refinery revenue optimization

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Project Report

On

REFINERY REVENUE OPTIMIZATION

SUBMITTED IN PARTIAL FULFILLMENT FOR THEAWARD OF THE DEGREE OF MBA

Submitted by

Sunil Pillai

Institute of Business Management & ResearchAhmedabad

May-June 2010


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RefineryRevenueOptimization

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Project Report

On

Refinery Revenue Optimization

Project Guide:

Dharmendra Mehta

DGMEconomic Planning & Scheduling

Submitted by

Sunil Pillai

Institute of Business Management & Research

Ahmedabad

May-June 2010


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29th May 2010

CERTIFICATE

This is to certify that Mr. Pillai Sunil Sasidharan has satisfactorily

completed the project at Essar Oil limited, Vadinar during the period

of May June 2010.

His Project was onRefinery Revenue optimization

Project Guide

Dharmendra Mehta

DGMEconomic Planning & Scheduling


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Acknowledgements

Before we get into the thick of the thing, let me pen down a few words for all those who

made this possible.

Above everything, am grateful to GOD for leading the way at all steps and enabling me to

complete this Project well in time and with desired quality.

I would like to begin with Mr. C Manoharan, HOR-Vadinar Refinery for providing me an

opportunity to do my internship at Essar and add value to its operations. I am thankful

to Mr. Milind Hiwale, JGM-HR for facilitating the process. It was truly a learning

experience.

I am grateful to Mr. Dhaval Doshi, head-EPS for assigning challenging tasks and setting

the bar high. Sincere Regards to Mr. Dharmendra Mehta, my Project Guide for the

quality time he devoted to the project. His in-depth analysis has played an important role

in the improvement of the project.

Kudos to Karishma Mam, our SIP Coordinator for skillfully managing the entire

Internship process and being a guide at most crucial stages.

Bhargav Chandarana deserves mention for explaining the diesel blending process and

laying the foundation for development of models. His analysis of the model went a longway in making the model bug-free.

Next in line would be Ms. Muthulaxmi Balasubramanium for lets say the infrastructure

support, for being always available to queries, for encouraging experimenting. Her

insights have been very useful in making the report

Mohit sirs contribution in the MS Model cannot go unmentioned. Right from explaining

the process to finally obtaining feasible solution, his significant presence was

everywhere. I am thankful to him for all the help extended to this project.

Last but not the least; I would like to express my gratitude to Mr. Anand Pachouri andMr. Vikrant of Nalanda Knowledge Center and all the members of Essar family who

directly or indirectly made my stay at Essar, a smooth and pleasant experience.


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Tableof

Contents

Acknowledgements........................................................................................................................................... 4ExecutiveSummary........................................................................................................................................... 71. AboutESSAR.............................................................................................................................................. 81.1. ENERGYBUSINESS............................................................................................................................... 9

2. Objective.................................................................................................................................................. 122.1. RefineryEconomics........................................................................................................................... 122.2. Optimisation...................................................................................................................................... 122.3. ProductOptimizationModels........................................................................................................... 132.4. Solver................................................................................................................................................. 142.5. ModelOverview................................................................................................................................ 15

3. LiteratureSurvey..................................................................................................................................... 163.1. RefineryEconomics........................................................................................................................... 163.2. LinearProgramming.......................................................................................................................... 173.3. WhatsBest........................................................................................................................................ 183.4. Excel2010Solver............................................................................................................................... 193.5. MicrosoftSolverFoundation............................................................................................................. 19

4. HSDBlendingModel................................................................................................................................ 224.1. Objective............................................................................................................................................ 224.2. Units................................................................................................................................................... 224.3. Products............................................................................................................................................. 26OtherProducts........................................................................................................................................ 28

4.4. Inputs................................................................................................................................................. 294.5.

Outputs

..............................................................................................................................................

29

Revenue................................................................................................................................................... 29


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5. MSBlendingModel................................................................................................................................. 305.1. Objective............................................................................................................................................ 305.2.

Units

...................................................................................................................................................

30

5.3. Products............................................................................................................................................. 32OtherProducts........................................................................................................................................ 33

5.4. Inventory........................................................................................................................................... 345.5. Inputs................................................................................................................................................. 345.6. Outputs.............................................................................................................................................. 34

6. IntegratedHSDMSBlending................................................................................................................... 356.1. MaterialBalance................................................................................................................................ 356.2. HydrogenAccounting........................................................................................................................ 35

7. TankCorrectionModel............................................................................................................................ 378. Applications............................................................................................................................................. 388.1. MonthlyPlan..................................................................................................................................... 388.2. QualityTargets.................................................................................................................................. 388.3. StreamDeviationandQualityGiveawayReports&Targets............................................................. 38

9. Analysis.................................................................................................................................................... 399.1. IntermediateProperties.................................................................................................................... 399.2. FinalProductProperties.................................................................................................................... 39

10. Assumptions........................................................................................................................................ 4010.1. Prices................................................................................................................................................. 4010.2. Yields.................................................................................................................................................. 4010.3. IntermediateProperties.................................................................................................................... 40

11. Limitations........................................................................................................................................... 4112. GLOSSARY............................................................................................................................................ 4213. Bibilography......................................................................................................................................... 43


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ExecutiveSummaryThe overall economics or viability of a refinery depends on the interaction of three

key elements:

the choice of crude oil used (crude slates), the complexity of the refining equipment (refinery configuration) and the desired type and quality of products produced (product slate).Accordingly the refinery planning problem can also be decomposed into three sub

problems. Given the scale of situation, we focus on the product blending area. This wholeprocess is usually described by massive amount of operational data and decision-makingprocesses. These situations call for detailed planning over a specific period of time -

typically one year. Often the production plans are further broken down into feasibleoperations throughout time with detailed schedule of each activity and event in therefining operations. The scheduling horizons span from a few days to weeks, dependingon information availability and uncertainty, and decisions are taken on hourly basis.

The problem being addressed here in is the monthly planning of product blending.The objective is to obtain optimum production slate while maximizing the revenue. Thecrude selection is treated as an independent optimization area and hence the input costsare considered to be optimized & locally constant. The operating cost of the refinery is aninsignificant fraction of the total costs. Hence revenue from product sales can beconsidered as a basis for optimizing production.

