a knowledege desisión support system for mining methods selection for ore deposits

8/10/2019 A Knowledege Desisin Support System for Mining Methods Selection for Ore Deposits

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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.

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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

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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

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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

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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)

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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.

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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


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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:

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(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

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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).

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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.

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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

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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


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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.

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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.

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of

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Mining Systems ,

SME

Mining

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12.1

Vol.

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SM IAlME, p 12.2-12.13.

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a knowledege desisión support system for mining methods selection for ore deposits

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