CSMM222 - Decision-making for Engineers and Scientists (2023)

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MODULE TITLEDecision-making for Engineers and Scientists CREDIT VALUE15
MODULE CODECSMM222 MODULE CONVENERProf Hylke J Glass (Coordinator)
DURATION: TERM 1 2 3
DURATION: WEEKS 11
Number of Students Taking Module (anticipated) 50
DESCRIPTION - summary of the module content
Decision-making is an integral part of our daily life and profoundly influences our professional mastery. This module focusses on decision-making for engineers and scientists who are active in studies of mineral exploration, resource estimation, mining engineering, mineral processing, and tailings management. 
This module imparts valuable knowledge of essential techniques for understanding the design of sampling campaigns and information contained in data in order to make decisions. 
Technical decision-making skills are useful during individual research projects which follow the taught modules of the CSM MSc programmes and in later life.
As a prerequisite to engage fully with the module, you  are expected to have a basic appreciation of statistics and mathematics. Guidance on appropriate self-study to improve knowledge in these areas can be provided as required.
 
AIMS - intentions of the module
The module seeks to provide:
  • insight into the sampling of solid or particulate materials.
  • enhanced insight into decision-making about physicochemical properties or processing behaviour of a metal- or mineral-bearing materials.
  • understanding of the effect of risk, uncertainty, and bias on decision-making.
  • improving confidence in data through a process of reconciliation.
  • solutions for practical applications:
  • analysis of sample collection in the field and in a processing plant.
  • determination of a fit-for-purpose sample size. 
  • simulation of data variability.
  • design of experimental programmes in the laboratory.
  • interpretation of experimental results under consideration of uncertainty.
  • building models through regression.
  • identification of outliers and trends in datasets.
  • adjustment of data under consideration of process constraints.
 
INTENDED LEARNING OUTCOMES (ILOs) (see assessment section below for how ILOs will be assessed)
On successful completion of this module, the learner should be able to:
 
Module Specific Skills and Knowledge
1.  select suitable techniques for sample collection.
2. calculate sampling variance and required sample size.
3. distinguish between outlier and spatial or temporal trends in data.
4. analyse sample variability with bootstrapping and jackknifing.
5. create factorial experiments.
6. develop models with regression.
7. create factorial experiments.
8. determine parameter significance with Analysis of Variance.
9. apply classic parametric and non-parametric tests.
10. understand Bayesian updating.
11. perform data reconciliation.
 
Discipline Specific Skills and Knowledge
12. select appropriate methods for the analysis, modelling, and solution of practical engineering problems.
13. apply computer-based decision-making for applications across the mining value chain.
 
Personal and Key Transferable / Employment Skills and Knowledge
14. analyse and present data in a way that facilitates effective decision-making.
15. communicate effectively and persuasively using the full range of currently available methods.
SYLLABUS PLAN - summary of the structure and academic content of the module
Topics:
  • Field sampling techniques
  • Plant sampling techniques
  • Variability, uncertainty, and bias
  • Statistical distributions
  • Sampling variance
  • Sample size optimisation
  • Inference from sample analyses
  • Bootstrapping and jackknifing
  • Classic parametric and non-parametric hypothesis testing
  • Bayesian updating
  • Experimental design
  • Regression models
  • Outlier detection
  • Mass balance closure
LEARNING AND TEACHING
LEARNING ACTIVITIES AND TEACHING METHODS (given in hours of study time)
Scheduled Learning & Teaching Activities 30.00 Guided Independent Study 120.00 Placement / Study Abroad
DETAILS OF LEARNING ACTIVITIES AND TEACHING METHODS
Category Hours of study time Description
Scheduled learning & teaching activities 12 Lectures
Scheduled learning & teaching activities 18 Computer Tutorials
Guided independent study 120 Private Study

 

ASSESSMENT
FORMATIVE ASSESSMENT - for feedback and development purposes; does not count towards module grade
Form of Assessment Size of Assessment (e.g. duration/length) ILOs Assessed Feedback Method
Computer exercises Duration: 18 hours   Verbal, or electronic if required
       
       
       
       

 

SUMMATIVE ASSESSMENT (% of credit)
Coursework 100 Written Exams 0 Practical Exams
DETAILS OF SUMMATIVE ASSESSMENT
Form of Assessment % of Credit Size of Assessment (e.g. duration/length) ILOs Assessed Feedback Method
Decision-making coursework 75% Length 3000 word equivalent 1-15 Electronic or written feedback
In-class test 25% Duration 1 hour 1-11 Electronic or written feedback
         
         
         

 

DETAILS OF RE-ASSESSMENT (where required by referral or deferral)
Original Form of Assessment Form of Re-assessment ILOs Re-assessed Time Scale for Re-assessment
Decision-making coursework Decision-making coursework 1-15 Aug Ref/Def period
In-class test In-class test 1-11 Aug Ref/Def period
       

 

RE-ASSESSMENT NOTES

If a student is referred or deferred, the failed / non-completed component(s) will be re-assessed at the same weighting as the original assessment.

RESOURCES
INDICATIVE LEARNING RESOURCES - The following list is offered as an indication of the type & level of
information that you are expected to consult. Further guidance will be provided by the Module Convener
 
Web based and Electronic Resources:

Reading list for this module:

Type Author Title Edition Publisher Year ISBN Search
Set Davis, John C. Statistics and Data Analysis in Geology 3rd 2002 [Library]
Set Hair, Joseph F. Multivariate Data Analysis 1998 [Library]
Set Kachigan, Sam Statistical Analysis: An interdisciplinary introduction to univariate and multivariate methods 1986 [Library]
Set Little, Roderick J. A. and Donald B. Rubin Statistical Analysis with Missing Data 3rd Wiley 2020 [Library]
CREDIT VALUE 15 ECTS VALUE 7.5
PRE-REQUISITE MODULES None
CO-REQUISITE MODULES None
NQF LEVEL (FHEQ) 7 AVAILABLE AS DISTANCE LEARNING No
ORIGIN DATE Monday 24 January 2022 LAST REVISION DATE Thursday 02 February 2023
KEY WORDS SEARCH Sampling theory; sampling statistics; data analysis; statistical distributions; bias; uncertainty; error; sampling variance theory; sample size optimisation; sampling techniques; data reconciliation; regression analysis; ANOVA; principle components...