This project envisages the development and implementation of four models viz the

HSD Model, MS Model, Integrated HSD-MS Model and the Tank Correction Model. Thefirst two models are centered on HSD blending & MS Blending process and their fallouts.

The Integrated model takes care of Hydrogen Accounting while providing an optimizationover both the process. The tank correction model comes as a handy tool when some ofthe target parameters are not met and the failed tank has to be corrected.

The solver required for solving the models is an equally important part of theproject. Due analysis has been done for the solver capability required and currentlyavailable. The model can readily utilize the services of whats Best & Excel Solver. Inaddition Microsoft Solver Foundation can be called upon to aid in transferring the modelto other optimization modeling languages which can then by accessed by a large numberof solver suites.

The models with over 130 variables and 220 constraints span over six units, 20+intermediates and ten products. The models are logically arranged in a most user-friendly layout equipped with multi solver support. Fully documented and worked out ina easy to understand way, the models will facilitate the work flow at EPS.


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1. AboutESSAR The Essar Group is a multinational conglomerate and a leading player in the

sectors of Steel, Energy, Power, Communications, Shipping Ports & Logistics,

Construction, and Mining & Minerals. With operations in more than 20 countries

across five continents, the Group employs 60,000 people, with revenues of about USD

15 billion in FY08-09.

Essar began as a construction company in 1969 and diversified into

manufacturing, services and retail. Over the last decade, it has grown through

strategic global acquisitions and partnerships, or through Greenfield and Brownfield

development projects, capturing new markets and discovering new raw material

sources.

Today, the Group continues to expand its global footprint, focusing on markets in

Asia, Africa, Europe, the Americas and Australia. Essar invests significantly in the

latest technology to drive forward and backward integration in its businesses, and on

leveraging synergies between these businesses. It also focuses on in-house research

and innovation to be a low-cost manufacturer with high quality products and

innovative customer offerings.

Alongside its ambitious business pursuits, Essar has been committed to its social

responsibility. The Group runs community outreach initiatives in all its plant

locations, with a focus on education, healthcare, environmental and agricultural

development, and self-employment. Essar is committed to sustainable businesspractices. Our Health, Safety and Environment (HSE) management system is on par

with global standards. We are also taking climate change initiatives to reduce our

carbon footprint. This includes several CDM (Clean Development Mechanism) projects

that can earn the company Certified Emission Reduction (CER) credits. A growing

number of our businesseswith new businesses joining the list every yearare

certified to international environment standards, like ISO 9001 / 14001, and health

and safety standards, like OHSAS 18001.

The Essar Group is widely regarded as a responsible and conscientious global

employer. It has experience in managing businesses in different geographies with aculturally diverse workforce. This is why its people practices are sensitive to cross-

cultural nuances. The Groups people strategy is focused on promoting a learning

culture that continually enhances the professional skills of its employees.

Essar is into the following Businesses:


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Steel Business Energy Business Power Business Communication Business Shipping, Ports & Logistics Business Projects Business Mining, Metals & Minerals Business

1.1.ENERGYBUSINESS A fully integrated oil & gas company of international scale with strong presence

across the hydrocarbon value chain from exploration & production to oil retail.

Global portfolio of onshore and offshore oil & gas blocks, with about 70,000 sq kmavailable for exploration.

Over 280,000 bpd (barrels per day) of crude refining capacity that is being expandedto 680,000 bpd, with a goal to reach a global refining capacity of 1 million bpd.

50 percent stake in Kenya Petroleum Refineries Ltd which operates a refinery inMombasa, Kenya, with a capacity of 80,000 bpd.

Over 1,200 Essar branded oil retail outlets in various parts of India, with plans toopen over 3,000 outlets countrywide

CURRENT OPERATIONS

Exploration & Production

Asia, Africa & Australia: Diverse portfolio of offshore and onshore Oil & Gasblocks as well as Coal Bed Methane blocks Ratna and R-series fields, with reserves of 161 million barrels of oil equivalent,

near the Mumbai High field in the Mumbai offshore basin


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50 percent interest in 1 shallow water offshore exploration block MB-OSN-2005/3, near the Mumbai High field in the Mumbai offshore basin

70 percent operating interest in Mehsana oil and gas block that has startedcrude production

100 percent interest in 1 CBM block in Durgapur, West Bengal 100 percent interest in 2 exploration blocks in Assam 100 percent interest in 1 exploratory block in Nigeria shallow offshore 100 percent interest in 2 exploratory blocks in Madagascar 100 percent rights in 2 exploration blocks in Northern Territory, Australia

offshore

49.5 percent interest in 1 on-land exploration South East Tungkal block inIndonesia

Refining:

Vadinar, Gujarat, India: World-class 10.5 million ton or 210,000 bpdrefinery (operating at 280,000 bpd) producing fuels compliant with latestemission standards; being progressively expanded to 16 million tons(320,000 bpd) and to 34 million tons (680,000 bpd).

Major units:

Crude Distillation Unit: Current potential capacity of 14 million tons. Vacuum Distillation Unit: Current potential capacity of 7.2 million tons. Vis Breaker Unit: Current potential capacity of 2.3 million tons (Axens,

France).

Continuous Catalytic Regenerator: Current potential capacity of 1 milliontons (Axens, France)

Fluid Catalytic Cracking Unit: Current potential capacity of 3.5 million tons(Stone & Webster)

Diesel Hydro Desulphurisation unit: Current potential capacity of 4.5million tons (Axens, France)


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Naphtha Hydro Treater: Current potential capacity of 1.7 million tons Dedicated infrastructure includes a captive power plant (currently 125 MW,

being expanded to 950 MW), dispatch facilities by rail, road, sea and

pipeline, associated tankages, pipelines, water intake facilities, and a Single

Buoy Mooring system, which can accommodate Very Large Crude Carriers,

to receive crude

Mombassa, Kenya: 50 percent stake in a 80,000 bpd refinery run by the KenyaPetroleum Refineries Ltd (KPRL); the remaining 50 percent is owned by the Kenyangovernment

Retailing

Across India:

Marketing network of over 1,200 operational retail outlets, with plans to reach3,000 outlets countrywide

Tie-ups with other oil marketing companies that gives Essar Oil access to productand right to use their terminals and facilities for placing and marketing our products.

