1

Paradigm Lost: Lessons learned in consulting

for mining and other industriesJim Everett

Centre for Exploration TargetingThe University of Western Australia

jim.everett@uwa.edu.au

CET Seminar4:00 – 4:45 Thursday 6th August 2015

2

3

4

5

6

7

8

9

Lost and Misplaced Paradigms

• Organizations often use Paradigms that are:

• Out of date,

• Out of Context – Inappropriate to the current situation,

• Based on anecdote or insufficient evidence, or

• Just plain wrong

• Often arise from a failure to realise potential of available

information technology

*

10

Some Caveats

• Recognise and Respect Domain Knowledge

• Respect Local Sensitivities

• Use Psychology as well as Science

• Work WITH not FOR the client

• Start Simple, Expand Reluctantly

Clichés – trite but true

“Client Ownership”, “Consultant is Facilitator”

11

Examples

12

Examples

13

The CT Scan

14

Cardboard Box Cutting

15

Railway Wagon Counting (1)

R² = 0.43

0

1

2

3

4

10 20 30 40 50 60 70

Train Weight (kt)

Train Length (Wagons)

16

Railway Wagon Counting (2)

R² = 0.68 R² = 0.84

0

1

2

3

4

10 20 30 40 50 60 70

Train Weight (kt)

Train Length (Wagons)

Young Staff

Old Staff

17

Cigarette Production Simulation

• Simulation of a day’s production took more than a day to run

- Simulating each filter tip as a discrete event

- Approximate as a flow

- Equivalent to using Einstein instead of Newton

• Need for timely response

18

Hospital Laundry Costing

• Needed costs for each laundry type

- Daily production was mix of types

- Regression analysis of varying daily mix solved it

19

Ore Grade Prediction

• Predicting “Lump” and “Fines” grade split for different ore types

- Had been running full day with single ore type

- Expensive, low statistical power, probably distorted results

- Solved by Regression analysis (like laundry problem).

• Also relevant for beneficiation plant

- Predicting upgrade from input ore type and grade

20

Kuwait 1990 – Chances May Not Multiply

• Software backup files stored in multiple locations

- All in Kuwait

- All lost during the Iraqi invasion

- Probabilities do not multiply, unless events independent.

21

Opportunity Cost – Ore Quality

• Ore produced to a target

- Aim to reach target levels, within tolerance

- Exceeding target reduces other opportunities

- Aim is to “Please customer” not “Delight customer”

22

Response to Error – Ship Loading

• Ships loaded from stockpiles, according to a plan

• Grade monitored during loading

• Source stockpile modified in response to assays

• Customers unhappy, although port records look good

• Change of approach – original plan adhered to

• Customers now happier, port records worse but match customers

• Psychological problems of implementation

• Target aim analogy (responding to error)

Selecting Maximum Tonnage at Target Grade• A block model for an Iron Ore prospect

• Marketing defines target grade (Fe, Al2O3, SiO2, P)• Find Maximum Ore Tonnage at Target Grade• Note – Ore Selection is not Ore Sequencing

23

24

Ore Selection – Quadratic or Composite

2.0

2.5

3.0

3.5

4.0

4.5

5.0

5.5

53 54 55 56 57 58 59 60 61

Al2O3

Fe

Selecting Maximum Tonnage at Target Grade• Select as Ore if

Comp = Fe – a.Al2O3 – b.SiO2 – c.P > Cut-off

• At each iteration, to find coefficients a, b, c:

- cumulate tonnage in descending Comp value

- calculate stress against tonnage for analyte “k” S[k]=(Grade-Target)/Tolerance

- find minimum Total Stress = ∑S2[k]

25

Select Maximum Tonnes at Target (Reference) Grade

26

Coefficients {1,‐1,‐1,‐90}

Op mum Coefficients for {Fe,Al2O3,SiO2,P}= {1,‐2.09,‐1.95,‐92.5}

Coefficients {1,0,0,0}

Coefficients {1,‐1.8,‐1.8,‐88}

0.00001

0.0001

0.001

0.01

0.1

1

10

100

0 100 200 300 400 500

Total Stress

Total Mt

Select Maximum Tonnes at Target (Reference) Grade

27

57

58

59

0 100 200 300 400 500

Fe

Total Mt

3.0

3.2

3.4

3.6

0 100 200 300 400 500

Al2O3

Total Mt

2.5

3.0

3.5

4.0

4.5

0 100 200 300 400 500

SiO2

Total Mt

.06

.07

0 100 200 300 400 500

P

Total Mt

Extracting Maximum Value Ore• A block is Ore if Marginal Value > Marginal Cost

- Marginal Cost is cost of processing and transport- not cost of mining (except near pit boundary)- Value and Cost simultaneous – no discount rate

• But note – Ore Selection is not Ore Sequencing

• Value will be a linear function of grade- (total grade is a linear blend of block grade)- but unlikely to be the same as the Comp function- Target Grade unlikely to yield Maximum Value

28

Maximum Tonnage at Reference Grade vs Maximum Value

29

Maximum tonnage

at reference grade

Non‐selected blocks of

comparable value

30

40

50

60

70

80

90

100

110

120

5 10 15 20 25 30 35 40 45 50

Composite Func on 'Comp'

Block Value (100 = Reference Grade)

30

Anecdote, “Hunting”, Batch versus Process• Tendency to respond to outstanding events and anecdotes

- Benefit of statistical approach

- But correlation is not causation

- “Significant” correlations can be misleading

- effect may be significant but too small to be meaningful

- with 100 random relations, one is significant at 1% level

• Batch operations - “hunting”, stop/start, irregular attention

• Process operation, with continuous objective function

- Allows smoother operation

31

Constraints versus Objective Functions

Target Target Target+ToleranceTarget-ToleranceUpper LimitLower Limit

Stress FunctionConstraint

Allowed Range Increasing Stress Increasing Stress

32

ISO Standards – Assay Error Estimation

• Mean Absolute Deviation versus Root Mean Square Deviation

- Variances add only if normally distributed

- Failure to use readily available computer power

• Removal of Outliers

- Without identifying cause and repeating data collection

- Gives grossly optimistic estimates of assay error

*

33

Conclusions –Types of Paradigm Problems

1) Alternative Paradigms in Small Worlds

- Lack of communication with colleagues

- Industrial secrecy

2) Problem Representation Inadequate

- Simulation too detailed

- Unaware of more powerful models *

34

Conclusions –Types of Paradigm Problems

3) Invalid or Inefficient Heuristic

- Assumption of independence

- Inappropriate treatment of error

4) Inappropriate Objective Function and/or Constraints

- Avoid discontinuities

- Process rather than batch

- Convert constraints to objective function components*

35

Thank You

ANY QUESTIONS?