Intelligent Methods in Mineral Processing Treating the Mine-Mill Complex as a Factory
John A. Meech
University of British Columbia
Department of Mining and Mineral Process Engineering
6350 Stores Road, Vancouver, B.C., V6T 1Z4, Canada
Tel : (604) 822-3984
Fax : (604) 822-5599
Email : jam@mining.ubc .ca
Outline
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Background to Problems
Strategies to Follow
Incentives for Integration
Complexity Analysis
Intelligent Manufacturing Systems
IMS Architectures - agent-based / holonic systems
Structure of an Agent
"Swarm" Intelligence
Applications in Mining and Processing
Overview of IPMM
Conclusions and Recommendations
Background
• The mining industry is at a crossroads facing:
–
–
–
–
–
–
ever-declining commodity prices
difficulties in marketing
high competition from abroad
increasingly complex ores
decreasing ore grades and reserves
a very poor image in society
Strategies to Follow
1. continue the routine of cutting costs
– labour-reduction
– adoption of new technologies
2. expand the organizational horizon to
– integrate activities across the mine and mill
– include value-added down-stream processing
Incentives for Option 2
• Impurities and Material Quality Issues
- may require separate processing
• New Processes
- allow final product production at the mine
• Local Markets
- can sustain production of final product
• Recycling
- can create new markets
• Value-added
- additional value ( gold jewellry, Polar diamond)
Incentives for Option 2
• Regulations
- can provide reasons for value-added
• Infrastructure
- can sustain mining in remote regions
• Design impact
- down-stream processing can affect design decisions
• Local resources
- power, rail, shipping ports, etc. may provide benefits
• Delivery costs
- savings in transportation costs
An Important Additional Incentive
• Complexity Analysis
– complex, interactive decision-making across
an enterprise has not been possible in the past
» poor data-communication
» poor data-collection
» poor data-analysis
– such is not the case today
The Advent of "Complex" Analysis
• Options can provide flexible response to
– changing commodity prices
– competition from other sectors




aluminum vs. copper
composite materials vs. superalloys
fibre-optics vs. coaxial cable
coal vs. petroleum products
– complex ore changes (grades and hardness)
– complex technology changes
 communication systems
 advanced materials
 robotics
 nanotechnology
Attributes of an Intelligent Manufacturing System
• Collect and manage large amounts of data
• Analyse data to optimize across departments
• Develop simulation models of interactions
between independent parts of an organization
• Apply intelligent robots to perform routine tasks
• Simulate assembly lines & plant processes to
discover new ways to coordinate processing steps
Flexibility - the Key to Intelligent Operation
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



Create alternate plans
Expand mine production
Maintain production costs (or reduce)
Change mill circuit layout
Adjust product mix and/or quality
Flows in an IMS
• Materials and Resources
• Information (messages and/or data)
Interactions between process stages are treated as
seller-customer or server-client relationships
Architectural Features of an IMS
• High-level tasks are decomposed
• Simulation conducted at different times/resolutions
• Behaviours are decomposed into sub-functions
• Functions are distributed across the system
Traditional System Hierarchy
ENTERPRISE
PLANT WIDE
SUPERVISORY CONTROL
DIRECT CONTROL
PROCESS INSTRUMENTATION
PROCESS
Intelligent Manufacturing Systems
NASA/NIST STANDARD REFERENCE MODELING ENVIRONMENT
Sensor
World
Task
Processing Modeling Decomposition
maps
objects
S1
M1
E1
S2
M2
E2
state
variables
S3
M3
E3
objective
functions
S4
M4
E4
program
files
S5
M5
E5
detect
and
integrate
model
evaluation
operational
scheduling
task
actions
path
planning
dynamic
operations
plan
and
execute
Time
Scale
days to years
hours
Multiple
User
minutes
Interfaces
seconds
milliseconds
servo
control
after Monckton, 1997
Elements of an Intelligent System
•
•
•
•
•
•
•
•
•
rule-based modeling (expert systems)
fuzzy logic inferencing
artificial neural network modeling
genetic algorithm optimization
ability to explain and justify
ability to adapt or learn from experience
management of temporal-reasoning
agent-based architectures
"swarm" intelligence
Real-Time Intelligent Control System Modules
Process
Bridge
Inference
Engine
Multiple
User Interfaces
Blackboard
InterNet
Bridge
Knowledge
Base
Artificial
Neural Network
Genetic
Algorithm
What is an Expert System?






