1276 0.374 1.591 0.078

0.655 0.826 0.156

0.453 0.702 0.125

Values are given in millions of U.S. dollars per day unless otherwise noted.

Table 11. Production (k m3/d) and Market Scenarios in 2020 2009−2012 trends GLNC crude diet (vol %)

LPG

gasoline C (GLNC)

jet fuel (JET)

diesel (DSL)

capacity refitting

national

imported national imported sales national imported sales national imported sales national

imported sales CDU VDU HCC DC+DCA D2HT CLNHT ST REF


GLNA GLNA ethanol GLNC

10 wppm 500 wppm 1800 wppm 3500 wppm 15 wppm 536 260 74 124 135 62 62 12

4.2% p.a.

GLNCETH

GLNC

GLNCETH

−30%

0%

−30%

0%

−30%

0%

−30%

0%

0%*

9.0 33.0 23.2 25.0 9.8 20.7 20.5 41.2 84.7 63.0 49.2 196.8 16.8 18.7 35.5 67.8 49.1 45.9 5.1 87.2 255.0

9.0 33.0 27.2 25.0 5.8 29.6 11.6 41.2 111.7 35.9 49.2 196.8 28.3 7.1 35.5 141.5 51.0 45.9 5.1 11.4 254.9 505.1 227.9

9.0 33.0 23.2 25.0 9.8 20.7 20.5 41.2 84.1 34.8 39.7 158.6 16.8 18.7 35.5 67.8 49.1 45.9 5.1 87.2 255.0

9.0 33.0 33.0 25.0

9.0 33.0 25.4 25.0 7.6 20.5 29.8 50.2 84.1 33.1 39.1 156.2 16.8 10.5 27.3 73.1 44.5 40.1 4.5 60.4 222.6

9.0 33.0 33.0 25.0

9.0 33.0 25.4 25.0 7.6 20.5 39.8 50.2 83.7 10.7 31.5 125.9 16.8 10.5 27.3 73.1 44.5 40.1 4.5 60.4 222.6

9.0 33.0 33.0 25.0

9.0 33.0 33.0 25.0

25.0 25.2 50.2 94.5

25.4 24.9 50.2 94.5

31.5 125.9 27.3

31.5 125.9 27.3

27.3 115.2 44.5 40.1 4.5 18.3 222.6 446.0 206.2 68.4 94.2 92.6 43.9 43.9 20.0

27.3 133.5 44.5 40.1 4.5

117.6 114.7 55.6 55.6 20.0

27.7 13.5 41.2 105.3 13.7 39.7 158.6 28.3 7.1 35.5 127.0 51.0 45.9 5.1 26.0 254.9 498.1 235.7 106.5 103.2 48.5 48.5 20.0

27.2 23.1 50.2 104.6 12.6 39.1 156.2 27.3 27.3 133.5 44.5 40.1 4.5 222.6 483.7 227.1 68.4 103.8 106.6 48.7 48.7 20.0

222.6 497.9 257.6 68.4 94.2 104.8 43.9 43.9 20.0

*

GLNA, JET and DSL imports set to zero.

conceptual phase were in fact executed (0% deficit), the throughputs for the overall units in 2020 indicate overall capacity

refitting for the sets of units to be expanded or installed to supply the market needs as shown in Table 11. In the 0% deficit cases in 4361



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Table 12. Economic and Model Data for the 2020 Scenariosa 2009−2012 trends GLNC profit sales crude costs import costs operational costs HT costs profit/crude US$/m3 US$/barrel no. of equations no. of variables no. of nonzero elements no. of nonlinear elements CPU (s) CONOPT IPOPT SNOPT profit (local solution) IPOPT SNOPT a

4.2% p.a. GLNCETH

GLNC

GLNCETH

−30%

0%

−30%

0%

−30%

0%

−30%

0%

0%b

−0.588 551.475 297.167 232.004 22.886 0.203

20.383 553.397 400.501 102.329 23.111 0.279

0.412 515.270 297.167 195.906 21.444 0.203

17.540 526.105 390.780 89.391 22.287 0.261

2.488 479.397 295.998 159.483 20.387 0.212

20.051 483.168 379.643 55.744 20.782 0.265

3.286 450.653 295.998 130.814 19.243 0.217

17.161 450.967 349.918 58.533 19.394 0.240

15.216 469.756 390.609 37.989 20.630 0.240

−1.58 −0.25

40.36 6.42

1.11 0.18

35.12 5.60

6.69 1.06 466 532 2115 1276

41.45 6.59

8.83 1.40

38.47 6.12

30.56 4.86

0.951 0.874 0.156

0.655 1.467 0.219

0.780 1.139 0.141

0.670 0.639 0.125

0.796 1.045 0.109

0.530 0.983 0.218

0.717 0.655 0.171

1.248 2.386 0.172

0.936 1.700 0.094

12.844

13.993

18.081

16.828

18.864 2.421

Values are given in millions of U.S. dollars per day unless otherwise noted. bGLNA, JET and DSL imports set to zero.

