Integrated Process Modelling of Ni C S l hid T Ni-Cu Sulphide Treatment at

Xstrata Nickel’s Sudbury Smelter

N Tripathi R Pandher R Schonewille and A BarnesN. Tripathi, R. Pandher, R. Schonewille and A. BarnesCoM2011 Montreal

Wednesday, October 5, 2011

Introduction

Outline of Presentation

Introduction

Process Models• Thermo-Chemical Model: FACTSAGE• Heat and Mass Balance Model: METSIM• Discrete Event Model: ARENA• Combination: CHEMSHEET

Smelter ProjectsSmelter Projects• Operational Troubleshooting• Capital Projects

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Xstrata Nickel’s Sudbury Smelter

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Aerial view of Xstrata Nickel Sudbury SmelterFalconbridge, Ontario, Canada

Introduction: Xstrata Nickel’s Sudbury Smelter

Location: 15km northeast of Sudbury in the township of Falconbridge. Operational since 1930.1930.

Capacity: 550,000 tonnes of Ni/Cu concentrates annually

R t f O ti l ll i i Recent focus: Operational excellence, emission control, capacity expansions

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Xstrata Nickel’s Sudbury Operation

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Improving plant performances needed process models:

Thermo-chemical modeling

Heat and Mass Balance Study: yIndividual Unit Operation

Applying principles of thermo chemistryApplying principles of thermo-chemistry

• FACTSAGE • METSIM

U d t di f l h i i Understanding of a complex process mechanism in a vessel such as Electric Furnace.

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Process Mechanism: Electric Furnace

Solid calcine

Calcine Bank

Freeboard

Matte

Slag

BankReducing gas

Various equilibriums within EF

• Troubleshooting• Optimized operation

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• Optimized operation• Equipment design

Defining Optimum Operating Windows

50.0 44.0

45.0 46.080% RoastMet

MG

35.0

40.0

izat

ion

48.0

50.0

Matte G

MG

25 0

30.0

% M

etal

l

52.0

54 0

Grade (%

)

Met

20.0

25.0 54.0

56.070% Roast

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15.02.00 2.20 2.40 2.60 2.80 3.00 3.20 3.40 3.60 3.80 4.00 4.20 4.40 4.60 4.80 5.00

Coke Consumption (tph)

58.0

Mapping of Matte Temperature

70 60 50 40

wt% M.G.

45556575

t% S

2570 60 50 40

700 o C800 o C

900 o C

1000o C

45556575

Bessemer Matte

wt% 20

15

1000 C

1100 o C

1200 o CEFSCV

SMC

wt% Fe

155 10 15 20 25 30 350

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wt% Fe

Discrete event modelling

Analysis of the flowsheet from a logistics perspective considers:• Ladle size• Ladle size• Crane capacity, travel time• Availability constraints• Availability constraints• Equipment utilisation• Scheduling- rebuilds etc.g• Input from METSIM and plant data

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• Computes plant capacity for various scenarios and operating conditions

Discrete Event Modeling: Arena

Total = matte + slag (inches)Ladles of slag

Inches of slag

BlowingReactionSlag skimming Ladles of matteInch of matte

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Converter Aisle Capacity: ARENA

• Cumulative Capacity of an Assembly• Cumulative Capacity of an Assembly• De-bottlenecking

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Mapping of Liquidus Temperature: pp g q pFACTSAGE

• Feed variability has impact on slag liquidus hence

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• Feed variability has impact on slag liquidus hence super heat available

• Slag losses are linked to viscosity and liquidus temperature

Silica

Spinel

Olivine Liquid Slag

1275

1300

1325

Fe/SiO2 = 1.75

Fe/SiO2 = 2

Fe/SiO2 = 2.25

Fe/SiO2 = 2.5

g L

iqui

dus

(C)

1200

1225

1250

Fe/SiO2 = 1

Fe/SiO2 = 1.25

Fe/SiO2 = 1.5

Fe

Slag

112

1150

1175

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F

% MgO by weight in slag0 .5 1 1.5 2 2.5 3 3.5 4 4.5 5

1100

1125

Dissolved Metal Losses: FACTSAGE

0.090

0.095

g

0.080

0.085

CoO

in s

la

0.070

0.075

% C

o as

C

0.060

0.065

%

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0.75 1.00 1.25 1.50 1.75 2.00 2.25 2.50 2.75% Fe/SiO2 in slag

Reducing Slag Volume

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Slag Superheat with Changing g p g gFe/SiO2 ratio

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Combined Flowsheet Modeling

Experimental d

FACTSAGEdata data

METSIM platform

Plant data• No need for the user to be expert in performing thermo-chemical

calculations• Unlike FACTSAGE, reaction kinetics can be indirectly taken into account• Validation with existing plant data and FACTSAGE data

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• Validation with existing plant data and FACTSAGE data• Reliable, accurate and robust tool for flowsheet evaluations•Uses Excel as the platform

Plant Recovery: Different Cases

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Emissions and Consumption

V i f d• Various feeds• Process changes e.g. roasting, matte grade, metallization etc.• In situation like a vessel is down in converter aisle• Plant performances, flexibility for custom feed, slag management, p , y , g g ,emissions etc.

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Evaluating Future Options

Various Capacity and Emission Controloptions were evaluated using process models:

l• Isa smelting• Isa converting• DC furnace

Important plant performance parameters

were compared for different cases

• Reductive roaster• High Roast• Controlling Furnace Atmosphere

different cases

g p• High Roast + Control Furnace Atmosphere

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Flowsheet Optimisation

• Lowering the slag volume• Lowering the slag volume

• Potential for improved overall metal recoveries

• Enhanced custom feed treatment capacity at the plant

• Enhanced revenues

• Controlled emissions

Process model is connected with an economic model to calculate CAPEX and OPEX requirements/ forecasts

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Feasibility Study

Comparing different flowsheets from Comparing different flowsheets from different perspectives:

E i• Economic• Technical• Environmental

This helps the client in selecting viable options for piloting and identifying revenue generating opportunitiesidentifying revenue generating opportunities.

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Operational Support: CHEMSHEET

• Uses FACTSAGE database and thermo-chemistry but……

li i ll i i d d• No FACTSAGE license & installation is needed• Built on simple Excel platform• Results are based on reliable data• Provides operators with an online tool • Reduces plant variability• Easy to use• Easy to use

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Example of a CHEMSHEET Model

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Summary

• Various modeling tools play an important role in supporting plant operations and their long term objectivesj

• Modeling tools need to be reliable and robust based on sound engineering principles and based on sound engineering principles and plant and laboratory data and be capable of validation.

• Working in conjuction, these integrated models reduce the time taken on investigating dead

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g gends and highlight areas where piloting should focus

Thank you. Questions?

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