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Title: Intelligent Methods in Mineral Processing - Treating the Mine-Mill Complex as a Factory


1
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_at_mining.ubc .ca
2
Outline
  • 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

3
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

4
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

5
Incentives for Option 2
  • Impurities and Material Quality Issues
  • New Processes
  • Local Markets
  • Recycling
  • Value-added

- may require separate processing
- allow final product production at the mine
- can sustain production of final product
- can create new markets
- additional value ( gold jewellry, Polar
diamond)
6
Incentives for Option 2
  • Regulations
  • Infrastructure
  • Design impact
  • Local resources
  • Delivery costs

- can provide reasons for value-added
- can sustain mining in remote regions
- down-stream processing can affect design
decisions
- power, rail, shipping ports, etc. may provide
benefits
- savings in transportation costs
7
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

8
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
  • robotics
  • advanced materials
  • nanotechnology

9
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

10
Flexibility - the Key to Intelligent Operation
  • Create alternate plans
  • Expand mine production
  • Maintain production costs (or reduce)
  • Change mill circuit layout
  • Adjust product mix and/or quality

11
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
12
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

13
Traditional System Hierarchy
14
Intelligent Manufacturing Systems
NASA/NIST STANDARD REFERENCE MODELING
ENVIRONMENT
servo control
after Monckton, 1997
15
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

16
Real-Time Intelligent Control System Modules




17
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)

18
Who or what is an Expert?
  • someone who everyday knows more and more about
    an ever-diminishing field until the scope of
    knowledge becomes so small that he/she knows
    everything about nothing.

An expert is
simply someone who has acquired specific
knowledge about a special area acquired over
years of working with a process or piece of
equipment.
19
Acquiring Knowledge
  • The man from out of town is not necessarily the
    expert.
  • Rather this person is
  • The Knowledge Engineer

20
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
21
Acquiring Knowledge
  • Sometimes multiple
  • experts are involved

22
Acquiring Knowledge
????
!!!!!
!!!!!
????
????
!!!!!
Sometimes special consultants are needed
23
Acquiring Knowledge
Sometimes knowledge overload occurs
24
Acquiring Knowledge
Care must be taken that an interview does not
become..
  • an interrogation

25
Acquiring Knowledge
The exercise must not be viewed as a competition
26
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"

27
Fuzzy-Logic Inferencing
100
Low
Medium
High
Degree
of
Belief
0
0
6
12
tank level
28
Fuzzy Associative Map
tank level
pump
speed
low
medium
high
med-low
med-high
opened
not
opened
opened
closed
minimum
a lot
changed
a little
a lot
a little
not
closed
opened
opened
closed
normal
changed
a lot
a lot
a little
a little
not
closed
closed
closed
opened
maximum
changed
a little
a lot
a lot
a little
29
Artificial Neural Networks
  • 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

30
Artificial Neural Network Modeling
31
Artificial Neural Networks
Basic Neuronal Equation
inputs 0 to 1 outputs 0 to 1
weights - to
32
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

33
Operators in GA
  • Selection for reproduction
  • Cross-over operator
  • Mutation operator
  • Elite strategies (cloning)

34
Real-Time Intelligent Control System Modules




35
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

36
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

37
Holonic Manufacturing System
Object A'
after Monostori and Kadar, 1999
38
Structure of a resource agent
Material flow
after Monostori and Kadar, 1999
39
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

40
Architecture of a CORBA Communication Protocol
System
Object Implementation
Client
ORB Core
Dynamic Skeleton
IDL Skeleton
Dynamic Invocation
Object Adapter
ORB Interface
IDL Stubs
ORB Core
  • after Nicoletti, 1999

41
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)

