Upload
alonso-valeriano
View
217
Download
0
Embed Size (px)
Citation preview
8/10/2019 A Knowledege Desisin Support System for Mining Methods Selection for Ore Deposits
1/15
A Knowledge-based Decision Support System for
Mining Method SeleCtlon for
Ore Deposits
9
198
Sukumar Bandopadhyay, University of Alaska Fairbanks
and
P. Venkatasubramanian, Temple University, Philadelphia
INTRODUCTION
In recent years, research in the field
of
artificial intelligence (AI) has had many
important successes. Among the most significant of
these
has been
the
development of
powerful new computer software known as the expert systems .
Expert system programs designed
to
provide expert-level consultative advice
n
mineral exploration (Duda et
aI,
1981), scientific (Feigenbaum, Buchanan and Lederburg,
1971) and medical (Shortliffe, 1976) problem solving
are
generally acknowledged t be
among the forerunners of this research.
As the decision makers virtually
in
all fields face a more complex and involved
world within which to operate, the
need
for some decision support is also becoming
urgent. Thus applications of
expert
systems continue to spread out reaching problems
that, because of their dimensions or particular aspects, set more demand on the decision
methodologies. To meet these new requirements, many activities which were performed
in the past by the
domain
expert
or
engineer must
become automated.
Furthermore,
previous research (Dawes and Corrigan, 1974; Dawes, 1979) has shown that automating an
expert s decision rules
often
provide
better
decision
than
the expert
does.
For large
systems it would be very useful (or even necessary) to have formal tools, allowing one for
example, to automatically discover inconsistencies, contradictions
or
redundancies, or to
identify the possibilities of wrong lines of reasoning in the decision process.
90-0126.KNO
8/10/2019 A Knowledege Desisin Support System for Mining Methods Selection for Ore Deposits
2/15
Page
2
Although computers and simulation models have become indispensable tools in
many mine planning endeavors, there is continued reliance on the human expert s ability
to identify and synthesize diverse factors, to form judgments, to evaluate alternatives and
to aid decisions.
Traditionally simulation methods and other analytical techniques have been used to
aid decision making process in mine planning problems.
In
some sense all these programs
and techniques try to behave expertly in their attempt to perform some well defined set of
tasks. However some
domains
are
more
highly constrained not easily
amenable
to
precise scientific formulations, i.e., domains in which experience and subjective judgment
plays an important role.
While the domain
of
decision-aid is of
immense
practical value, it is also
of
considerable
interest
in terms
of
its AI research content. In its most general form, it
involves representing the structure and functions
of
complex systems, along with some
knowledge about the problems the system is intended to deal with. Inference mechanisms
are
needed which can perform
completely,
even
expertly,
in domains
where system
variables are ill-defined and fuzzy
Many variables associated with geological, geotechnical, environmental, and other
conditions influence the selection of a mining method for a given mineral deposit. Each of
these
variables
in
turn depends upon other characteristics
for example, geological
variables
depend upon the
thickness
of are
body,
the grade
etc.;
and geotechnical
variables depend on the rock strength, the presence of fractures, etc. Each set of variables
has significant influence on the selection of a method to mine a deposit. In reality, mine
conditions
are
so varied
that
an acceptable decision rule cannot be easily written
that
covers the
selection of
a specific mining system
or method
for all mines
or mineral
deposits.
The combination of
conditions
that
affect
the
analysis for one mine cannot
90-0126.KNO
8/10/2019 A Knowledege Desisin Support System for Mining Methods Selection for Ore Deposits
3/15
Page 3
necessarily be applied to another mine. By changing just only one variable or condition, a
permutation
s
created that
is
not applicable to another mining locale.
