COM2011 - Integrated Process Modelling of Ni-Cu Sulphide Treatment at Xstrata Nickel… ·...
Transcript of COM2011 - Integrated Process Modelling of Ni-Cu Sulphide Treatment at Xstrata Nickel… ·...
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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