This gives the company pan-India presence with more than 30 supply locations.

UNDER EXECUTION

Vadinar, Gujarat, India: Expansion of existing refinery to 16 million tons through the addition of a

Delayed Coker Unit.

Expansion to 34 million tons through the addition of a new Crude DistillationUnit and associated secondary units.

Additional Single Buoy Mooring system to handle crude. New product jetty. Petrochemicals complex that will integrate with the refinery.


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2.Objective2.1. RefineryEconomics

Modeling the activities of petroleum refineries with regard to altering the mix of refinedpetroleum product output has proven to be a most elusive proposition. The structuraland data problems inherent in such analysis are difficult to resolve. It is imperative,however, that a framework be developed to enable such modeling efforts because of theoverall importance of petroleum in a balanced energy network.

Refinery operation is characterized by a phenomenon of being able to alter the relativeamounts of output of refined petroleum products (e.g. motor gasoline, distillate fuel oil,residual fuel oil, kerosene, propane, and kerojet fuel) given a barrel of crude oil input,

within technical limitations. There exists the possibility of output substitution in the

refining of crude petroleum.

It should be observed that the design of any specific refinery will depend upon, amongother things, the specific characteristics of the crude oil to be processed and the outputmix desired, within limits. There are different grades of crude oil and, as a result, there

will be differences in the yield obtained from them.

2.2.Optimisation The use of mathematical programming (MP) in detailed planning in the petroleumrefining industry spans well over half a century. However, usage prior to the invention ofthe electronic digital computer was limited to small problems that could be solved byhand. Dantzigs invention and development of the simplex algorithm in 1947 reallycreated the area of linear programming (LP) and blending of gasoline turned out to be themost popular application in the petroleum industry. Later, usage of LP was extended to agrassroots design and configuration selection, capital investment analysis, long-rangeoperations planning, supply and distribution planning. The limitations to progress arethe matrix size capacity of the computer or the time required to get a solution or bothand the accuracy of LP results which depend on the validity of input data. Refineryplanners have therefore solved complex refinery-planning problems by decomposing intosub problems and, to date, this is reflected in the way some refineries operate itsplanning, central engineering, upstream operations, refining, supply and transportation.

In most cases, the refinery-planning problem is decomposed into three sub problems -crude supply, refining and blending, and product distribution.

The primary goal of SCM is to maximize profit by integrated management of material andtransactional flows within a business and to customer and partner companies. Thesupply chain of a typical petroleum refining company involves a wide spectrum of


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activities, starting from crude purchase and crude transportation to refineries, refiningoperations, product transportation and finally delivering the product to the end user. Thenature of the value chain is such that its economics are extremely complex and heavily

linked.

For example, the process of selecting the right crude oil is linked not only to thetransportation costs involved in delivering it to the refinery, but it must take intoconsideration the need to balance the dynamic behavior of the whole refinery network indifferent scenarios, the capabilities and constraints in converting the crude intoproducts, stochastic nature of prices, increasingly restrictive environmental legislation as

well as the product volume.

This whole process is usually described by massive amount of operational data anddecision-making processes. These situations call for detailed planning over a specificperiod of time - typically one year. Often the production plans are further broken downinto feasible operations throughout time with detailed schedule of each activity and eventin the refining operations. The scheduling horizons span from a few days to weeks,depending on information availability and uncertainty, and decisions are taken on hourlybasis.

2.3.ProductOptimizationModelsThe objective of this exercise is to determine optimum Refinery Production scenario so as

to maximize revenue.

The underlying problem could be captured as

Given

A set of Refining Units A set of unit outputs with their yields A set of final products A set of possible allocation for unit output streams A set of product prices Product Demands and Product Specifications

Determine

The optimum component allocation scenario Amount of final products that should be made Production slates for each product Revenue from all final product


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Throughputs of processing units Inventory levels of each Components

In order to

Maximise the total revenue made during the planning horizon.Subject to

Product specification Constraints Component availability Constraints Unit Capacity Constraints Product Demand Constraints and Refining process constraints

The development of four models was undertaken to meet the specific requirements of the

department

HSD Blending MS Blending Combined HSD-MS Model Tank Correction Model

These models employ the concepts of Linear Programming, Non-Linear Programming and

Quadratic Programming

2.4.SolverThe Models were originally designed using Excel 2010s solver. The HSD model has been

completely linearized. Hence it can use the simplex algorithm of Excel 2010s solver. The

other 3 models have non-linearities present hence need the services of a non-linear

solver. The models can be run on Excel 2007s solver without any modifications. In

addition the services of WHATs BEST, a solver from LINDO systems is available. All the

models have been modified to run on both the solvers- WB & Excel.

Study of Microsoft Solver Foundation has also been undertaken. The models can be

transported to MSF, if and when required. MSF allows the model to be transported to

other mathematical programming languages like MPS, QPS & OML. The model can also

be deployed using C#. Once the model is available in these languages, it can be accessed

& solved by all the solvers that understand these languages.


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2.5.ModelOverviewAll the models operate in Microsoft Excel. They basically have two sheets: Processing &

Interface.

The Interface sheet is to be used to supply data values to the model. It also displays all

the outputs generated by the model. The processing sheet fetches values from the

Interface sheet. It does all the calculations. The solvers are binded to this sheet. After

processing, the processing sheet passes on all the required values to Interface sheet to be

displayed in an easy to visualize manner. The processing sheet can be referred to drill

down into the model.

FullyDocumentedAlmost all the cells have relevant meaningful cell names. All formulas use Cell names

instead of cell addresses. This makes the model self-explanatory & aids easyunderstanding without having to refer to voluminous documentation.

UserFriendlyThe Model has been arranged in most effective manner. All the objectives are highlighted

at the top of the sheet effectively summarizing the model. The Model has been

represented in a graphical way providing easy visualization of the situation. The model

also makes optimum utilization of sheet space. Laid out in a compact format, the model

envisages minimum scrolling effort. The units & products have been separated and

displayed apart to provide clarity.