The selection of mining method for a mineral deposit
is
thus a decision problem of
the most
general
sort,
where solution
is considered to
be
a highly skilled art. It
is
complicated enough to justify development and use of an expert system. Even the best
geological conditions are difficult to cope with if anything less
than
the most efficient
mining method
is
used. In addition, subjective judgments are applied to information about
many geological parameters which are inherently descriptive. Impreciseness arises from
the use of descriptive and some-what ill-defined terms. For example, a decision variable
such
as
ore body thickness
is
often expressed
as
moderately thick . Similarly, the strength
of the hanging wall of an ore body, expressed as
weak .
These qualitative expressions of
important variables
in
the decision process leads to complexity. Human judgment, based
on experience with mineral seams, and geologic conditions, remains the single most
important input of the decision-making technique in the mining method selection.
In this paper, a knowledge-based decision support expert system model for mining
method selection
is
presented. The expert systems model not only helps select the correct
mining method, it also helps ensure that all important variables have been examined. t
should serve as a check list to be certain that nothing is forgotten. The best of these
systems
enable
company specialist to maintain knowledge bases that provide mine
planners with valuable engineering support. The knowledge base can be constantly added
to and edited, becoming, in the longer term, a central part in company's information
resources.
An
Expert System for Mining Method Selection for Ore Deposits.
Ore deposits are often characterized by extreme complexity, therefore the number
of methods
and
their variants
used
in the practice
of mining
ore deposits
is quite
90 0126.KNO
8/10/2019 A Knowledege Desisin Support System for Mining Methods Selection for Ore Deposits
4/15
Page t
considerable.
The
diversity
of
mining conditions and the great number
of
existing systems
complicate the elaboration of a simple classification of methods for mining ore deposits.
Many researchers believe
that
the following ten basic mining methods, not including
hydraulic or
solution
mining, reflect the essence of the methods to be considered in any
selection process.
1.
Open Pit
6.
Room and Pillar
2. Block Caving
7.
Shrinkage Stoping
3.
Sublevel stoping
8.
Cut and Fill
4.
Sublevel caving
9.
Top
Slicing
5. Longwall
10.
Square-Set Stoping
The major factor
in
determining the mining method classification
is ground
support, which, in turn,
depends
largely on the geologic characteristics and
mechanical
properties of the ore deposits and its host rock. Boshkov and Wright (1973), Morrison
(1976),
Tymshare,
Inc. (1981), Nicholas (1981) and others have
presented schemes
for
selecting mining methods. Boshkow and Wright
1,973)
listed the mining methods possible
for certain combinations of
ore
width, plunge
of
ore,
and
strength
of
ore. Morrison (1976)
classified
the
mining
methods
into
three basic
groups, rigid
pillar support, controlled
subsidence, and caving; he then used general definitions
of
ore width,
support
type, and
strain energy accumulation as the characteristics for determining mining
method
(Figure
1).
Laubscher 1977),
on the
other
hand,
developed
a detailed rock mechanics
classification from which cavability, feasibility of open stoping or room and pillar mining,
slope angles,
and general support requirements could be determined. Tymshare,
Inc.
(1981)
developed
a
numerical
analysis that
determines one of
five mining
methods,
(1)
open
pit, (2)
natural
caving, (3) induced caving, (4) self-supporting,
and
(5) artificially
supporting, and calculates the tonnage and grade for the type of deposit described. This
90-0126.KNO
8/10/2019 A Knowledege Desisin Support System for Mining Methods Selection for Ore Deposits
5/15
Page 5
method is meant to be used as a pre-feasibility tool for geologists.
The decision-making process can be treated as
the selection
of a particular
alternative
from a given
set of
alternatives
so as to
best
satisfy
some
given goals
or
objectives. The problem to be solved is to evaluate alternatives, e.g., to calculate their
utilities.
An
expert system for decision-making has to establish an appropriate knowledge
base and use it for utility calculation.
In
addition to this,
it
has to explain the way the
utility was calculated.
0
0
0
0
z
c
::l
:
i-
0
II
0
z
0-3Om
(O-100ft)
Room
Pillar
Shr inkoQ
S10cinQ
+3Om(+100ft)
0
0
.