have been in use since the early 1970s
method based on how we store memories
symbolic reasoning is central to the method
syntax is easy to learn and use
symantics of a knowledge base is easy to
understand but difficult to create
expertise is acquired incrementally from
interviews with an expert (or experts)
Who or what is an Expert?
An expert is… …the man from out of town!
… simply someone who
…someone who everyday knows more and
has acquired specific
more about an ever-diminishing field until
knowledge about a
the scope of knowledge becomes so small
special area acquired
that he/she knows everything about nothing.
over years of working
with a process or
piece of equipment.
Einstein
Acquiring Knowledge
The man from out of town is not
necessarily the expert.
Rather this person is
The Knowledge Engineer
Acquiring Knowledge
The Knowledge Engineer
must work in a collaborative
way with the Expert to extract
the gems of knowledge
and then …
...code it into a computer
program using special
AI techniques such as
- fuzzy logic
- neural networks
- genetic algorithms
Acquiring Knowledge
Sometimes multiple
experts are involved
Acquiring Knowledge
!!!!!
????
Sometimes special
consultants are needed
????
!!!!!
!!!!!
????
Acquiring Knowledge
Sometimes knowledge overload occurs
Acquiring Knowledge
Care must be taken that an
interview does not become…..
…an interrogation
Acquiring Knowledge
The exercise must not be viewed
as a competition
Rule-based Modeling
Rule Name: water_valve_high
IF tank level is definitely "high"
AND pump speed is "maximum"
THEN valve position change is "closed a lot"
DEFUZZIFY (valve position)
FIND (pulp flowrate * )
WAIT ("water_valve_high", 120 )
ELSE valve position change is not "closed a lot"
Fuzzy-Logic Inferencing
100
Low
Degree
of
Belief
0
0
Medium
6
tank level
High
12
Fuzzy Associative Map
pump
speed
low
tank level
med-low medium med-high
minimum
opened
a lot
opened
a lot
normal
opened
a lot
opened
not
closed
a little changed a little
not
maximum opened
a little changed
opened
not
a little changed
closed
a little
closed
a lot
high
closed
a little
closed
a lot
closed
a lot
Artificial Neural Networks

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
based on the neuronal structure of the brain
applied where data exists but no model
has true learning capability
slow to adapt but fast to operate
applications
– predictive monitoring (soft sensors)
– image analysis
– pattern recognition
Artificial Neural Network Modeling
Artificial Neural Networks
Basic Neuronal Equation
input 1
W
1j
input 2
input 3
W
2j
W3j
W
4j
input 4 W
nj
input n

output j
inputs = 0 to 1
outputs = 0 to 1
weights = - to +
Genetic Algorithms
 high-speed optimization method
 based on "Survival of the Fittest"
 data are coded as chromosomes
- 01101 wherein each digit represents a
different variable and its current level
 each dataset is combined with another "fit"
dataset to create a "child" solution
 each generation is "fitter" than the previous one
Operators in GA
 Selection for reproduction
 Cross-over operator
 Mutation operator
 Elite strategies (cloning)
Real-Time Intelligent Control System Modules
Process
Bridge
Inference
Engine
Multiple
User Interfaces
Blackboard
InterNet
Bridge
Knowledge
Base
Artificial
Neural Network
Genetic
Algorithm
Intelligent User Interfaces
• Process mimic diagrams
• Trend diagrams of data vs. time
• Windows to view and log messages
• Explanation and Justification Abilities
• Message filtration into classes for each user type
Agent-based IMS Structure
• Holonic manufacturing systems
- A holon is an individual element of a whole
- Holons can be made up of other holons
-
resource holons
product holons
order holons
control holons
• Modeling methodology can be applied to a
hierarchy to create a heterarchical system in
both time and space
Holonic Manufacturing System
Holonic system
Material flow
Information flow
Information
Holon
" I want
Object C ”
Holonifier
Holonifier
machine
AGV
System
Object A
Object B
Holonifier
Assembly
Robot
Object A'
Object C
Customer
Transport
Holon
after Monostori and Kadar, 1999
Structure of a resource agent
Incoming message
Sent message
Input
box
Output
box
Message
processing
Registration
mechanism
Communication
agent
Local
Database
Resource supervisor
agent
Knowledge
Base
Material
Processing
after Monostori and Kadar, 1999
Material flow
Data Models and Communication
• Product Data Management systems
- STEP system under ISO 10303
• CORBA Communication Protocol
- Common Objects Request Broker Architecture
- developed by the Object Management Group
Architecture of a CORBA
Communication Protocol System
Object
ORB Core
Implementation
Client
Dynamic
Invocation
IDL
Stubs
ORB
Interface
Dynamic
IDL
Skeleton Skeleton
ORB Core
after Nicoletti, 1999
Object
Adapter
Agent Types and System Design
•
There are 4 agent types:
- problem-solving agents
- information agents
- service agents for other agents
- translation agents
•
Aspects of designing an agent system are:
- number of agents required
- number of types of agents
- number of actions performed (complexity)
System Design Issues

