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The Use of
Overload Detection and
Dual Entry Controlled Variables
to Maximize Production for an
Iron Ore Concentrator Plant
Bill Poe – Technical Advisor
AVEVA
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Presentation Outline
Introduction to AVEVA
Case Study
Objectives
Challenges
AVEVA APC Software
Application Design
Application Results
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Introduction to AVEVA
• AVEVA is a leader in industrial digital transformation
• FTSE 100 listed on the London Stock Exchange
• Combined with Schneider Electric software business on 1 March 2018
4,600 Employees 1,700 Total Capacity in R&D 4,500 SI Partners
10+ R&D Centers 16% of Sales on R&D 5,700 Certified Developers
22+ Project Centers 85% of R&D NextGen programs 160 Technology Partners
Oil and Gas Food,
Chemicals Beverage, Marine Life Science Mining Infrastructure EPC Power
CPG
Market Capitalization over £10B Production Optimization
Annual Revenues over £830M 50+ Value Chain Optimization
Asset Performance Management
Pending Acquisition of OSIsoft mining sites Workforce Transformation
Remote Operations Center
Advanced Process Control applications on communition, flotation, flash furnaces, smelters, roasters,
3 decomposers, kilns, dryers, sulfuric acid, air separation, solvent extraction and electrowinning
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Case Study: Iron Ore Concentrator
• Open Pit Truck and Shovel Mine
• Two stages of Crushing
• Transport to Plant by Rail
• Additional stage of Crushing
• Iron Ore Concentrator (3 Lines)
– Closed circuit rod mill / ball mill
– Magnetic separation
– Size and density separation
– Slurry to pellet plant
• Pellet Plant / Induration
• Rail to Port
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Grinding Circuit
First Stage Mag Sump
Rod Mill Separation
Feed ½in
DI
Water
Water to
Cyclone Hydro
Feed Sizers
Pumps PSI
Second Stage Low Density
Ball Mill
Sump Mag : Overflow to tailings
Separation
Overflow
Cyclones
Concentrate
Hydrosizer
Cyclone
Underflow Screens
Oversize
Undersize
Third Stage Mag
Separation
Screens
AI
Oversize Conc Product
to Pellet Plant
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Main Objectives
• Maximize feed rate subject to constraints
• Produce product concentrate within 5.4% silica limit as
measured by the line analyzer
• Cyclone feed density below an operator entered limit
• Concentrate Hydrosizer underflow density below high limit
• Avoid mill overloads
• Derrick VSDs are adjusted before cutting feed
An Expert System Approach had failed due
to properly considering dynamics
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Challenges
Application design must satisfy the main objective and
be:
Robust to Ore and Equipment changes
Tolerant of instrumentation drift
Tolerant of field adjustments
Minimum maintenance
Keep operators happy
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Operating Crews
Very experienced
20+ years at the console
Younger generation
starting console work
Most experienced
operator splits time
between console and field
A lot of shift-to-shift
variation
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AVEVA APC Software Platform
(formerly Simsci APC and Connoisseur)
Dynamic modeling methods: Modern and intuitive user interface
• transfer functions • Efficient via minimal clicks
• finite impulse response • Little need for a manual
• autoregressive moving average • Scalable for highly complex problems
• composite linear • Drag and Drop
• neural networks • Cut and Paste
Inferentials Virtual Training and Certification Program
• Principle Components Analysis
• Partial Least Squares
Python Scripting
• all types of logic
• model switching for ore type
• analyzer filtering
• expert system creation
Compatible with all DCS and PLC’s through OPC
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Application Design
Unable to add new instrumentation
Simplest structure that will result in good returns with
existing instrumentation
Instrumentation drift addressed
Manual field adjustments handled as process
disturbances
Silica Inferential
Calculated circulating load
United States Patent US 7,591,440 B2
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Application Results
Reduced scope APC application:
Harvested most of the available benefits
Addressed crew to crew variations
Increased throughput
Decreased specific energy consumption
Prevention of abnormal conditions that impact liner life
High on-line time
Project payout in 34 days on increased throughput
Benefits verified by Operations Manager and Metallurgist
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Integration of MPC and Expert System
• Most conditions predicted using MPC
• Some conditions unpredictable using MPC
• Rules indicate when an excursion or mode
change is occurring or expected
• An MPC is configured such that a CV is
entered twice with identical models
• One instance of the CV is used “normally”
• The second instance of the CV has its
setpoint or constraints set by an expert
system
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MPC Model Structure with Double Entry CV
Fresh Feed BM Discharge
CVs Rate (MV) Water (MV)
1a. Ball mill power (constraint set by operator or engineer based on
a d
equipment limitations)
1b. Ball mill power (constraint set by expert system) a d
2. Cyclone feed density b e
3. Product particle size c f
US Patent Number 2015/0286192 A1, October 8, 2015 by Alan Morrow
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Application Benefits
• Despite challenges such as:
– Changes in ore type
– Reliance on inferentials
– Particle Size
– Silica
– Instrument Drift
• Maximized utilization rates
• Increased production rate of 11.4 TPH or about 2.5%
Project payout 34 days
Conservative benefit calculation reviewed by
Operations Manager and Metallurgist
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Key Takeaways
• Fast payback achieved by understanding totality of unit operations
• Application design developed after considering:
– economic factors
– equipment constraints and instrumentation reliability
– management philosophy
– maintenance department responsiveness
– operating crew skills and teamwork
– individual operator concerns
• Application took advantage of AVEVA APC capabilities
• Application took advantage of engineering know-how
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