real is permitted fuel imports. It is only labeled like this to contrast with the −30% deficit if the projects in conceptual phase were not executed. The scenarios in 2020 in which the projects in the conceptual phase were not executed lead to significant fuel import amounts, resulting in unacceptably high expenditures, trade deficit, and external dependence at critical levels. For the cases in which the projects were executed, only in the case with lower demands (4.2% and GLNCETH) can the country be free of gasoline, jet fuel, and diesel imports, which is the official overall increase in demand considered for the nation (4.2% p.a.) without the ETH to GLNC market shift. For the cases that cannot count on the investments in the conceptual phase, the crude import remains at 9.8 and 7.6% for the 2009−2012 trends and 4.2% p.a., respectively, even with the presalt crude level at 33%. For the cases with investments, only in the scenario with maximum demands is it necessary to continue to import, but at a slight level (5.8%). In the other three cases, the country becomes free of oil imports. In Table 12 the economic data for the scenarios in 2020 are shown. As expected, all cases with investments are more lucrative, or less unprofitable, because of lower fuel imports. Additionally, to explore the planned capacities for 2020, the GLNA, JET, and DSL imports were set to zero. But the results got feasible only in the minimum demands scenario (4.2% and GLNCETH). Table 11 shows the results for this case in the last column. The unit throughputs got higher upper bounds than its pair with fuel imports free. However, as shown in Table 12, the profit in this case was lower because of the additional crude expenses to feed the CDU overcome the import costs decrease. When the refineries in conceptual project were defined before 2009, the strategic decisions considered were to be free of main fuel imports (GLNA, JET, and DSL) in 2020 and the gasolineethanol market shift was not in mind that time. In this way, the last column in Tables 11 and 12 represent roughly the official scenario in the country used to plan the production scenario for

the overall unit capacities in 2020. In this work, we used an overall 4.2% p.a. for the low fuel demands increase cases, as mentioned in PETROBRAS business plan 2013-2017,1 which in real should have a different distribution throughout the fuels. In the fuels production charts present in the next subsection, the official scenario in 2020 (4.2% p.a. and GLNCETH) without GLNA, JET, and DSL imports are considered. The size of the models is shown in Tables 10 and 12. For the 2016 and 2020 scenarios HCC was added and DC was permitted in the ATR as defined for the RNEST refinery, by considering a new DC receiving ATR (DCA) as the feed with a capacity equal to 24 k m3/d. Finally, considering the cases with projects in conceptual phase executed, the profit obtained ∼15−20 million US$/d demonstrated the gasoline and diesel pricing policy adopted, which is the demanded policy to avoid losses today, can be flexible in the future. It happens because of the lower or zero import amounts in 2020. 5.4. Scenario-Based Fuel Production Charts. Demand, national production, and import amounts are plotted in Figures 9−12 for LPG, GLNC, GLNCETH, JET, and total DSL. In Figure 9, the LPG production declined after 2012 because the model only considers LPG from fuel production, excluding the production from other sources. Hence, the LPG production is higher than presented, and the imports are lower. The short decline for GLNA production in Figure 10 is a consequence of the small production from non-PETROBRAS refineries not being included in the model. For GLNC in both considerations, with and without the ethanol shift, the imports and production refer to GLNA, the pure gasoline without ethanol. As seen in Figure 10, there is a smaller GLNA production in the GLNCETH cases, which is represented by the inferior blue dotted line. The lower retailed gasoline demands decreased the FCC, RFCC and DC throughputs leading to lower refined gasoline (GLNA). As shown in Figure 9, some intermediate cases for imports (4.2% p.a. and GLNCETH) are found due to the lower 4362



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Figure 12. Demand and production amounts for diesel (DSL). Figure 9. Demand and production amounts for LPG.

the figures, only in the total DSL projections may the imports reduce to zero, as illustrated in Figure 12. For retailed gasoline, its import can cease only in the lower demand case (4.2% p.a and GLNCETH). For jet fuel, its import can be reduced to zero in both 4.2% p.a. cases.

