RefiningNZ Rigorous Long Residue Inventory …...RefiningNZ Rigorous Long Residue Inventory...

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RefiningNZ Rigorous Long Residue Inventory Modelling Marcos Monguzzi, RefiningNZ, Commercial Programmer Strategy & Supply Optimisation Charles Taylor, Honeywell, Senior Consultant

Transcript of RefiningNZ Rigorous Long Residue Inventory …...RefiningNZ Rigorous Long Residue Inventory...

Page 1: RefiningNZ Rigorous Long Residue Inventory …...RefiningNZ Rigorous Long Residue Inventory Modelling Marcos Monguzzi, RefiningNZ, Commercial Programmer Strategy & Supply Optimisation

RefiningNZ Rigorous Long Residue Inventory Modelling

Marcos Monguzzi, RefiningNZ, Commercial Programmer Strategy & Supply Optimisation

Charles Taylor, Honeywell, Senior Consultant

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Presentation Overview

• New Zealand Refining Company Profile

• Role Played by RPMS for Refinery and Processors

• Business need for Long Residue inventory modelling requirements

• Process Overview

• Inventory Modelling Approach

• Problems identified and their resolution

• Business impact / benefits

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Company Profile

• is New Zealand’s only oil refinery

• JV Tolling 120kbbl/d refinery

• Customers-Shareholders: BP, Chevron, Mobil, Z Energy

• Our goal is to be NZ Oil products Supplier of choice, by being the most competitive source of supply- in terms of reliability, cost and environmental footprint

• We produce nearly 74% of NZ Oil products requirements

• We implemented RPMS and Assay2 in 2009 to replace GEMMS, together with custom tools to assist with our business model

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Company Profile

Feedstock purchase & delivery to Refining NZ

Imported

product

purchase &

delivery

International markets

26%

Product coastal

distribution

74%

Product

pipeline to Wiri

Exports &

bunker fuel

Refining

44%45% 5%

Wiri terminal Truck loading

International companies

Coastal terminals

New Zealand

supply chain

41.2 Mbbls

NZ Oil companiesRefining NZLegend:

6%

Notes: - Values and percentages are actual Refining NZ production and market estimates for 2011.

- Values include petrol, diesel, jet fuel & kerosene, fuel oil and roading bitumen.

35% 4%35%26%

New Zealand product market (~50.5 Mbbls)

Petrol (40%), Jet fuel (16%), Diesel (36%), Fuel oil (6%), Bitumen (2%)

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RPMS Use in the Planning Process

• Multi-period model (3*2months) including Inventories, Used by the refinery and the four customers for Manufacturing program submission and acceptance (RNZ=Σ Customers):

– Refining NZ

– Chevron NZ

– BP NZ

– Mobil Oil NZ

– Z Energy

• Used by the customers for crude ranking and purchasing (Multi-Case, Single or Multi-Period models)

• Used for Crude and Production Allocation to individual customers (Single Case, Single Period)

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Business Need for Long Residue Inventory Modelling

• Market constrained by excess fuel oil production. Excess FO production is exported at significant discount due to high freight costs

• Customer may be asked, or opt, to build Long Residue in storage for later processing to offset processing of light crudes or downstream unit shutdowns

• Average intake quality varies from period to period, resulting in significant differences to Atmospheric Residue quality

Gap Identified in migration project from GEMMS to RPMS

Business needs Long Residue inventory qualities to be representative of the crudes from which it was derived

Not a standard functionality in RPMS and hence involved Honeywell APS CoE

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Crude and Vacuum Tower Configuration

Complex processing:

•2 Physical Crude towers

•2 Physical Vacuum towers

•Bitumen mode

•Atmospheric residue is

directed to VDUs for

further distillation or it

can be reprocessed via

CDU to satisfy column liquid loading while on lighter feeds

Crude

Distiller

1

Crude

Condensate &

Low Sulphur

Crudes

+ Slops

Naphtha Minus:

75-185º CP

Liquid Mass

Rate 3126t/d

Density 0.711

Kerosene:

185-250º CP

Liquid Mass

Rate 2052t/d

Density 0.811

Light Gasoil:

250-300º CP

Liquid Mass

Rate 1523t/d

Density 0.853

Heavy Gasoil:

300-350º CP

Liquid Mass

Rate 2088t/d

Density 0.868

Long Residue:

350º + CP

Liquid Mass

Rate 4180t/d

Density 0.931

S.W.