ActiveXControlsThe model solving part has also been automated. The model uses ActiveX controls to doall the excel solver jobs. Clicking on optimize button will set up the solver, load the

constraints, solve the problem & come back to active sheet.

Multiplesolversupport The same file can be used with both whats best & Excel Solver. All the necessary

modifications have been built into the model. The original file though made with Excel

2010 can be used with any version of Excel or other spreadsheet programs with minimal

changes.


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3. LiteratureSurvey3.1.

Refinery

Economics

The overall economics or viability of a refinery depends on the interaction of three

key elements:

the choice of crude oil used (crude slates), the complexity of the refining equipment (refinery configuration) and the desired type and quality of products produced (product slate).Refinery utilization rates and environmental considerations also influence refinery

economics.

Using more expensive crude oil (lighter, sweeter) requires less refinery upgrading

but supplies of light, sweet crude oil are decreasing and the differential between

heavier and sourer crudes is increasing. Using cheaper heavier crude oil means more

investment in upgrading processes. Costs and payback periods for refinery processing

units must be weighed against anticipated crude oil costs and the projected

differential between light and heavy crude oil prices.

Crude slates and refinery configurations must take into account the type of

products that will ultimately be needed in the marketplace. The quality specifications

of the final products are also increasingly important as environmental requirementsbecome more stringent.

ProductSlatesRefinery configuration is also influenced by the product demand in each region.

Refineries produce a wide range of products including: propane, butane,

petrochemical feedstock, gasolines (naphtha specialties, aviation gasoline, motor

gasoline), distillates (jet fuels, diesel, stove oil, kerosene, furnace oil), heavy fuel oil,

lubricating oils, waxes, asphalt and still gas.

Light crude, for example, will yield a high percentage of useful products by

distillation, whereas heavy crude will produce significant amounts of crude-oil

material that must be sold as heavy fuel oil or else processed further. As a result,

there is some flexibility in switching a refinery to alternative crude streams, although

this is limited [4].


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In the refining process, some of the available crudes must be refined because of

long-term minimum volume commitments or because of requirements for specialty

products. These crudes are considered fixed, and yield constant motor gasoline and

distillate fuel oil volumes. From the remaining crudes, and from those crudes which

are available in volumes greater than their minimum volume commitment, are

selected those which can supply the required refined products most economically.

These are nominally referred to as the incremental crudes.

3.2.LinearProgrammingConsiderable research work has been done in the modelling of each of sub-

system. In the upstream and crude supply operations, Iyer et. al. (1998) develop a

multi-period mixed integer linear programming (MILP) problem for planning and

scheduling of offshore oil field infrastructure investments and operations. Van der

Heever and Grossmann (2000) present a nonlinear model for oil field infrastructurethat involves design and planning decisions while, in another study, the same

authors and others focus on business rules (Van der Heever, 2000). Meister, Clark

and Shah (1996) develop a model which allows the optimal selection of information-

gathering processes during the oil exploration phase and, at the same time, permits

the calculation of optimal operating policies. In other studies, Lee et. al. (1996) report

a detailed MILP formulation comprising of oil tankers, berths, storage tanks,

substations and refineries whereas Maglhaes and Shah (2003) focus on the crude oil

supply scheduling. Pinto et al. (2002) address the problem of crude oil supply and

unloading, transfer to storage tanks, charging schedules for each CDU and inventory

management of the tanks while Mas and Pinto (2003) go further to decomposesupply subsystem into subproblems.

Park et. al. (2002) reports a MILP formulation of actual refinery scheduling

consisting of crude oil purchase, shipping by oil tankers, unloading to storage tanks,

CDU and product tank operations with due consideration to environmental pollution.

Recently, Kosmidis et. al. (2005) present a novel mixed integer nonlinear (MINLP)

model for the daily well scheduling in the petroleum fields, the applicability of which

gave up to 10% increase in oil production compared to heuristic rules widely applied

in practice.

In the area of refining and blending sub-system, Moro, Zanin and Pinto (1998)

develop a real-world application for the planning of diesel production in Sao Paulo

which represents an increase in profitability of about $6,000,000/year. Pinto, Joly

and Moro (2002) focus on production scheduling for several specific areas in a

refinery such as crude oil, fuel oil, asphalt and LPG. Okeke and Osakwe-Akofe (2003)


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model a group of CDUs as nonlinear programming problem (NLP) with the aim of

obtaining increased gasoline yield in the context of reducing energy requirement.

Ponnambalam, Vannelli, and Woo (1992) develop an approach that combines simplex

method for LP with an interior point method (IPM) for solving a linear multi-period

planning model in an oil refinery. Jia and Ierapetritou (2003) propose a realistic large-

scale continuous-time formulation for the short-term scheduling of gasoline blending

and distribution system.

In the distribution sub-system, Van der Bruggen et. al. (1995) present a hierarchal

approach for the operations related to the delivery of gasoline and diesel oil between

depots and clients while Persson and Gothe-Lundgren (2005) suggest an optimisation

model and a solution method for a shipment planning problem. In his thesis, Kogal

(2002) presents two different approaches for pipeline models one of which was a

general MILP formulation. In a more recent report, Rejowski and Pinto (2003) present

a model composed of petroleum refinery, one multi-product pipeline and several

depots that are connected to local consumer markets.3.3.WhatsBest

Whats Best is an add-in to Excel that allows you to build large scale optimization

models in a free form layout within a spreadsheet. Whats Best combines the power of

Linear, Non-Linear, Quadratic, Stochastic & Integer Optimization with Microsoft

Excel.

Key Benefits

The worlds Most powerful Solver for Microsoft ExcelIt will efficiently solve your biggest, toughest models. The linear, integer, nonlinear

and global solvers have been designed for large scale commercial use and field tested

on real world models.

Modeling is Fast & EasyModeling is straight forward. Most users are able to begin modeling within

minutes of installation

.