Structure - level of self-containment of an agent

Communication - protocols & interchange language

Group formation - persuading machines to participate in
a group -- reward/penalty systems

Configurability - addition/deletion of machines/groups

Scalability - scale-up to the extended enterprise level

Global vs. local optima - dealing with 'selfish' agents
Intelligent Manufacturing and the Web
1. Virtual Rapid Prototyping on the Web
- interactive automation tools to simulate conceptual design
2. Enterprise Information Integration Agent System
- a collaborative infrastructure for large-scale integration
3. Multi-Agent Framework for "Lean" Manufacturing
- customer-driven with globally synchronized-scheduling
4. Internet agent-based Infrastructure for Mass Customization
- Internet supports global communication between
customers and manufacturers
after G. Nicoletti, IPMM-2001
Web-based Collaborative Engineering Design
- Adaptive Modeling Language (AML) demo by TechnoSoft Inc.
- developed from a single-user, single-computer environment used to
model complex engineering problems
- a Dual Use Science & Technology (DUS&T) agreement
Air Force Research Laboratory,
Lockheed-Martin Electronics & Missiles, and
TechnoSoft Inc.
- multiple users interact simultaneously with a unified parts model
over a network of geographically-distributed machines
Chemaly, IPMM-2001
Matrix of Launch Vehicle Design Disciplines
- Zweber et al., IPMM-2001
Lockheed-Martin's Missile Design Network
- Zarda et al., IPMM-2001
Optimization-based Design:
The Multi-Process Design Executive
- software package to design multi-stage materials processes
- based on the Adaptive Modeling Language (AML)
- integrates models of materials, geometry, processes, equipment, and
cost with optimization algorithms
- a tool for preliminary selection of manufacturing processes
- to evaluate alternate processing sequences and parameters at early
design stages, when decisions have the greatest influence on cost
- demo-ed processes to manufacture Ti-alloy turbine engine disks
E. Medina and W. G. Frazier, IPMM-2001
Processing Sequence Design Problem
Design System Main Control Object
D1
F1
Optimization Objects
Di
Initial
Workpiece
P1
P2
Ai
Fi
Pi
Bi
Process Sequence Object
Pn
Final
Workpiece
Virtual Manufacturing Environment
 Web-based
interface to integrate material process design and
analysis modules
 models
of various manufacturing processes
 module
to view the output as a 3D model in a web browser
 interface for
headgear and Data-Glove to provide an interactive,
immersive environment
B. Mehta, IPMM-2001
http://webme.ent.ohiou.edu//vm/
VRML model of the
strip rolling process
Swarm Intelligence
Ant Colonies exhibit
"collective" intelligence
The Civil or Mining
Engineers of the
Insect World
Ants can Fold a Leaf
Ants can Build a Bridge
Ants can Farm
• Harvesting food
• Storing food
• Feeding their young
• Serving their Queen
WHAT IS SWARM INTELLIGENCE?
 Refers to a higher-level "intelligence"
 autonomous agents acting in their natural environment
 each with local low level behaviour
 collective action results in an "apparent" intelligence
Swarm Intelligence and Modeling
• Can help solve complex problems by providing
-
a distributed model
an adaptable model
a flexible and robust model
an extremely fast optimization algorithm
• Fits in well with agent-based models
- a centralized program is replaced by an emergent
and distributed set of autonomous functional entities
ANT COLONY OPTIMIZATION
 Applications
– Travelling Salesman Problem
– Telecommunication Channel Assignment
– Vehicle Routing (shovel/truck scheduling)
ANT COLONY OPTIMIZATION
 Benefits
– Solve extremely large-scale problems
– Faster than Genetic Algorithms
– Highly adaptable to changing conditions
Adaptability of Ants
Adaptability of Ants
Adaptability derives from cooperation of individuals
(not intelligence) because of 2 factors:
1. Pheremone signals between ants
2. Stimulus-response of each ant
The collective response guarantees survival of the colony
Analogy Between Ants and Shovel-Trucks
Block 1
Block 2
Target
Block n
Maintenance/ Emergency repairs
Feeding and Herding the African Ants
Applications in Mining
• Systems Design and Simulation
• Orebody Modeling
• Long-Range Planning of Production Options
• Mine Planning and Scheduling
• Optimization Studies on Mine/Mill Interface
Applications in Mining
• Improvements in Environmental Control
• Vertical Integration Opportunities
• Strategic Planning of Investment/Expansion
• Intelligent Stockpiles
• Enhanced Comminution Systems
Applications in Mining
• Coordinated Real-Time Maintenance
• Tele-remote Operations
• Enhanced Data Communication Protocols
• Discovery of New Ideas
• Value-Added Production at the Mine
Example 1: Highland Valley Copper
• Optimized comminution requirements
- blasting (fragmentation)
- primary crushing (10" > 4")
- semiautogenous grinding (SAG)
• Benefit
- increased throughput by up to 30%
• Discovery
- SAG milling is not a legitimate unit process
Example 2: Mount Isa Mines
• Examined orebody to provide stable mill feed
- ore hardness (variance reduced by 10%)
- head grades (variance reduced by 25%)
- ore reserves reduced by 25%
• Benefits
- increased throughput by 15%
- improved recovery by 5%
• Discovery
- new methods to treat lost reserves
Example 3: Harmony Gold Mine
• Installed new process to produce 99.99 % Au
- refining stage after bullion production
- new process for gold bars (powder metallurgy)
- jewelry production at mine site
• Benefits
- new opportunities for local labor force
- increased marketing opportunities
• Discovery
- can market gold to consumers on the InterNet
Example 4: Ekati Diamond Mine
• Invested in jewelry production outside of CSO
- set up new facility in Yellowknife
- marketing the "Polar Diamond"
- about 20% of total production
• Benefits
- new job opportunities for local labor force
- increased marketing opportunities
• Discovery
- can market diamonds directly to consumers
The Polar Diamond Brand
The Polar Diamond Certificate
Example 4: Globalcoal.com
• Joint Venture by 4 of the largest mining companies
- Anglo American
- Billiton
- Glencore International
- Rio Tinto
• Created a single online marketplace for thermal coal
- set to begin February 2001
- will be expanded to iron ore and base metals
- threatens conventional markets such as LME
- provides opportunity to market to many customers
Example 5: Internet Commerce
• Australian mining companies have set up a B2B
market web site to provide auction opportunities for
multiple suppliers and consumers of raw materials
• BHP is planning to sell "rough" uncut diamonds over
the Internet to wholesalers wishing to take their
stones to a jeweller to have them cut and designed the
way they want, bypassing numerous intermediaries.
GST payments are reduced as well.
Conclusion
• Alternate strategies to cost-cutting are required
• Opportunities exist to apply Intelligent Manufacturing
Systems based on Agent or Holonic principles
• IMS can provide data collection and data analysis at various
time and resolutions to conduct simulation modeling
• Value-Added production at the mine site can be examined
using an IMS system
• High-tech Internet applications can lead to significant
improvements in the industry's image and competitiveness
IPMM-2001
Conference Theme
"Cross-Disciplinary Research in IPMM an Essential Ingredient for Innovation!”
A Brief History of IPMM