Max Intake Actual

13,000t/d

Imported / RNZ

Long Residue

T10

/T12 NHDT

U250

T16

/T12

KHDS U5500

T43

to 49 HDS3 U5800

T140

to 144 HV2 U6100

T122,

123,124

JET A1 5-25%

T24,

26,27,54

F.O.

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HVU Structure

H2 from HMU

HCU

Hydro Cracker

Unit

Max Feed

4700t/d

70-80t/d H2 97%

Off Gas to

HMU

HVU1

High Vacuum

Unit

Max Intake

1100t/d

HVU2

High Vacuum

Unit

Max Intake

6000t/d

BBU

Bitumen

Blowing Unit

Max Feed

4500t/d

Long Residue from

CD1 & CD2

Imported Residue

from Storage

Long Residue from

CD1 & CD2

NZR Residue from

Storage (T140’s)

S.W.

Short

Residue

BDU

Butane

Deasphalting

Unit

Max Feed

2100 t/d

DAO

Tankage

Waxy

Distillat

e

C4

HC Tops

HC GO

HC Kero

S.W.

H2S

C4 From

Storage

S.W.

Asphalt

New

Route

Bitumen

Flash

Distillate

B45

T28

B180 T40

VGO to HDS3

Feed tankage

Tankage

Residue from HV1

on GP Mode

Bitumen Mode

(CD2 only)

General Purpose

Mode

Bitumen Feed from

Tankage and/or CD2

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Long Residue Processing – Requirements - Example

• If residue quality is defined in CRDW tables (Assay library), changes to the quality and yields of it will not be accounted for on a multi period model

• Hence LP can create a ‘free’ upgrade in quality or yield for the LR

Period A Qty [kt] API %VGO %WAX %SR TAN S %w N2 ppm

Opening Stock Basrah Resid 40 30.3 6.24% 47.75% 46.01% 0.07 3.00% 3210

To refinery Basrah Resid -20 30.3 6.24% 47.75% 46.01% 0.07 3.00% 3210

from refinery Murban Resid 35 39.9 10.34% 65.52% 24.13% 0.12 1.20% 1285

closing stock Basrah/Murban Resid 55 36.4 8.85% 59.06% 32.09% 0.10 1.85% 1985

Period B Qty [kt] API %VGO %WAX %SR TAN S %w N2 ppm

Opening Stock Basrah/Murban Resid 55 36.41 8.85% 59.06% 32.09% 0.10 1.85% 1985

To refinery Basrah Resid -40 36.41 8.85% 59.06% 32.09% 0.10 1.85% 1985

from refinery Murban Resid 35 39.9 10.34% 65.52% 24.13% 0.12 1.20% 1285

closing stock Basrah/Murban Resid 50 38.6 9.80% 63.17% 27.03% 0.11 1.44% 1540

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Long Residue Processing - Requirements

• Enable Long Residue (LR) to go to storage from multiple operating modes

• LR inventory required for Fuels mode and Bitumen operating modes

• To retain HVU yields and qualities for the LR in inventory, such that HVU yields and qualities are rigorously simulated when the LR is processed. Effectively: – retaining visibility of the crude processed from which the LR in storage

was generated,

– such that yields and qualities of HVU products when processing LR ex storage would be the same as when processing the LR generated directly from crude processed on the CDU

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Long Residue Inventory Modelling Approach

Processing Straight Run LR Direct from CDU • Define HVU products as implicit products (implicit =

deferred cuts) • HVUs then explicitly defined as standard process

submodels • HVU yields derived from ‘implicit yields’ defined in

Assay – defined as new qualities in the assay

• HVU product qualities recursed in normal manner

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Long Residue Inventory Modelling Approach

Processing LR ex Inventory • HVU yields also derived from ‘implicit yields’ defined in Assay (these

are recursed as properties through inventory)

• Determining the properties of the HVU product cuts for feed stock processed via inventory is not so easy….

• The product cut qualities are lost as soon as they go into inventory (or RPMS looses visibility of the Assay data)……..

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Solving the inventory problem

• Solving inventory problem with implicit cuts: – Need to retain the individual implicit stream qualities through the

inventory blend – eg 1SD spg, sul etc. • Achieve this by modelling new set of properties for each implicit cut

for the Vacuum Unit:

• New qualities can be then be blended in the normal manner in inventory

• Individual implicit HVU yield qualities can then be calculated via customised recursion structure in the new HVU process models (Similar to current QPROPRT structure in Deasphalting / Platformer models).