Build Models for your clientsIt is an ideal tool for creating optimization applications for use by others. It allows

you to provide application in a form that is best suited to the user.

Extensive documentation & Help


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User manual fully describes the commands & features of the program. Also

included in the manual is discussion of major classes of linear, integer and nonlinear

optimization problems along with over two dozen real world based examples that you

can modify and expand.

3.4.Excel2010SolverExcel 2010 includes a new version of the Solver add-in, which you can use to find

optimal solutions in what-if analysis. Solver has an improved user interface, a new

Evolutionary Solver, based on genetic algorithms, that handles models with any Excel

functions, new global optimization options, better linear programming and nonlinear

optimization methods, and new linearity and feasibility reports. In addition, the

Solver add-in is now available in a 64-bit version

Excel 2010 Solver ships with three solvers. Select Simplex Solver for solving linear

problems. The GRG Solver can be used with smooth Non-linear problems. The

Evolutionary solver can manage the non-smooth problems.

If Simplex Solver is used, it will result in a global solution. The GRG solver

ensures that the result is at least locally optimal.

3.5.MicrosoftSolverFoundationSolver Foundation is a pure, managed code runtime for mathematical

programming, modeling, and optimization. This .NET/CLR based framework provides

a rich set of tools, services, and engines to aid companies in their continuous questfor operational efficiency, profit maximization, and risk management. Solver

Foundation is designed to help businesses make strategic decisions. It affords its

users the advantage of easy to use tools, numerically stable solver technologies and

deep integration with ubiquitous information worker systems like Microsoft Office.

Solver Foundation uses a declarative programming model, consisting of simple

compatible elements that are solved by application of solvers, meta-heuristics, search

techniques, and combinatorial optimization mechanisms to accelerate the solution

finding process. Building a model in Solver Foundation is as simple as specifying the

decisions to be made, constraints to be respected, the goals to be used to evaluate

candidate proposals (solutions) and the data to be processed by the model (historical

or projected parameters). This can be done from any CLS-compatible language and

the modeler does not need to understand anything about the details of solver

technologies or search strategies. The separation of concerns is total, providing a high

degree of modularity.


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Solver Foundation consists of a series of layered services that efficiently divide the

work of modeling and solving. The following system diagram describes the extensible

architecture.

Key features in Solver Foundation include:

Solver Foundation Services (SFS). Essentially a runtime API that augments thecapabilities of individual solvers with framework-level model analysis and meta-

heuristics. In short, we abstract the complexities of optimization so users can focus

on modeling and solving their decision problems. Key features in this area:

o Easy data binding. SFS allows easy LINQ data binding of model parameters anddelivery of results in multiple formats.

o Solver/Capability Selection. SFS will automatically analyze models anddetermine which solver is most appropriate. Advanced Modelers can optionally

choose specific solvers, and solver attributes.

o


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o Transparent Parallelism. SFS will manage all threading, synchronization, andscheduling around the solvers and model execution.

o Solver Plug-in Capability. Solver Foundation allows new or existing 3rd partysolvers to plug into the SFS directly, avoiding the need to learn a new modeling

language or the significant overhead in managing solver specific solutions. We

included a collection of reference and certified partner wrappers, and included the

Gurobi MIP solver to handle all default MILP problems.

o Model/Solver Reports and Analysis. SFS can report information about solverbehavior and results, and provide additional information about solutions,

including sensitivity.

o Type safe version of the Optimization Modeling Language (OML). The SFSinterface provides the same facilities as OML, but adds type checking and higher-

level constructs.

Solver Foundation supports all of the common domains that a business modeler and

planner would require including working with models that involve:

Linear Programming (Primal Dual Simplex and Interior Point Techniques) (Mixed and Binary) Integer Programming (From Branch and Bound to Cutting Plane

Techniques)

Finite Domain Programming (From Constraint-Logic Programming to Adaptive LocalSearch Algorithms)

Nonlinear Programming (From Convex (Quadratic) Programming to Non-ConvexTechniques)

Stochastic Programming (What if?-style Techniques with Markov Decision Proceduresand Stochastic Variables)

Dynamic Programming (Deterministic and Nondeterministic Techniques)


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4.HSDBlendingModel4.1.

Objective

To maximize Product Revenue Optimum Production Slates of HSD, FO, IFO, Bitumen Optimum Unit Throughputs

Diesel can be produced using a variety of components. These components have

alternative uses. The final product is subject to stringent quality specifications. We have

to decide optimum mix of components so as to meet the product specifications &

maximize the revenue.

Now to maximize the revenue, all the alternative uses of feed components have to be

considered.

The single input of raw material to the model is at Crude Input to CDU. The model is

divided into two sections: Units & Products.

4.2.UnitsCrudeDistillationUnit(CDU)

The main input to the refinery is Crude Oil. The Crude Oil is sent to Crude Distillation

Unit (CDU). The CDU gives different products by fractional Distillation process. Some of

the CDU products are used to drive the Vis-Breaker Unit (VBU). Some of the products of

VBU & CDU are used as feed stock to Fluid Catalytic Cracker (FCC).

The Output streams of the CDU are :: Gas LPG SN HN LK HK LGO HGO VD VGO HHVGO VR


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These product streams can be used As Final Product for Selling To Blend MS As Feed To DHDS To Blend Diesel As Feed To VBU As Feed To FCC To Blend FO

The Detailed possible Allocations for each of the CDU streams are shown below in theform of table:

As

Product

To

DHDS

To

Blending To VBU To FCC To FO

LPG

HN

LK

HK

LGO

HGO

VD

LVGO

HVGO

HHVGO

VR

The output stream SN (Stabilized Naphtha) is involved in the MS Blending Model & is

detailed there. In addition to these, CDU also emits Gas in small quantities. This gas

is sent to Utility & Power plant for generating steam & electricity

CDU

Crude Oil

Thruput


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VisBreaker

Unit

(VBU)

The inputs to the VBU are the CDU streams HHVGO & VR. The VBU outputs the

following Products

VBVR VBVGO VBAGO VB Naptha VB LPG VB H2S VB GAS

These can be used As Final Product for Selling As Feed To DHDS As Feed To FCC To Blend FO To Blend IFO

The Detailed possible Allocations for each of the VBU streams are shown below in theform of table

As Product To DHDS To FCC To FO To IFO

VBVR

VBVGO

VBAGO

VBNap

VB LPG

VB Gas is send to utility for generating steam and electricity. VB H2S is sent toSulphur Recovery unit for recovering Sulphur.