Founded in 1997 in Gold Coast, Australia.
In 1999, the 2nd International Conference
was held in Honolulu, Hawaii.
Now we have completed the traverse of the
Pacific arriving at the home of IPMM Vancouver, British Columbia
What is IPMM ?
An eclectic group of scientists, engineers, and
researchers with a wide variety of backgrounds
•
•
•
•
•
•
•
•
•
materials science & engineering, mechanical & electrical engineering
mining, processing & metallurgical engineering
computer science & engineering and biological computing
manufacturing & industrial engineering
chemical engineering and civil engineering (structures & transportation)
environmental sciences & engineering
astronomy and space exploration
HMIs & ergonomics / psychology (emotions in decision-making)
image analysis & vision analysis / measurement & instrumentation
The Spirit of Haida
Gwaii
"...The Spirit Canoe…is an exploratory vessel, sailing an
unknown course through unknown seas. Beings looking
for other beings to speak to, feast with, trade with..."
Bill Reid - 1992
Background to IPMM
similar to the creatures in the Jade Canoe,
IPMM Members are also travelling:
• an uncertain odyssey to an unknown destination
• looking for
– new ways to understand materials
– new processes to fabricate products
• we focus on applications but there is always
room in the boat for new theories and ideas
• we gather every two years to share in
our new knowledge and experience
Some people may ask:




“Why should a mining or processing engineer
participate in a conference with an astronomer?”
“How can a scientist studying manufacturing techniques
possibly gain anything from listening to a psychologist?”
“What can a ‘soft’ scientist learn from a mining
engineer?”
“I'm a materials researcher. Why should I care about
these so-called "intelligent" methods?”
Legitimate Questions
- here are some answers





the world has become a much more complex place in
which to work and study.
no single person or group can adequately hope to find the
"right" answer any more.
there may no longer even be a "right" solution.
"intelligent" methods derive from single minds operating in a
collaborative environment.
issues must be addressed using a multi-dimensional approach
– one which lends itself to input from cross-disciplinary teams.
Collaboration
– the Key to Innovation

Ideas spring from a single mind.