6. CONCLUSION AND FURTHER WORK The results for the 2020 scenarios indicate the necessity to reevaluate strategic decisions to supply the fuel market needs in Brazil taking into account the possible variation in future demands. In lower demand cases (4.2% p.a.) with investments in 2020, jet fuel and diesel imports are reduced to zero. For gasoline, the external dependence was eliminated in the investment cases with the projects in the conceptual phase performed with refitting only in the minimum import scenario (4.2% p.a. and GLNCETH). In the other 2020 scenarios with investments, gasoline, jet fuel, and diesel imports are needed. Comparatively, in all cases with jet fuel and diesel imports, jet fuel presents higher relative levels, considering their total demands, which becomes jet fuel supply and contingency plans more critical than diesel. Clearly, in the proposed process design for the refineries in conceptual project were privileged diesel production as well as in the refineries under construction. In all cases of analysis, to avoid a fuel deficit of ∼30%, which means performing the projects in conceptual phase, the overall capacity of the main units needs to be refit. In terms of CDU capacity, in the four cases with projects, the demanded throughputs were ∼30−40 k m3/d lower than the planned overall capacity (536 k m3/d). This suggests that a medium to large refinery is not used. Selecting possible investments in Figure 1, one future project can be cancelled like as the first or second trains in the refinery PREMIUM I, or even the whole project in PREMIUM II or COMPERJ-2. The future perspectives of the capacity expansion for the refining assets in Brazil do not release fuel imports in the short term, and doubts remain regarding the long-term scenario due to the market variation and delays in the planned projects in execution and in the conceptual phase. These, in turn, are leading to project reanalyses in the national oil industry, mainly related to the new fuel market demand and the production increase of the national presalt crude. Besides, the economic logic may lead the country to postpone the investments in downstream (refining and segments linked to it) to invest in higher return projects in upstream (oil exploration and production). To find the best set

Figure 10. Demand and production amounts for GLNC and GLNCETH.

Figure 11. Demand and production amounts for jet fuel (JET).

throughputs in some units, but they are not drawn in the Figures 10−12. In Figure 11, jet fuel production projected only increased due to the inclusion of the HCC unit and not because of kerosene hydrotreaters (KHTs). The kerosene increase from the CDU capacity expansion goes to diesel production. As seen throughout 4363



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Streams

of investments conjugated with import and export equilibrium should be the goal inside any thoughtful investment plan. In terms of future studies in this field, the possibilities include the prediction of national overall capacity per oil-refinery units using process design synthesis in a discrete optimization-based approach and refining and logistics asset debottlenecking per Brazilian region.

■

ASPR = asphaltic residue ATR = atmospheric residue C1C2 = fuel gas (FG) C3C4 = liquid petroleum gas (LPG) CLN = coker light naphtha CLGO = coker light gas oil CHN = coker heavy naphtha CMGO = coker medium gas oil CHGO = coker heavy gas oil DAO = deasphalted oil DO = decanted oil FHD = final heavy diesel FK = final kerosene FLD = final light diesel FN = final naphtha GOST3 = diesel from ST HCCD = hydrocracked diesel HCCK = hydrocracked kerosene HCCN = hydrocracked naphtha HCCO = hydrocracked gas oil HCN = heavy cracked naphtha HD = heavy diesel-cut HNST = heavy naphtha from ST HTCLN = hydrotreated coker light naphtha HTD = hydrotreated diesel HTK = hydrotreated kerosene HTLCN = hydrotreated light cracked naphtha HVGO = heavy vacuum gas oil K = kerosene-cut LCN = light cracked naphtha LCO = light cycle oil LD = light diesel-cut LNST = light naphtha from ST LVGO = light vacuum gas oil N = naphtha-cut REFOR = reformate SW1 = swing-cut 1 SW2 = swing-cut 2 SW3 = swing-cut 3 VR = vacuum residuum

ASSOCIATED CONTENT

S Supporting Information *

Tables listing the crude-oil assay yields, specific gravity, sulfur content, and acidity, product yields and properties for other oilrefinery units, and parameters for RON and MON blending values. This material is available free of charge via the Internet at http://pubs.acs.org.

■

AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. Notes

The authors declare no competing financial interest.