0
e
8
0
0
c
I
0
=
L
J
II
Figure 1: A
Method
Selection Scheme (after Morrison, 1976)
90-0126.KNO
8/10/2019 A Knowledege Desisin Support System for Mining Methods Selection for Ore Deposits
6/15
Page 6
The explanation of utility calculation is especially interesting because decision
making knowledge is subjectively defined and often imprecise. It offers different
interpretations and has some degree
of
uncertainty. This kind of knowledge
is
usually
referred to as imprecise knowledge
or
soft knowledge.
The
prevalence
of
imprecision
increases when the domains are socio-economic in nature. Such domains often have
to
contend with nebulous terms and reasoning rules.
Consider the statement weak hanging wall and weak footwall characteristics have
highly significant influence on the selection of the square-set method for mining an ore
deposit . This statement
is
useful in selecting a mining system. Note, however, that the
statement is far from precise. First,
there
is uncertainty in
the
proposition. Second,
several terms in the statement are ill-defined. Good footwall characteristics and highly
significant are examples of types
of
imprecision, distinct from uncertainty, which
arise frequently, and will be
referred to
as fuzziness.
One
indication of their being
different is that one type can arise independently
of
the other.
EXPERT SYSTEM ARCHITECTURE
According
to
a definition generally
accepted
as true,
expert
systems are
characterized
by the independence of the
control structure
and the knowledge base.
Nevertheless, there are different classes of expert systems, each of them implementing a
certain strategy. The architecture of the expert system presented here is
based on
the
model developed by Bohanec et aI (1983). The selection of the above formal model was
motivated due to the fact that a semantic tree is a natural form for representing decision
knowledge and provides a suitable framework for experts for systematically formalizing
their
decision
expertise
Duda et
aI., 1978).
The tree structure facilitates
a
gradual
aggregation
of
the basic
variable values
through aggregate
variables.
90-0126.KNO
8/10/2019 A Knowledege Desisin Support System for Mining Methods Selection for Ore Deposits
7/15
Page 7
Using a top-down approach, a semantic tree with multiple nodes and several leaf
variables Figure 2) has
been
defined. Much
of
this knowledge is internalized in a
knowledge base as production rules, which are IF-TIIEN relationships. A standardized set
of
knowledge independent predicate
functions
and
a
range of
knowledge specific
attributes, objects and associated values form the vocabulary of primitives for constructing
90 0126.KNO
x
=
X
7 7
X1
I
Xl =
fl
X
27
X
3
)
X2 = f2
X57 X6)
X3
= f3 X
41
X
7
)
X4= f4 XS
7
X9, Xl0)
i ~
Figure 2: A Semantic
Tree
8/10/2019 A Knowledege Desisin Support System for Mining Methods Selection for Ore Deposits
8/15
Page 8
rules. A rule premise is always a conjunction clause, and the action
part
indicates one or
more conclusion that can be drawn if the premises are satisfied, making the rule purely
inferential.
When
a
quesiton
is asked of
the
knowledge
base
a knowledge
three is
generated. The derivation of the knowledge
is
a forward process, where
as
evaluation of
the tree is a backward contraction process a pull-back in the structure
of
facts.
The Knowledge Base and User s Interface
The knowledge of
an area
of expertise
is
generally of the three types: facts, rules of
good judgement (heuristics), and evaluations. The crucial problem in the mining method
selection process is the interpretation of the knowledge, such as:
1.
depth of the orebody and character of the overlying rock,
2. size, shape and dip of the ore body,
3.
mechanical characteristics of the ore and surrounding rock,
4. ore grade and degree of continuity.
The
goal
of the evaluation
process
is obtained
in
terms of
a preferred mining (
method and a description of the mining method. In order to achieve this task, production
rules have been developed which lend themselves to symbolic reasoning.