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FluidCatalyticCracker(FCC)The inputs to the FCC are the CDU streams LVGO and HVGO & VBU stream VBVGO.

The VBU outputs the following Products

Gas LPG Coke LG MG HG LCO Slurry Oil

These can be used As Final Product for Selling As Feed To DHDS To Blend Diesel To Blend FO To Blend Bitumen To Blend MS

The Detailed possible Allocations for each of the FCC streams are shown below in theform of table

As

ProductTo DHDS

To

BlendingTo FO

To

Bitumen

Gas

LPG

Coke

LG

MG

HG

LCO

Slurry Oil

LG & MG are used for MS Blending & are detailed in the MS Blending Model. Coke is

fed as fuel to FCC.


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DieselHydroDesulphurisation(DHDS)DHDS removes the sulphur content out of the feed products by adding H2 and

extracting H2S. The output pool has sulphur of 40ppm, Flash of 60C and KV reducedby 0.2.

DHDS Feed comprises of following streams.

LK

DHD

S

HK

LGO Diesel

HGO Naptha

VD H2S

HGGas

LCO

VBAGO

H2The DHDS produces following Product streams::

Diesel Naphtha. H2S Gas

The Diesel output from DHDS is used for Diesel Blending. Naphtha is directly sold asa product. GAS is sent to CDU for recovering LPG. H2S is routed to Sulphur Recovery

Unit.4.3.Products

DieselBlendingSo finally the Components that can be used to blend Diesel are as Follows

DHDS Diesel H

SD

LK

HKHG

HN


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The Key Quality Parameters for HSD are::

KV min : 2.2CST Sulphur min : 300 ppm, max : 320ppm Flash min : 38C, max : 45C

The values for these parameters are known for the feed Components. The product

parameters can be calculated by taking sum product of the component parameter

values with the volume used of respective components. The Model determines the

optimum volume of each of the feed Components in such a way that the product key

parameters are in the desired range.

FOBlendingThe Following components are used for Fuel Oil Blending

LK

FuelOil

HK

LGO

LCO

LVGO

HHVGO

Slurry Oil

VBVR

VBVGOVBAGO

The Key Quality Parameters for FO are::

Density max: 0.991 KV max: 180 CST Flash min: 66 C

IFOBlendingInternal Fuel Oil is blended fromthe following components


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VBVR Slurry Oil

The Key parameter for IFO is KV which should be equal to 40,800 CST.

BitumenBlendingBitumen is blended from

HHVGO (40%) VR (60%)

Usually a Cap is maintained on the maximum amount of Bitumen to be produced.

OtherProductsNapthaNaptha is sourced from three units:

HN Naptha (CDU stream) DHDS Naptha (DHDS) VB Naptha (VBU)

Naptha is directly sold as a Final Product. It appears directly in the Revenue

Calculation of model.

LiquifiedPetroluemGas(LPG)LPG is sourced from three units: CDU LPG (CDU) FCC LPG (FCC) VB LPG(VBU)

LPG is directly sold as a Final Product. It appears directly in the Revenue Calculation

of model.

SuperiorKeroseneOilThe part of LK which optimizer allocates for selling as a product is directly sold asSKO. Usually there is a cap on maximum quantity of SKO.


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4.4.InputsAs far as the model is considered, crude oil is the only raw material used.

The following data have to be fed to the model

Crude Throughput to CDU The yields of different products (in %). Parameter values for each of the Component streams. The Product Specifications. Capacity Constraints for units. Production Caps for Final Products if any. Prices of Final Products

The model can be run for any time period- monthly, weekly, daily. The only changes

to be made are in the capacity constraints which have to be scaled up or scaled down

as per the required period.

4.5.OutputsThe Model outputs the following data

Optimum allocation of Components Optimum throughputs of the Units

RevenueThe prices of the products have to be entered into the model as an input. For revenue

calculation the output quantities have been converted into the units in which they are

actually traded viz bbls & tons. The total revenue is the sum of revenue from all

product sales including LPG & SKO. The objective of the model is maximizing this

revenue.


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5.MSBlendingModel5.1.Objective To maximize Revenue Optimum Throughputs for NHT & CCR Optimum production slates for MS BS3, MS BS 92, MS Exp

The Input to the MS Blending process is from three streams.

SN (CDU Stream) LG (FCC Stream) MG (FCC Stream)

5.2.UnitsCrudeDistillationUnit

The only stream of CDU that is involved in the MS Blending Process is SN (Stabilised

Naptha). The possible allocation for SN is as follows::

As Product To Blend MS BS3 To Blend MS BS92

To Blend MS Exp As Feed to NHT

NapthaHydroTreater(NHT)The Feed for NHT is obtained from the CDU stream SN. The outputs from NHT are as

below:

Gas LN HN

These can be used

As Product To Blend MS BS3


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To Blend MS BS92 To Blend MS EXP Stored in Inventory As Feed to CCR

The detailed possible allocations for each of the FCC Streams are shown below:

NHT

AsProduct

ForMSBS3

ForMSBS92

ForMSEXP

TOINV

CCRDomestic

CCRExport

OFF Gas

LN

HN

ContinuousCrackingRegenerator(CCR)CCR can be run in two modes:

Domestic Export

It takes NHT HN Stream as Feed Stock. The products of CCR are:

H2 Gas LPG Domestic Reformate (in Domestic Mode) Export Reformate (in Export Mode)

To model this we have two CCR units, one for Domestic Operation & other for Export

Mode Operation.