■ ■

ACKNOWLEDGMENTS We thank Ignacio E. Grossmann and Jeffrey D. Kelly for valuable advice and for keeping up with our research for following work. ABBREVIATIONS

Units

CDU = crude distillation unit CLNHT = coker light naphtha hydrotreating DC = delayed coker DCA = delayed coker with atmospheric residue as feed D1HT = diesel hydrotreating (medium severity) D2HT = diesel hydrotreating (high severity) FCC = fluid catalytic cracking HCC = hydrocracking KHT = kerosene hydrotreating LCNHT = light cracked naphtha hydrotreating PDA = propane deasphalting unit REF = reformer RFCC = residue fluid catalytic cracking ST = stabilizer VDU = vacuum distillation unit

Subscripts

c = cuts cr = crude fc = final cuts imp = imports p = products s = streams u = units

Imports

ETH = ethanol GLNimp = gasoline A imported (pure gasoline) JETimp = jet fuel imported LSDimp = light-sulfur diesel imported LPGimp = liquid petroleum gas imported

Parameters

Products

Acr,c = crude assay acidity for each cut ΔHT = specific gravity reduction in hydrotreaters Gcr,c = crude assay specific gravity for each cut pr = prices Refcost = operational cost Scr,c = crude assay sulfur content for each cut tr = taxes rate Ycr,c = crude assay yield for each cut Ys = yield for oil-refinery units other than CDU/VDU

COKE = coke GLN = gasoline C (with ethanol) FG = fuel gas FO = fuel oil H2 = hydrogen HSD = high-sulfur diesel (1800 wppm S) JET = jet fuel LPG = liquid petroleum gas LSD = low-sulfur diesel (10 wppm S) MSD = medium-sulfur diesel (500 wppm S) PQN = petrochemical naphtha UHSD = ultra high-sulfur diesel (3500 wppm S)

Variables

Ac = cut acidity Afc = final cut acidity 4364



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(12) Barsamian, A. Gasoline and Diesel Blending: Technology, Operations, Economics; Refinery Automation Institute, LLC: Morristown, NJ, 2007. (13) National Agency of Petroleum, Natural Gas and Biofuels (ANP). Brazilian Annual Oil Statistic 2012. http://www.anp.gov.br/?pg= 62463&m=&t1=&t2=&t3=&t4=&ar=&ps=&cachebust= 1371479937518 (accessed Nov 30, 2013). (14) Federal Public Ministry (MPF). Diesel and Gasoline Agreement for Sulfur Reduction. http://s.conjur.com.br/dl/acordo_diesel.pdf (accessed Apr 14, 2013). (15) 2012 Worldwide Refining Survey. Oil Gas J. 2013. www.ogj.com/ ogj-survey-downloads.html (accessed Jun 20, 2013). (16) Perrisé, J. B. Oil-Refining Evolution in Brazil. M.D. Thesis, State University of Rio de Janeiro, August 2007. (17) U.S. Energy Information Administration (EIA). Spot Prices for Crude Oil and Petroleum Products. http://www.eia.gov/dnav/pet/ pet_pri_spt_s1_d.htm (accessed Nov 30, 2013). (18) National Agency of Petroleum, Natural Gas and Biofuels (ANP). Price System Survey. http://www.anp.gov.br/preco/prc/Resumo_ Mensal_Index.asp (accessed Nov 30, 2013). (19) Sugar Cane Industry Union (UNICA). Producer Prices Anhydrous Fuel EthanolSão Paulo. http://www.unicadata.com.br/ preco-ao-produtor.php?idMn=42&tipoHistorico=7&acao= visualizar&idTabela=1505&produto= Anhydrous+Fuel+Ethanol&frequencia=Monthly&estado=São+Paulo (accessed Nov 30, 2013). (20) Petróleo Brasileiro (PETROBRAS). Products: Price Composition. http://www.petrobras.com.br/pt/produtos/composicao-deprecos (accessed Nov 30, 2013). (21) Agencia T1. Preço de querosene de aviaçaõ ainda pesa nas passagens aéreas. http://www.agenciat1.com.br/preco-de-querosenede-aviacao-ainda-pesa-nas-passagens-areas (accessed Apr 30, 2013). (22) U.S. Energy Information Administration (EIA). Performance Profiles of Major Energy Producers 2009. http://www.eia.gov/cfapps/ frs/frstables.cfm?tableNumber=28 (accessed June 10, 2013). (23) Brooke, A.; Kendrick, D.; Meeraus, A. GAMSA User’s Guide, release 2.25; The Scientific Press: San Francisco, CA, 1992. (24) Drud, A. CONOPT: A GRG Code for Large Sparse Dynamic Nonlinear Optimization Problems. Math. Program. 1985, 31 (2), 153− 191. (25) Wächter, A.; Biegler, L. T. On the Implementation of an InteriorPoint Filter Line-Search Algorithm for Large-Scale Nonlinear Programming. Math. Program. 2006, 106 (1), 25−57. (26) Gill, P. E.; Murray, W.; Saunders, M. SNOPT: An SQP Algorithm for Large-Scale Constrained Optimization. SIAM J. Optim. 2002, 12 (4), 979−1006. (27) O Globo. Economy: Brazilian Center of Infrastructure Data. http://oglobo.globo.com/economia/petrobras-acumula-perda-de-22bi-com-venda-de-combustiveis-diz-cbie-9041250 (accessed Nov 30, 2013).