Within the expert system the knowledge is represented by 4-uplets of the type:
(PARAMETER, CONTEXT, VALUE, CF)
The
CONTEXT
is
instantiated
by
the name
of a
mining method. Each
PARAMETER corresponds to an attribute of this
CONTEXT
and the
VALUE
qualifies
the attribute. Finally the certainty factor (CF) defines the plausibility
of
the context. The
plausibility is a number belonging to the (-10, 10) range (where -10 means false and the
10 means true) and where all the possibilities between the absolutely true and absolutely
false
are
represented
by a
number between
-10 and 10 inclusive.
For
example:
90-0126.KNO
8/10/2019 A Knowledege Desisin Support System for Mining Methods Selection for Ore Deposits
9/15
Page 9
(Gen-shape, BLOCK CAVING, irregular, -1/10)
signifies that the selection of block caving is not at all probable when the general shape of
the
ore
body
is
irregular. Whereas, (ore-thickness,
BLOCK
CAVING,
very thick,
4/10)
signifies that the selection
of
bock caving as mining method is probable if the ore body is
very thick.
Since the rules are usually judgmental, that is,
they make
inexact inferences on a
confidence scale, the conclusions are therefore evaluated y certainty factors. Standard
statistical measures are rejected in favor of certainty factors because experience with
human
experts
shows
that experts
do
not
use
information
in a way
compatible
with
standard statistical methods (Negoita, 1985). Thus for example, if some basic variable
describes
the
footwall characteristics
of
the overburden as weak , we can specify the
selection of square-set mining as a primary method with a certainty factor 4/10 .
Certainty
factors CF) are a measure of the
association between
the premise
and
action clauses in each rule and indicate how strongly each clause
is
believed.
When
a
production rule succeeds because its premise clauses are true, the certainty factors
of
the
component clauses
are
combined. The resulting certainty factor
is
used to modify the
certainty
factor
in
the action
clauses. Thus,
if
the
premise
is believed only weakly,
conclusions derived from the rule reflect this weak belief. Also, because conclusions of
one rule may
be
the premise of another, reasoning from premises with less than complete
certainty factor is common place.
For each
rule
in the system, a CF is assigned by the domain expert. It is based on
the expert's knowledge and experience.
The
CF
that
is included in a rule is a component
certainty factor
CF
comp), and it describes the credibility of the conclusion, given only the
evidence represented by the preconditions of the rule. The rules are so structured that any
90-0126.KNO
8/10/2019 A Knowledege Desisin Support System for Mining Methods Selection for Ore Deposits
10/15
Page
10
given rule either adds to belief in a given conclusion or adds to disbelief. Because there
are many rules that relate to any given conclusion, each
of
which can add to the overall
belief or disbelief in a conclusion, a cumulative certainty factor is used to express the
certainty
of
the conclusion, at a given point in execution, in light of all of the evidence that
has been considered up to that point.
Inference rules are defined as situation - action pairs. The left member (i.e., the
situation) describes a constraint on each of the certainity factors associated with several
events. When the constraint is satisfied then the right member (Le., the action) of the rule
is triggered. This action modifies the certainty factor associated with all the events
belonging to the right member of the rule, following the certainty factor combination law.
For example:
gen-shape (Open-pit, Massive, 3/10).
gen-shape (Block-caving, Massive, 4/10).
ore-thickness (Open-pit, Narrow, 2/10).
ore-thickness (Block-caving, Narrow, -1/10).
o-rack-strength (Open-pit, Weak, 3/10).
o-rock-strength (Block-caving, Weak, 4/10).
o-fracture-spacing (Open-pit, Close, 3/10).
o-fracture-spacing (Block-caving, close, 4/10).
/
Rule Base
*
Start -7 decision (X,A,B,C,D,K), Write (X,K).