The cracked products are to be used for each of the following:

To Blend MS BS3 To Blend MS BS92 To Blend MS EXP Stored in Inventory


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FluidCatalyticCracker(FCC)As mentioned in the HSD Blending Model two output streams of the FCC are used in

MS Blending. They are:

LG MG

The cracked products are to be used for each of the following:

To Blend MS BS3 To Blend MS BS92 To Blend MS EXP Stored in Inventory

5.3.ProductsThe main products of the MS Blending Model are:

MS BS3 MS BS92 MS EXP

The products can be blended from each of the following components:

FRN LN NHT HN Domestic Reformate Export Reformate LG MG

The Key quality parameters of the products are:

Density Research Octane Number (RON) Motor Octane Number MON Reid Vapor Pressure RVP Sulphur


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Benzene Olefin Aromatics Recovery @ 70C Recovery @ 100C Mercapatan Vapour Lock Index VLI Anti-knocking Index AKI Reid Vapor Index (RVI)

OtherProductsThe other products involved in the MS Model are:

NapthaThe Naptha is sourced from two sources:

SN Naptha (CDU Stream) NHT LN (NHT Stream)Naptha is directly sold as a product in Tons & reflects in the Revenue calculation of

the model.

GASGas is obtained from the CCR unit. The total gas in the model is from

Off Gas (NHT) Gas (CCR- D) Gas (CCR- E)

The gas is directly sold in Tons. It gets reflected in the Revenue calculation part of the

model.

LPGLPG occurs as a product of CCR. Hence it has two sources in the model

LPG (CCR- D) LPG (CCR- E)


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The LPG is directly sold in tons. It appears in the Revenue calculation part of the

model.

5.4.InventoryMS is produced in batches. This is because the MS parameters RON & MON are

non-linear in nature. They change with the feed composition. So MS cannot be

blended online. Hence batch blending is done for MS. For this, Inventory of the

components has to be made. The MS Blending Model also provides for the

Inventory. The opening Stock can be entered into the model. This is added to the

produced quantity of Components to obtain the net availability of the components.

The Minimum & Maximum Quantities to be maintained in the Inventory can also

be specified. The optimizer will output the optimum inventory level for each of the

component streams. Usually the inventory limits remains constant throughout theyear.

5.5.InputsThe Input to the model comes from the CDU & FCC.

The Following data have to be fed to the model.

CDU & FCC throughput Opening Stock of all Components (By default Zero) Component Yields

Component Parameters Product Specifications Unit Capacities Inventory Limits Production Caps Prices of the Final Products

5.6.Outputs Total Revenue Optimum allocation of the Unit Products Optimum Unit Throughputs Optimum Inventory Level for Each of the Components


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6. IntegratedHSDMSBlendingThe HSD Blending Model & MS Blending Model is combined to provide a higher level

of optimization. All the elements of the both the models are present in the Integrated

Model. In addition it incorporates two new features:

Material Balance Hydrogen Back Calculation6.1.MaterialBalance

Mass is neither destroyed nor created. So what goes in should come out in one

form or another. Material balance helps us account for the total raw material

utilized by the refinery.

It shows the distribution of Mass as Inputs are converted to Outputs. It can be

used to know the amount of final products generated by feeding 1 unit of Crude to

the CDU. In other words, it shows the final products produced per unit of Crude

processed. It shows the allocation of crude to various products. It shows the total

product produced from the crude oil.

It helps to identify the avenue where revenue loss is occurring. It helps to

validate the model & account for all the processes. It also shows the losses &

miscellaneous by-products generated as a part of the process.

6.2.HydrogenAccountingHydrogen is an important Raw Material for Refinery. All over the world, there is

a shift towards low sulphur products. Hydrogen is important for these low sulphur

products. Production cost of hydrogen is very high so it has to be used optimally.Hydrogen is required by DHDS for Sulphur extraction. H2 is produced by the

CCR plant. Excess H2 production is of no use & has to be flared. This reduces the

revenue per barrel of crude processed.

Hence Economics suggests capping CCR throughput as per H2 requirement.

Hydrogen demand depends on the feed components of DHDS hence is dynamic.

This back calculation results in a classic case of circular loop.

1) Fixing CCR according to H2 would mean more / less CCR product availabilityto MS blending.


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2) Which means to maintain MS specifications more/less of LG & MG (FCCproducts) would be required.

3) Pulling more/less of FCC to MS would mean a change in availability to Diesel.4) This would disturb the allocation of intermediates to Diesel Blending.

5) Accordingly DHDS Feed Components would change to maintain Diesel specs.6) Change in DHDS throughput would change H2 demand.

Earlier attempts to solve the problem in micro mode involved use of iterations &

loops leading to a less than perfect solution. We directly provide the hydrogen

situation as a constraint to the optimizer. To meet our objective of maximizing the

revenue, we provide an alternative route to H2 of being flared. Since this flaring

does not fetch any revenue, the optimizer minimizes the H2 flared while not

adversely impacting other units.

Objective

The main objective of the integrated model is to optimize the combined Revenue

from both the Models. It also incorporates all other objectives of both the models.

The product allocation takes into consideration the need to optimize h2

production & routes the intermediates in such a way that the revenue is

maximized.


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7.TankCorrectionModelThe operations execute the plan with utmost adherence. However in practical situation

the planned results are not fully achieved. The variance of +-1% is expected. Hence the

final product parameters achieved in the tank may not meet the desired specifications.

When such a case is verified by the lab results, the tank has to be corrected. Content

from the available tanks are transferred to the correction tank in such a way that the

final content of correction tank meets the product specification.

Objective

Given

A tank to be corrected A set of Available tanks with products A set of Parameters of products in the tank A set of desired parameters

Determine

The volume to be transferred from the available tanks to the correction tank soas to correct it.

In order to

Minimize the Inter-tank transfers

Subject to

Product specifications Constraints Availability Constraints

The model requires the user to input the available volumes in the tanks along with the

parameters of its content. The tank to be corrected is also included with the input

tanks. The desired final parameters required in the correction tank are to be specified.

With this data, the model will determine the minimum volume of products that are to

be transferred from the input tanks to the correction tank.


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8.Applications8.1.MonthlyPlan

Refinery Operations are guided by the Rolling Plan. The Rolling plan is a 4

monthly operational plan generated using PIMS software. The PIMS model utilizes

13000 variables & 12000 Constraints to set directions for Refinery Operations.