Even the best minds freely admit that they performed at
the top of their abilities when they were "collaborating".
The question is -

"how can we create environments which
capture the best of truly ‘great’ collaboration?"
The Return of the Generalist
"You can lead a person to knowledge,
but you can't make them understand it.”
"While the Internet may have democratized the
availability and access to Knowledge,
Intelligence is a commodity that can never be
distributed uniformly
- it must be shared to be useful and to be used!"
Concern for People is Key
• sharing comes from mutual respect and trust
• a collaborative system must do more than
simply provide a common work space
• it must not inhibit creativity and innovation
• searching for "intelligence" must be the goal
• an individual reward system is essential
Paper-Recycling and the Internet
The goal of the University of Newcastle
Paper Usage Action Plan is
To reduce the consumption of paper products.
with a 4-point plan:
–
–
–
–
Reduce paper consumption for University communication.
Maintain & establish programs for recycling and reuse of paper products.
Encourage "environmentally-friendly" stationery and business equipment.
Encourage "environmentally-friendly" bathroom paper products.
The Paperless Office
 The
Internet was supposed to give us the Paperless Office.
 Instead paper use has increased steadily - Why is this?
 In migrating from one paradigm to another,
 change is resisted and we continue using paper
- even more so, as we search for the "perfect" draft!
 As
more people use the new tools, paper use goes up.
 As our comfort-level with the environment increases,
slowly we stop using paper naturally and entirely!
– no hard copy reports
– only email communication or wireless cell phones
IPMM and Paper Use
Year
1997
1999
2001
2003
Pages hard copy CD-ROM InterNet Price ($ US)

2200
~ 20,000


1550
~ 15,000


1750
~ 500

?
~0
in 2003 the Proceedings will be entirely on the Internet
Paper-Recycling and the Internet
Why legislate something that is a natural evolution?
» Rules should be made for the benefit of the group in
total, not for a single individual or sub-group
» Rules should not stifle creativity and innovation
» The World is made up of three main groups



the Sergeants (or Bosses/Decision-makers)
the Anarchists (or Thinkers/Rebels)
the Uppers (or Workers/Believers)
» I submit - we must find the "intelligence" in these
activities -- who are these systems designed for?
Other Examples
Why legislate something that is a natural evolution?
» The Vancouver "Air-Care" Program
» "Blue-Box" Programs
» Regulating the Internet
"There is nothing more useless than doing
efficiently what shouldn’t be done at all "
- Peter Drucker
The Evolution of the Internet
Year Number of hosts
1965
1968
1971
1974
1978
1983
1987
1989
1992
1995
1999
2
4
8
32
100
1,000
10,000
100,000
1,000,000
10,000,000
100,000,000
Innovation
ARPA(DARPA)
ARPANET
Telnet, Ethernet
TCP/IP - UUCP - FTP
USENET
DNS
T1
World Wide Web/HTML
T3
first e-business
first software agent
soon there will be more host computers than people
The Age of Machines
The Trans-humanist and Post-humanist Societies
The Age of Machines
Benefit
Democratization of Information
and the advent of "Empire"
s
"a fluid, infinitely expanding and highly organized system
encompassing the world's entire population."
- Michael Hardt and Antonio Negri
Computers outperform Humans in thinking and in emotions
Nanotechnology will combine with Computational Intelligence
No more Human "Wet" Diseases
"If you can hang on until 2016, you will never die!"
- J.W. Lewis
Closing
IPMM is fulfilling an important function by
» organizing biannual meetings to discuss "intelligent"
methods for material production and manufacturing
» providing a collaborative environment to share in
new ideas across multiple disciplines
» creating a society that understands the importance
of "intelligent" approaches to processing and
manufacturing of materials
» promoting the use of "intelligent" thinking in the
important technical activities of the 21st Century
IPMM’03
The 4th International Conference on
Intelligent Processing and Manufacturing of Materials
Tohoku University, Sendai, Japan
May 18 - 23, 2003
Theme:
Nanotechnology for the 21st Century
– do good things really come in small packages?
Fuzzy-Woozy meets Fuzzy Logic
Fuzzy-Woozy Logic
An Illusion of a Reality that is of itself a Reality