MINING 7115 - Mine Automation

North Terrace Campus - Semester 2 - 2023

The aim of this course is to provide some basic training to students in the area of digital evolution in mining, including automated unit operations, data collection and data analytics, sensors and remote control, communications and Internet of Things (IoT), simulation and digital twins, system integration and system engineering.

  • General Course Information
    Course Details
    Course Code MINING 7115
    Course Mine Automation
    Coordinating Unit School of Civil, Environmental & Mining Eng
    Term Semester 2
    Level Postgraduate Coursework
    Location/s North Terrace Campus
    Units 3
    Contact Up to 4 hours per week
    Available for Study Abroad and Exchange Y
    Course Staff

    Course Coordinator: Associate Professor Chaoshui Xu

    Course Timetable

    The full timetable of all activities for this course can be accessed from Course Planner.

  • Learning Outcomes
    Course Learning Outcomes
    On successful completion of this course students will be able to:
    1. Understand the impacts of digital evolution in mining operations;
    2. Demonstrate principles and components of major automated unit operations in mining;
    3. Understand data collection and data management, principles of big data analytics and prediction modelling, machine learning, artificial intelligence, data visualisation
    4. Understand the fundamentals of communications in digital mining, Internet of Things (IoT) and basic cyber security
    5. Understand the fundamentals of mine simulation, mine optimisation, systems engineering and their applications
    6. Understand on-site and off-site digital system integration of mining operations and the concept of digital twins
    7. Construct simple mine models and apply mine simulations to solve practical digital mining problems

    University Graduate Attributes

    This course will provide students with an opportunity to develop the Graduate Attribute(s) specified below:

    University Graduate Attribute Course Learning Outcome(s)

    Attribute 1: Deep discipline knowledge and intellectual breadth

    Graduates have comprehensive knowledge and understanding of their subject area, the ability to engage with different traditions of thought, and the ability to apply their knowledge in practice including in multi-disciplinary or multi-professional contexts.


    Attribute 2: Creative and critical thinking, and problem solving

    Graduates are effective problems-solvers, able to apply critical, creative and evidence-based thinking to conceive innovative responses to future challenges.


    Attribute 3: Teamwork and communication skills

    Graduates convey ideas and information effectively to a range of audiences for a variety of purposes and contribute in a positive and collaborative manner to achieving common goals.


    Attribute 4: Professionalism and leadership readiness

    Graduates engage in professional behaviour and have the potential to be entrepreneurial and take leadership roles in their chosen occupations or careers and communities.


    Attribute 5: Intercultural and ethical competency

    Graduates are responsible and effective global citizens whose personal values and practices are consistent with their roles as responsible members of society.


    Attribute 6: Australian Aboriginal and Torres Strait Islander cultural competency

    Graduates have an understanding of, and respect for, Australian Aboriginal and Torres Strait Islander values, culture and knowledge.


    Attribute 7: Digital capabilities

    Graduates are well prepared for living, learning and working in a digital society.


    Attribute 8: Self-awareness and emotional intelligence

    Graduates are self-aware and reflective; they are flexible and resilient and have the capacity to accept and give constructive feedback; they act with integrity and take responsibility for their actions.

  • Learning Resources
    Required Resources
    PPT slides and course readers (available on MyUni).
    Recommended Resources
    Recommended additional readings (available on MyUni).
    Online Learning
    Lecture and software training recordings (available on MyUni).
  • Learning & Teaching Activities
    Learning & Teaching Modes
    Mixture of online and face-to-face teaching.

    The information below is provided as a guide to assist students in engaging appropriately with the course requirements.

    The information below is provided as a guide to assist students in engaging appropriately with the course requirements.

    Activity Contact Hours Independent Study Hours Total
    Lectures 32 0 32
    Tutorials 8 0 8
    Practicals 4 0 4
    Quizes and Tests 0 60 60
    Machine Learning and Digital Twin Project 6 40 46
    Total 50 100 150

    Learning Activities Summary

    No information currently available.

  • Assessment

    The University's policy on Assessment for Coursework Programs is based on the following four principles:

    1. Assessment must encourage and reinforce learning.
    2. Assessment must enable robust and fair judgements about student performance.
    3. Assessment practices must be fair and equitable to students and give them the opportunity to demonstrate what they have learned.
    4. Assessment must maintain academic standards.

    Assessment Summary

    Assessment Start Due Weighting Hurdle Rate
    Quiz 1 Week 4 Week 4 15% N/A
    Quiz 2 Week 7 Week 7 15% N/A
    Quiz 3 Week 11 Week 11 20% N/A
    Project Report Week 7 Week 12 40% N/A
    Project Presentation Week 12 Week 12 10% N/A
    Total 100%

    Assessment Detail
    Assessment description details are given as follows.

    Assessment Task Weighting Task Description
    Quiz 1 15% Robotics, mine automation, sensors, data collection
    Quiz 2 15% Data analytics, machine learning, digital twins
    Quiz 3 20% Simulations, optimisations and systems engineering
    Project Presentation 10% Machine leaning and digital twin project presentation
    Project Report 40% Machine learning and digital twin project report


    Assessment Task Topics Releasing Due
    Quiz 1 Robotics, mine automation, sensors, data collection Week 4 Week 4
    Quiz 2 Data analytics, machine learning, digital twins Week 7 Week 7
    Quiz 3 Simulations, optimisations and systems engineering Week 11 Week 11
    Project Presentation Machine leaning and digital twin project presentation Week 7 Week 12
    Project Report Machine learning and digital twin project report Week 7 Week 12
    Course Grading

    Grades for your performance in this course will be awarded in accordance with the following scheme:

    M10 (Coursework Mark Scheme)
    Grade Mark Description
    FNS   Fail No Submission
    F 1-49 Fail
    P 50-64 Pass
    C 65-74 Credit
    D 75-84 Distinction
    HD 85-100 High Distinction
    CN   Continuing
    NFE   No Formal Examination
    RP   Result Pending

    Further details of the grades/results can be obtained from Examinations.

    Grade Descriptors are available which provide a general guide to the standard of work that is expected at each grade level. More information at Assessment for Coursework Programs.

    Final results for this course will be made available through Access Adelaide.

  • Student Feedback

    The University places a high priority on approaches to learning and teaching that enhance the student experience. Feedback is sought from students in a variety of ways including on-going engagement with staff, the use of online discussion boards and the use of Student Experience of Learning and Teaching (SELT) surveys as well as GOS surveys and Program reviews.

    SELTs are an important source of information to inform individual teaching practice, decisions about teaching duties, and course and program curriculum design. They enable the University to assess how effectively its learning environments and teaching practices facilitate student engagement and learning outcomes. Under the current SELT Policy ( course SELTs are mandated and must be conducted at the conclusion of each term/semester/trimester for every course offering. Feedback on issues raised through course SELT surveys is made available to enrolled students through various resources (e.g. MyUni). In addition aggregated course SELT data is available.

  • Student Support
  • Policies & Guidelines
  • Fraud Awareness

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