42
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

43
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
44
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 (DUST) 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
45
Matrix of Launch Vehicle Design Disciplines-
Zweber et al., IPMM-2001
46
Lockheed-Martin's Missile Design Network- Zarda
et al., IPMM-2001
47
Optimization-based DesignThe 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
48
Processing Sequence Design Problem
49
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
50
http//webme.ent.ohiou.edu//vm/
51
VRML model of the strip rolling process
52
Swarm Intelligence
Ant Colonies exhibit "collective" intelligence
The Civil or Mining Engineers of the Insect World
53
Ants can Fold a Leaf
54
Ants can Build a Bridge
55
Ants can Farm
  • Harvesting food
  • Storing food
  • Feeding their young
  • Serving their Queen

56
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

57
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

58
ANT COLONY OPTIMIZATION
  • Applications
  • Travelling Salesman Problem
  • Telecommunication Channel Assignment
  • Vehicle Routing (shovel/truck scheduling)

59
ANT COLONY OPTIMIZATION
  • Benefits
  • Solve extremely large-scale problems
  • Faster than Genetic Algorithms
  • Highly adaptable to changing conditions

60
Adaptability of Ants

61
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
62
Analogy Between Ants and Shovel-Trucks
63
Feeding and Herding the African Ants
64
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

65
Applications in Mining
  • Improvements in Environmental Control
  • Vertical Integration Opportunities
  • Strategic Planning of Investment/Expansion
  • Intelligent Stockpiles
  • Enhanced Comminution Systems

66
Applications in Mining
  • Coordinated Real-Time Maintenance
  • Tele-remote Operations
  • Enhanced Data Communication Protocols
  • Discovery of New Ideas
  • Value-Added Production at the Mine

67
Example 1 Highland Valley Copper
  • Optimized comminution requirements
  • - blasting (fragmentation)
  • - primary crushing (10" gt 4")
  • - semiautogenous grinding (SAG)
  • Benefit
  • - increased throughput by up to 30
  • Discovery
  • - SAG milling is not a legitimate unit process

68
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

69
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

70
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

71
The Polar Diamond Brand
72
The Polar Diamond Certificate
73
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

74
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75
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.

76
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

77
IPMM-2001
  • Conference Theme
  • "Cross-Disciplinary Research in IPMM - an
    Essential Ingredient for Innovation!

78
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

79
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

80
Bill Reids Jade Canoe - The
Spirit of Haida Gwaii
  • "...The Spirit Canoeis 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

81
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

82
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?

83
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.

84
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?"
85
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!"
86
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

87
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.

88
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

89
IPMM and Paper Use
InterNet - - ü üü
Year 1997 1999 2001 2003
Pages 2200 1550 1750 ?
hard copy ü ü - -
CD-ROM - ü ü -
Price ( US) 20,000 15,000 500 0
in 2003 the Proceedings will be entirely on the
Internet
90
Paper-Recycling and the Internet
  • 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?

Why legislate something that is a natural
evolution?
91
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 shouldnt be done at all "

  • - Peter Drucker

92
The Evolution of the Internet
  • Year Number of hosts Innovation
  • 1965 2 ARPA(DARPA)
  • 1968 4 ARPANET
  • 1971 8 Telnet, Ethernet
  • 1974 32 TCP/IP - UUCP - FTP
  • 1978 100 USENET
  • 1983 1,000 DNS
  • 1987 10,000 T1
  • 1989 100,000 World Wide Web/HTML
  • 1992 1,000,000 T3
  • 1995 10,000,000 first e-business
  • 1999 100,000,000 first
    software agent

soon there will be more host computers than people
93
The Age of Machines
  • The Trans-humanist and Post-humanist Societies

94
The Age of Machines
Benefits
Democratization of Information and the advent of
"Empire"
"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 Nanote chnology will combine with
Computational Intelligence No more Human "Wet"
Diseases
  • "If you can hang on until 2016, you will never
    die!"
  • - J.W. Lewis

95
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

96
IPMM03
  • 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?

97
Fuzzy-Woozy meets Fuzzy Logic
98
Fuzzy-Woozy Logic
An Illusion of a Reality that is of itself a
Reality
99
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