Sunil Pillai, SIP

8/9/2019 Sunil Pillai, SIP

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Sunil S Pillai 1 ESSAR OIL LTD, SIP

Executive Summary

Profit optimization is a goal worth pursuing for every business; it becomes more

important in a high investment scenario like a refinery. Fluctuating (More importantly

the Sharp rises in) crude prices have wreaked havoc on the optimum profit & Return on

Investment Calculations of such huge investment projects. Maintaining / controlling theinput costs is now the buzzword.

The problem explained hereafter deals with classic question: which products to produce

& in how much quantity to optimize the profit with given raw material.

However in practical situation as in case with a refinery, we know which products to

produce & how much to produce. The question therefore remains is which raw material

to buy (which type of crude blend) and in how much quantity so as to meet the

production targets of our products. And this has to be done at the optimum cost possible

so as to optimize the profit.

If a plants processing capabilities are known i.e the kinds (grades) of crudes it can

process, then an INPUT COST OPTIMIZATION model can be built which can advise us

on which quality crude to buy at what rates so that our production targets can be met.

This can help us in risk mitigation given the uncertainty in costs of Crude.

So when the Crude prices fluctuate, we can get back to the model & input into it the

costs of various grades of crudes and the model will suggest a lowest cost option of mix

of various crudes which will sustain the production. This will help us control the most

uncertain factor of refinery economics: the input cost. Hence an Optimum profit level

could also be maintained irrespective of the global economic conditions & their impacton crude prices.

Furthermore its a known fact that processing lower quality crude reduces plant life (salt

deposition & various other factors). Integrating these numbers into the model will help

us develop a production plan that gives optimum return on investment over the lifetime

of the plant all the while absorbing the impact of global crude price fluctuations.

In a nutshell, when crude prices are high, we can resort to processing higher proportions

of relatively lower quality grades of crude. This will help us survive the hard times. And

when the prices smoothen we can switch to larger proportions of higher quality crudes.

This will preserve the plant life. So at the end of day we get maximum return on

investment over the plants lifetime all the time maintaining the existing product lines &

product volumes.


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Data from a Case study of Shale Oil, located on the Island of Aruba

The Problem:: Optimum Production Schedule

A Simplified Model of Refinery Operation relevant to the Problem

Final Products (Gasoline) of the Refinery::

Products Regular Premium SuperOctane Number (ON) 87 89 92

Demand Limits for Different Products of Refinery

Products Regular Premium SuperDemand (Barrels perDay)

50,000 30,000 40,000

The Net profit from the Final Products

Products Regular Premium SuperProfit (per Barrel) $ 6.70 $ 7.20 $ 8.10

Parameters of Different Processing Stages in the Refinery

Processing Stage Input Capacity(bpd) Output Input-OutputRatio

Output

OctaneNumber

Distillation Tower 1,500,000 0.2 bbl per bbl of Crude 5:1 82

Cracker Unit 200,0000.5 bbl per bbl of

Feedstock2:1 98

The Objective is to develop an optimum Production Schedule for Refinery


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The Solution

Mathematical Model

The variables can be defined in terms of two input streams to the blender (Feedstock andcracker gasoline) and the three final products.

Let xij = bbl/day of input stream iused to blend final productj.i=1,2.j= 1,2,3.

So we have

Daily Production of Regular Gasoline = x11 +x21 bbl/day

Daily Production of Premium Gasoline = x12 +x22 bbl/day

Daily Production of Super Gasoline = x13 +x23 bbl/day

Daily output of Blender= Daily Production of Regular Gasoline +Daily Production of Premium

Gasoline + Daily Production of Super Gasoline

Daily output of Blender= x11 +x21 + x12 +x22 + x13 +x23bbl/day

Daily Feedstock to Blender = x11 + x12 + x13 bbl/day

Daily Cracker unit feed to Blender = x21 + x22 + x23 bbl/day

Daily Feedstock to Cracker = 2(x21 + x22 + x23) bbl/day

Daily Crude Oil used in Refinery= 5(x11 + x12 + x13) + 10(x21 + x22 + x23) bbl/day

The objective of the model is to maximize the total profit resulting from the sale of all three

products.

Maximise z= 6.70(x11 +x21) + 7.20( x12 +x22) + 8.10( x13 +x23 )

Subject to following Constraints:

1) Maximum Capacity of plant is 1500000bbl/day, this means total crude oil supply doesnot exceed 1,500, 000 bbl/day

5(x11 + x12 + x13) + 10(x21 + x22 + x23)


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3) Daily demand for Regular Gasoline does not exceed 50,000 bblx11 +x21=89 (x12 + X22)

8)

Octane number for Super Gasoline is atleast 9282 x13 + 98 x23 >=92 (x13 + X23)

The Complete model can thus be Summarized as

Maximize z= 6.70(x11 +x21) + 7.20( x12 +x22) + 8.10( x13 +x23 )

Subject to

5(x11 + x12 + x13) + 10(x21 + x22 + x23)


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Solving this Model using Linear Programming will give the optimum values for our

variables.

Now as stated in the Executive Summary, in a practical situation we know the products

which need to be manufactured & their volumes. So if we reverse engineer this model or

create the dual of the model and make appropriate changes we will be able to predict which blends to buy to meet our production targets. We can replace the profit on final

products by the costs on blends & minimize the resulting function.

The model will predict the lowest cost option of mix of various crudes which can be used

to blend our routine products. This can help us control our costs when the crude prices

rise by switching over to lower cost lower quality crudes.

However, processing lower quality crudes can degrade the equipment & pipes. If we

could quantify these rates of damages we can incorporate the same into model. So while

prices are high we can go for more of lower quality lower priced crude which are well

within the processing capability of plant.Hence we canoptimize the return on investmentover the life of plant.

Sunil Pillai, SIP

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