AROs = aromatic concentration of stream s (fixed for some units) AROV = volume-based aromatic concentration AROVQ = quadratic volume-based aromatic concentration Gc = cut specific gravity Gfc = final cut specific gravity GHT = hydrotreater feed specific gravity (also for units) Js = sensitivity of stream s (fixed for some units) JV = volume-based sensitivity MONs = motor octane number of stream s (fixed for some units) MONV = volume-based motor octane number MONVV = motor octane number blending value OLEs = olefinic concentration of stream s (fixed for some units) OLEV = volume-based olefinic concentration RONs = research octane number of stream s (fixed for some units) RONV = volume-based research octane number RONVV = research octane number blending value Sc = cut sulfur content Sfc = final cut sulfur content Qcr,CDU = crude-oil flow to CDU Qc,fc = cut to final cut flow Qs,u = transfer stream s to upstream unit u (up) Qu,s = transfer stream s from unit u to other downstream unit (do) QFu = unit throughput QSu,s = output stream (product) from unit u SHT = hydrotreater feed sulfur content (also for units) SHT,s = hydrotreater output s (also for units) υcr = crude diet ΥHT = severity in hydrotreaters (not for KHT)

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REFERENCES

(1) Petróleo Brasileiro PETROBRAS. Business Plan 2013−2017; Rio de Janeiro, March 2013. (2) Brooks, R. W.; Van Walsem, F. D.; Drury, J. Choosing Cut-Points To Optimize Product Yields. Hydrocarbon Process. 1999, 78 (11), 53− 60. (3) DiVita, V. B. White Paper on the Development of the Liquids Fuels Market Module (LFMM); Jacobs Consultancy: Houston, TX, 2009. (4) Zhang, J.; Zhu, X. X.; Towler, G. P. A Level-by-Level Debottlenecking Approach in Refinery Operation. Ind. Eng. Chem. Res. 2001, 40, 1528−1540. (5) Li, W.; Hui, C.; Li, A. Integrating CDU, FCC and Blending Models into Refinery Planning. Comput. Chem. Eng. 2005, 29, 2010−2028. (6) Watkins, R. N. Petroleum Refinery Distillation, 2nd ed.; Gulf Publishing Co.: Houton, TX, 1979. (7) Menezes, B. C.; Kelly, J. D.; Grossmann, I. E. Improved Swing-Cut Modeling for Planning and Scheduling of Oil-Refinery Process. Ind. Eng. Chem. Res. 2013, 52, 18324−18333. (8) Moro, L. F. L.; Zanin, A. C.; Pinto, J. M. A Planning Model for Refinery Diesel Production. Comput. Chem. Eng. 1998, 22 (1), 1039− 1042. (9) Pinto, J. M.; Joly, M.; Moro, L. F. L. Planning and Scheduling Models for Refinery Operations. Comput. Chem. Eng. 2000, 24 (9−10), 2259−2276. (10) Neiro, S. M. S.; Pinto, J. M. A General Modeling Framework for the Operational Planning the Petroleum Supply Chain. Comput. Chem. Eng. 2004, 28, 871−896. (11) Refinery Technology Petroleum Chemicals Division. The Ethyl Technique of Octane Prediction; Ethyl Corp.: Richmond, VA, October 1981.

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NOTE ADDED AFTER ASAP PUBLICATION This paper was originally published ASAP on March 6, 2014. Additional text corrections were submitted by the author, and the corrected version was reposted on March 19, 2014.

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Nonlinear Production Planning of Oil-Refinery Units for the Future

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