Decision (X,A,B,C,D,K), -
Geometry (X,A,B,C,K1) -
Ore-zone (X,C,D,K2) -
/* Geometry * /
j
Ore-zone' /
geometry (X,A,B,K1),
ore-zone (X,C,D,K2),
K
=
min (K1,K2).
gen shape (X,A,L)
ore-thickness (X,B,M)
Kl
=
min (L,M).
o-rock-str (X,C,L1)
o-fracture-spacing (X,D,L2)
K - min (L1,L2)
The activation of the rules modifies the certainty factor by combining the individual
certainty factors from each parameter group (Figure 3).
90-0126.KNO
8/10/2019 A Knowledege Desisin Support System for Mining Methods Selection for Ore Deposits
11/15
GEOMETRY
X,A,B,K1)
K1=MIN L,M)
MIN L,M)
ORE-THICKNESS
X,B,M)
START
LEVEI:1
WRITE X
,K)
K MIt-HK1,K2) LEVEL-2
MIN K1,K2 )
ORE
ROCK
STRENGTH
X,C,L1 )
K2=
MIN L1,L2)
MIN
L1,
L2) LEVEL-3
OR
FRACTURE
SPACING
X,D,L2)
Figure 3: A Segment of the Mining
Method
Selection Semantic Tree
User Interface
Page
11
The output of
the
mining method
expert
system is a
characterization
of
each
are
deposit in terms
of
the stability of the ground (hanging wall, footwall, and
are
zone) and its
influence
on
the
selected
mining method. The strength properties of the hangwall
footwall and the ore
zone
are
characterized
by the ratio
of
the uniaxial strength of the
material to the overburden pressure, the fracture spacing and the fracture shear strength.
The
characteristics of the of the are
body
are defined by
the
general shape the
are
thickness, the plunge of the are body, and the grade distribution. Information acquired
from the external environment is qualitative and imprecise
--
narrow , thick , uniform ,
etc. Based
on
terms of this type, and the sets of heuristic rules, inferences are developed.
90-0126.KNO
8/10/2019 A Knowledege Desisin Support System for Mining Methods Selection for Ore Deposits
12/15
Page 12
The determination of the ground stability and characteristics of the ore body is used
to determine
the
mining method. Figure 4
is
an example of the user interface with the
system.
The
expert system contains 13 parameters and
the
geological knowledge
is
encoded within 514 produciton rules. Using the resources available at the University of
Alaskaz Fairbanks this expert system was implemented and
studied on
a
VAX
750
computer, using essentially standard Prolog.
pro
C-Prolog version 1 5
1
?-
['methfux.pro].
methfux.pro consulted 27752 bytes
1 90201
yes
1 ?- start.
Questions
on geometry and grade dbn of deposit
General shape
m:
Massive
tp : Tabular
or
Platy
i : Irregular
I: i.
Ore
thickness
n:
Narrow
i :
Intermediate
t
Thick
\It :
Very thick
I:
i
Ore plunge
f
Flat
i : Intermediate
s : Steep
I s
Grade distribution
u:
Uniform
g :
Gradational
e: Erratic
I u
90-0126.KNO
Rock mech characteristics for hanging wall
Rock material strength
w: Weak
m: Moderate
s:
Strong
I:w
Fracture spacing
vc : Very close
c:
Close
w:Weak
vw : Very weak
I: c.
Fracture strength
w: Weak
m : Moderate
s: Strong
I:w
Rock
mech characteristics for
ore zone
Rock
material strength
w: Weak
m:
Moderate
s: Strong
I:w
Fracture spacing
vc:
Very close
c:
Close
w:Weak
vw : Very weak
I:vc
8/10/2019 A Knowledege Desisin Support System for Mining Methods Selection for Ore Deposits
13/15
Fracture strength
w:Weak
m:
Moderate
s: Strong
I:w.
Rock mech characteristics for foot wall
Rock material strength
w:Weak
m:
Moderate
s: Strong
I:w.
Fracture spacing
vc
: Very close
c: Close
w:
Weak
vw :
Very weak
I: c.