Simultaneously, a monthly plan is developed for product optimization. The monthly

plan sees to it the overall objectives of the rolling plan are achieved. The monthly

plan sets directions for the daily plans. The daily plans are implemented by the

operations as per the inputs from EPS. The HSD Blending & MS Blending model

developed as a part of this Exercise are meant to replace the current monthly

models.

The daily plan derives the direction from monthly plan. The daily plan takes

into account the actual situation of the plant, the changes in the crude blends, the

performance of units & any other unexpected deviations. Due to these

considerations the daily plan may not strictly resemble the monthly plan but

overall direction is guided by the monthly plan. Both monthly & daily plan set the

blend ratios for the products albeit for different time periods.

8.2.QualityTargets The model can also be used to back calculate intermediate properties. This

helps to set quality targets for the units. Now we shift the focus from the finalproduct quality to the intermediates properties. Instead of trying to hit the product

parameters with the available intermediates, we try to fix the product parameter &

develop intermediates properties so as to meet the set product specifications.

These Intermediate Quality targets are revised weekly.

8.3.StreamDeviationandQualityGiveawayReports&Targets The model can also be used as a basis for setting unit throughputs. These

targets are set daily and are reflected in the Stream Deviation & Quality giveaway

Target reports. The daily report also tracks the performance over the last 24 hours.

The quality giveaway results in loss of revenue to the refinery.


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9.AnalysisThe economics decide which component to be routed to which product. However these

are limited by the product specifications. The model helps us take several such calls.

Some of the possible decisions are detailed here.

9.1.IntermediatePropertiesRVP: Based on daily LPG-MS Price Differential

A particular component can either be used for LPG Production or routed to MSBlending. The decision of this choice is depended on the LPG-MS Price Differential.

The model routes it to where it realizes more revenue. This price differential is

calculated daily. However this component can be added to MS till the RVP of MS is

hit. So accordingly the RVP min-max is hit.

Sulphur: HSD MS

A particular high sulphur component is used for HSD Blending. It can also be

diverted to MS Blending. However such a moves feasibility depends on the HSD-

MS price differential. Again this component can be dumped into MS only till its

sulphur is hit. Beyond this no more addition is possible although MS fetches morerevenue.

9.2.FinalProductPropertiesThe final product parameters have been given a desired range in which they can

lie. Now whether to hit the extreme ends of the range depends on economics. The

Naphtha MS price differential determines the RON value realized by the model. If

the differential is positive the n Minimum RON is achieved. If the price differential

turns positive more of naphtha is backed out from MS till the Max value of RON is

hit


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10. Assumptions10.1. Prices

Refinery Revenue is calculated on the basis of the final product prices. The

product price used by the plan is not the price at which it is sold. The price

scenario for MS & HSD is highly volatile. As a result the price fetched by the

product may not be same as the one used in plan. This difference is reflected in

Plan vs Actual Delta. The product is usually sold at a price in 5days from the BOL.

This reduces the plan prices to a best approximation of the prevailing prices. So forrevenue optimization the model assumes that the product is sold at plan prices.

10.2. YieldsIntermediate availability for final product blending is calculated on the basis of

the percentage yields. Intermediate Yields used by the plan are a best

approximation of the practical situation. The unit yields are impacted by a variety

of factors. The crude blend changes impact the Intermediate yields. Also the unitperformance and operating parameters influence the intermediate yields.

10.3. IntermediateProperties The properties of Intermediate streams are assumed to be constant. The

properties are influenced by Unit performance, operating conditions, Crude blend

characteristics. The Intermediate properties are not tested every day before being

sent to product blending. So we use the last known property sets.


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11. LimitationsProcessingSpeed

The speed of system used to solve the model would determine the time required to

arrive at a solution. With some typical data sets, the amount of computation required

would be huge. Most solvers have inbuilt max solving time limits. If a solution is not

found in the given time limit, it aborts the attempt. This is to ensure that the system

resources are not tied up with the solver.

SolverThe Solver used will determine the accuracy of the results. The ability of a solver to

find global solutions to non-linear problems will limit the applicability & extent of

usefulness of the model.

DummyPrices.The HSD model uses dummy prices for products which are not directly linked to the

HSD Blending model viz Coke, LG & MG. This will impact the value realized for the

FCC products and hence the FCC throughput. It also doesnot account for SN

revenues, however it will not impact the results as it is a direct output of CDU. But theIntegrated model takes care of that.

ScopeThe model is limited in its scope in the sense it does not attend to all the variables.

The Aspen PIMS model utilizes more than 13000 variables & 12000 Constraints


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12. GLOSSARYCCR Continuous Catalytic ReactorCDU Crude Distillation UnitDHDS Diesel Hydro De Sulphurisation PlantFCC Fluid Catalytic CrackerFO Fuel OilHG Heavy GasolineHGO Heavy Gas OilHHVGO Heavy Heavy Vacuum Gas OilHK Heavy KeroseneHN Heavy NapthaHSD High Speed DieselHVGO Heavy Vacuum Gas OilKV Kinematic ViscosityLCO Light Cycle OilLG Light GasolineLGO Light Gas OilLK Light KeroseneLPG Liquified Petroluem GasLVGO Light Vacuum Gas OilMG Medium Gasoline

MS Motor SpiritNHT Naptha Hydro TreaterRON Research Octane NumberSN Stabilised NapthaVBU Vis Breaker UnitVD Vacuum DistillateVR Vacuum Residue


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13. BibilographyMODELING OIL REFINERY OPERATIONSNOEL D. URIt

Division of Antitrust, Bureau of Economics, Federal Trade Commission,

Washington, DC 20580, U.S.A.(Received 29 May 1984)

INTEGRATING CRUDE SUPPLY, BLENDING AND PRODUCT

DISTRIBUTION MODELS IN REFINERY PLANNING USING AMATHEMATICAL PROGRAMMING LANGUAGE.Adebayo Alabi, Jordi Castro

Department of Statistics and Operations Research

Universitat Polit`ecnica de Catalunya

Jordi Girona 13

08034 Barcelona, Catalonia, Spain

May 21, 2007

refinery revenue optimization

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