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Long Residue Inventory Modelling Approach

LR Inventory Blending • Blend equation for HVU yields defined as fraction of Long Residue

• Blend equation for new ‘Child’ or psuedo properties introduced for each HVU product stream and stream quality are determined as follows (example for spg of cut 1FW)

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New ‘Pseudo’ Properties for LR inventory modelling

• Current model has 25 true properties • 72 new Pseudo properties for LR modelling structure – example for HVU#1 below

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Model Over view

New HVU process

models

New CDU’s for

reprocessing

Atmos Resid

Atmos Resid

inventories- IV8, NZ2

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Revised Long Residue Potential Modelling Concerns

Concerns Prior to Implementation

• Drastically increases the size of the matrix and slows down the convergence time.

• Step change in the number of properties represented in the model

• It may also exacerbate Local Optimum Phenomena.

• The user is forced to define properties of material in the inventory as defined in the Assay (different code names carried in the Parent Process Stream itself ). This is quite cumbersome.

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Modelling Problems Endured

• Model convergence became a challenge

• Hence needed to audit model: – Tuning recursion tolerances

– General model settings

• Aligned model custom structures and constraints with best practices

• Development of a calculator to determine pseudo property values to be entered for Long Residue in inventory.

• Local Optima became an issue requiring implementation of ‘best practices’ to minimise occurrence

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Handling Local Optima

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Local Optima – back ground

• All LP models have local optima solutions,

• As users we are just not always aware that they are happening – only aware of local optima when you find one.

• Caused by:

– Non linearity of refinery models

– Use of penalties with high initial values can be a major cause of local optima

– Property tolerance setting have some influence as optimiser can stop searching (as satisfied tolerance criteria) before a better optimum has been found

– Bad starting point for initial estimated stream qualities

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Minimising Local Optima

• Having a well tuned model will help to reduce local optima

• Use of SLPA (Successive Linear Programming) for complex model will limit the step size the solver can make in searching for an optimum solution. Hence preventing solver from ‘jumping over’ a better optimum solution

• Tighter property tolerances will help – having loose tolerance is equivalent to having a non converged solution. For non converged solutions ‘you do not really know where you are’

• Alignment of starting costs for penalties with crude price, such that optimiser considers breaking penalties in its search for an optimum solution

• Removing penalties from model can also help if users are able to resolve infeasibilities.

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User Influence Over Local Optima #1

• Initial stream qualities are generally automatically calculated by RPMS; – Initial Crude stream qualities / yields are recalculated based upon

entries in T. CRCHG

• Initial estimates for intermediate blends can be updated in TRACKB

• Use of Case Stacking initiated from a good planning case helps

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Updating T. TRACKB - #1

• T. TRACKB can be updated with the latest solution using RPMS “Copy Matched” functionality

• Step 1: Open T. TRACKB In the BASECASE model

• Step 2: Open T. #SQU in the solution file – USRAHOC.WRK

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Updating T. TRACKB - #2

• Step 3: after selecting ‘File’, the user is presented with the popup box below. Navigate to the ‘WORK’ directory and open file USRAHOC1.WRK

• Step4: type in text ‘#SQU’ as shown and open this table

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Updating T. TRACKB - #3

• Step5: Select Excel view option such that both tables #SQU and TRACKB are displayed side by side

• NB #SQU contains all the stream properties for the latest model solution.

• Step 6: select all contents of the sheet #SQU (rh side below)

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Updating T. TRACKB - #4

• Step 7: From RDFactory menu select ‘Copy Matched’ function

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Updating T. TRACKB - #5

• Step 7: From RDFactory menu select ‘Copy Matched’ function

• Step 8: Make sure that option ‘Ignore Empty Cells’ is selected. Select the destination file you wish to copy the data to and press ‘OK’

• If you forget to select t the ‘Ignore empty Cells option then close you Excel file and start again.

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Updating T. TRACKB - #6

Contents of TRACKB will then be updated automatically. Then save and close T. TRACKB and continue with your model runs

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Checking the Solution for Local Optima

• Investigate whether you can find a better solution by:

• Changing the starting point (T. TRACKB updates)

• Relax / tighten up constraining variables to see if model solution moves in the right direction.

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Thank you for your attention