Fracture strength
w: Weak
m:
Moderate
s:
Strong
I:w.
Mining methods and their correspoinding scores
2
o
1
o
1
o
o
2
o
3
no
I?
1
Exit
Openpit
Block Caving
Sublevel Stoping
Sublevel Caving
Longwall
Room and Pillar
Shrinkage Stoping
Cut and Fill Stoping
Top Slicing
Square Set Stoping
[ Prolog execution halted 1
Figure
4:
An Example of the User Interace with the Expert System.
90 0126.KNO
Page 13
8/10/2019 A Knowledege Desisin Support System for Mining Methods Selection for Ore Deposits
14/15
Page 14
CONCLUSION
Expert system models are still evolving, both theoretically and in terms of their
practical applications in mining engineering. It is an useful tool for the domain considered
since analytical models are not amenable. Mining integrates the skill of many engineering
disciplines. Within these disciplines lies experience and expertise found in not other
industry.
To
capture and widely apply this expertise is the challenge to developing
knowledge base systems. This paper shows how the methodology of expert systems may.be
integrated in a mining method selection process. The integration of expert knowledge in
designing an inference process seems to be advantageous for many technical reasons.
REFERENCES
Bohanec, M. Bratko,
1.
and Rajkovic, V.
1988
An Expert System for Decision Making ,
Proceedings of the Joint IFIPWG 8.3 lH S Conference on Processes and Tools
for Decision Support, H.G. Sol, (efd), North-Holland, Amsterdam, 254 p.
Boshkov, S.H., and Wright, F.D., 1973, Basic and Parametric Criteria in the Selection,
Design and Development
of
Underground
Mining Systems ,
SME
Mining
Engineering Handbook, Chapter
12.1
Vol.
1
SM IAlME, p 12.2-12.13.
Dawes, R.M., 1979 The Robust Beauty of Improper Linear Models in Decision Making ,
American Psychologists, Vol. 34, pp. 571-582.
Dawes, R.M., and Corrigan, B. 1974 Linear Models in Decision Making , Psychological
Bulletin, Vol.
81
p. 95-106.
Duda, R., Gaschnig, J., and Hart, P., 1981, Model Design in the Prospector Consultant
System for Mineral Exploration , Expert System in the Micro-Electronic Age, D.
Michie (ed), Cambridge Press, p. 154-167.
Duda, R.,
Hart
P.E., Nilsson, N.J., and Sutherland G., 1978, Semantic Network
Representations in Rule-Based Inference Systems , Pattern Directed Inference
90-0126.KNO
c
(
8/10/2019 A Knowledege Desisin Support System for Mining Methods Selection for Ore Deposits
15/15
Page 15
System, D.A. Waterman and
F.
Hayes-Roth (ed), Academic Press, New York.
Feigenbaum, E.A., Buchanan, B.G., and Lederberg, J., 1971, On Generality and Problem
Solving: A Case Study Using the
DENDRAL
Program , Machine Intelligence,
Vol.
6
P. 165-190.
Laubacher, D.H., 1977, Geomechanics Classification of Jointed Rock Masses - Mining
Applications , Transactions of the Institute of Mining and Metallugy of South
Africa, Section A Vol.
6
p. AI-A7.
Morrison, R.K.G., 1976, A Philosophy of Ground Control, McGill University, Canada, p.
125-159.
Negoita,
C.V., 1985, Expert Systems and Fuzzy Systems, Benjamin/Cummings.
Nicholas, D.E., 1981, Method Selection - A Numerical Approach, Design and Operation
of Caving and Sublevel Stoping Mines, D.R. Steward (ed),
SME/
AIME, New York,
p.39-53.
Tyrnshere, Inc., 1981, ComputerEvaluation of Mining Projects, Mining Journal, Vol.
10
p.11l.
Shortliffe, E.H. 1976, Computer-based Medical Consultation: MYCIN, American
Elsevier, New York.
90-0126.KNO